WO2015142160A1

WO2015142160A1 - Organized knowledge and service system (okss)

Info

Publication number: WO2015142160A1
Application number: PCT/MY2015/000019
Authority: WO
Inventors: Syed Osman AIHaddad SYED MOHAMED
Original assignee: Wafina Sdn. Bhd.
Priority date: 2014-03-19
Filing date: 2015-03-19
Publication date: 2015-09-24
Also published as: MY191940A

Abstract

The present invention is a system for providing a user with continuous, updated and structured knowledge relevant to the user and/or a system for facilitating a service required by the user (100, 101, 102]. More specifically, the invention relates to a system that finds out a requirement of knowledge from a user (100, 101, 102], collect the knowledge from right sources, process the knowledge and present the knowledge in the most suitable form to the user (100, 101, 102]. It also includes a system to find out a requirement of a particular service from a user (100, 101, 102], identify a person or a machine or animal having the capability to do the service and present the information regarding the person or the machine or the animal with the required capability to those who are in requirement.

Description

ORGANIZED KNOWLEDGE AND SERVICE SYSTEM (OKSS)

FIELD OF INVENTION

The invention relates to an organized knowledge and service system (OKSS).

BACKGROUND OF INVENTION Everybody wants some kind of knowledge in their life. The moment we are born in to this world we start learning some knowledge. Knowledge has many definitions. Knowledge can be defined as acquaintance or familiarity gained by sight, experience, or report. Knowledge is the body of truths or facts accumulated in the course of time. It can also be defined as acquaintance with facts, truths, or principles, as from study or investigation. Some researchers say that we learn even before we are born to this world. Without learning we cease to exist. The very existence of living beings in this world is related to learning from our environment on how to deal with our environment. Therefore all humans require some sort of knowledge and all humans need to update knowledge continuously for successful existence in this world.

Education traditionally meant the acquisition of the knowledge people needed for their working lives. In today's fast developing technological world, formal education in schools or colleges can only provide only limited amount of knowledge required for daily life. This is because technology is changing the way we are interacting with the world so that yesterday's knowledge can be obsolete today. One example is internet of things (IoT). There will be billions of connected devices around us so that we need to update our knowledge continuously to learn how to deal with these devices. Many of these devices will be intelligent and can deal with people more or less similar to the way in which humans deal with humans. Animals also will be carrying electronic devices. These devices will be communicating with the animal in one way or other and these devices will also be communicating with other systems or other humans or other animals in one way or another. There is a need for a new knowledge management tool that allows humans, animals and intelligent machines to access updated knowledge as they need it. The amount of knowledge that people can access is also increasing drastically nowadays. Vast amount of knowledge cannot be stored in people's mind. Even though teacher-student interaction was basis of learning in human history, we required some sort of knowledge storage and knowledge management techniques. Traditionally handwritten and printed books facilitated knowledge storage and libraries and similar institutions took care of knowledge management. With the advancement of digital technologies and computer systems, machine readable memories took the place of knowledge storage, and computer systems and computer networks processed and managed this vast amount of knowledge.

With the introduction and popularization of internet, knowledge storage and management have witnessed explosive transformation. Anyone, anywhere at any moment got facilities to access a huge amount of knowledge. One example for knowledge management in digital age is internet search engines. Internet search engines and similar facilities empowered people with abilities to search for any kind of information that are available to mankind. Apart from the conventional process of searching hundreds of books, taking a large amount of time, information or knowledge can be made available at our location without travelling or taking physical stress.

But the present knowledge searching technologies have also brought several disadvantages and difficulties. Internet is like an ocean of information. Search engines are said to narrow down knowledge search areas by providing selected websites that may be relevant to us. But really search engines give us a sea of information from the ocean of information.

For example, the search for a term "rose" gave 42,900,000 results in search engine 1 and 1,600,000,000 results in search engine 2. Then how can we say that search engines narrow down search for knowledge. They narrow down billions of web pages to millions. Where is the real advantage in knowledge search? The information most relevant to us may be in the 100,000^th webpage. Will anybody in this world go up to this? Many times the search engines make real knowledge search difficult.

What happens if two or more people with two different requirements have searched the same keyword? In all the cases the search engine give the same result, may be millions of web pages. For example one wanted to know about different kinds of rose flowers and their prices and availability in his locality for setting a garden and another one, who is a botany student wanted to know scientific details of different kinds of roses. Both of them get the same result regardless of their requirements. Here both the of the above mentioned persons have to navigate into the provided search results and have to do much research to find out the desired results. The search can be made narrower by using additional keywords and by using operators. But everybody who use search engines may not be knowledgeable in technical issues like operators or they will be lacking more information regarding the subject or they may be young and less educated for using an efficient search keyword. In the above cases search engine could not identify the real requirements of the one who is conducting a search. There are many publications that propose search engine technologies that are different from conventional search engines. For example, US published patent application number US20070005566 mentions a knowledge correlation search engine. This search engine creates correlations linking terms from inputs provided by a user to selected target terms. The correlation search process receives pre- processed inputs from a user including a wide variety of input formats including keywords, phrases, sentences, concepts, compound queries, complex queries and orthogonal queries. But all these technologies cannot provide accurate and relevant knowledge to all the users.

Search engines are not the only knowledge management and dissemination platform in the digital world. There are lot of online learning platform in the internet which are set up by different people. They teach the participants regarding various subjects, but they are simply like schools. You can only learn what they teach you. Torrents are another kind of technology which facilitates knowledge sharing. But torrents are also very weak in providing required knowledge to the users. We can see that the present knowledge management technologies available in the digital world are not suitable to provide all humans with required kind of knowledge. Knowledge requirement from each person can be unique. It not only depends on his age or qualification or profession. But it may also depend on the complete profile of the person in terms of his history, family background, immediate environment, the people whom which he interacts, the intelligent machines whom which he interacts, his real world interactions with animals, plants etc. As mentioned earlier, this kind of requirement based knowledge cannot be obtained from schools or colleges or any knowledge management tools.

Another issue with existing knowledge management systems is that, they can only search for knowledge in the internet or any computer networks. We can only get knowledge that is available in computer readable formats. They cannot extract or generate knowledge from real world situations.

One of the complicated issues in knowledge management domain is that, with the advancement in technology humans are not the only consumers of knowledge. One example is regarding intelligent machines. With the advancement in technologies, machines are becoming more and more intelligent. They are being deployed in many areas that were conventionally dealt by living beings. These areas not only include areas which require power and precision, but also on those areas which require intelligence.

For example, manufacturing sector was handled by skilled workforce conventionally. Due to the requirement of increased throughput, machines started to assist humans in manufacturing sector. The role of machines became more and more prominent with advancement in technology. With the introduction of computer systems, machines not only became powerful, but also they became intelligent. The precision of the machines also increased due to the addition of intelligence.

Due to this, machines started to be used in areas other than manufacturing sectors, like mining, construction, safety, homeland security, emergency rescue etc. Gradually with the advancement in computer technologies, intelligence of machines increased and this gave rise to autonomous machines which can do many tasks without any human intervention. Intelligent machines even can handle very sophisticated and precise jobs that are conventionally done by humans only. One of the recent areas in which machines are used solely or with human intervention is health care. One example is a surgical robot. These are autonomous robotic machines that can do medical surgeries with high precision and minimal invasion.

Many of these intelligent machines can learn from their environment and adapt to new situations or improve their performances. The artificial intelligence systems for self-learning implemented in these intelligent machines are able to acquire knowledge in one way or other. In an example case, a robotic surgery machine can learn from another robotic surgery machine to make its job perfect and efficient, if provisions for self-learning are added to the robotic surgery machine by its developers. This learning provision is compulsory in these robotic surgery machines because each surgery can be different experience, and a high precision intelligent machine will be a better option than a human doctor with fatigue or negligence. Yet another kind of consumers of knowledge is animals. With advancement in technologies like Internet of Things (loT) animals like pet animals are connected to the internet in one way or other. Animals will be carrying many electronic devices and many of these devices will be intelligent also.

One example is electronic tagging. There have been major advances in both the capability and reliability of electronic tags and analytical approaches for geolocation of tagged animals in marine, areal and land habitats. Advances such as increased data storage capacity, sensor development, and tag miniaturization have allowed researchers to track a much wider array of animals, not just large and charismatic species. Importantly, data returned by these tags are now being used in population analyses and movement simulations that can be directly utilized in stock assessments and other management applications. Electronic devices can also communicate with the animal in some ways and providing them instructions and train them to do something. One example is a geo-fencing system for animals that will give a non-fatal electric shock to the animal carrying the geo-fencing system when the animal tries to cross a boundary decided by its owner. After several attempts by the animal to cross the border and resulting electric shock, the animals will learn not to cross the boundary. The electronic systems connected to animals can provide their instructions for exercise, feeding etc.

With advancement in technology machines can communicate with human and animal brains. Brainwaves can be detected using electrodes such as Electroencephalogram (EEG) electrodes, which make machines read our thoughts. Controlling a robotic arm by thoughts or controlling a vehicle by thoughts is possible nowadays. There are many cases in animal control is implemented with directly communicating with brain of the animal through external electronic systems. In one case researchers implanted electrodes in rat brains and controlled the rat movement with external electronic systems. In another case, researchers have electronically linked the brains of pairs of rats, enabling the animals to communicate directly via implanted microelectrode arrays to solve simple behavioural problems.

We can see that with advancement in technologies, the boundaries between humans, intelligent machines and animals are disappearing. We can see that human thoughts are extended by machines, but sometimes human thoughts are replaced by machines. This gives rise to a complex world of knowledge management, where humans are not the only knowledge seekers, but intelligent machines and animals. This is where the existing knowledge management and knowledge rendering systems fail. The existing knowledge management technologies can only provide knowledge to humans. They cannot process knowledge in order to make the knowledge available to intelligent machines and animals. The existing technologies cannot deal with knowledge requirements from an intelligent machine or an animal.

There are also many cases in which knowledge sharing is required between humans, intelligent machines and animals. Knowledge from a surgeon can be utilized by an intelligent robot that is designed to do surgeries, or vice versa. Knowledge from animals inhabiting in the forest can be utilized by humans who need to live or travel in forests. Knowledge from intelligent traffic machines can be utilized by humans who cross roads or drive on roads. There are issues of kangaroos crossing roads and causing road accidents. This issue also reduces number of kangaroos and is an issue of concern in Australia. There are issues of camels crossing roads and causing road accidents in Middle East. In all these cases electronic systems can be connected to animals or implanted inside the animals so that they can be provided with knowledge and trained to avoid above mentioned fatalities.

Existing systems cannot provide a platform for knowledge sharing between humans, intelligent machines and animals.

In order to provide accurate knowledge for all those who require knowledge, accurate profiling is required. Accurate profiling is required to extract exact knowledge requirement from not only humans but also intelligent machines and animals. Existing computer based profiling techniques profiles a user based on his interaction with the computer network only. This is done for marketing, career search etc.

Companies operating on the internet or other computer networks have access to vast information collected from people's online activities. Companies can record every activity that a visitor makes on their website. Many companies and web browsers use cookies that monitor one's online activity. Cookies give lot of information like websites visited by a person, the search keywords provided by a user etc.

Existing profiling techniques can profile humans only and the users or beneficiaries of existing profiling techniques are only humans. They cannot identify a requirement of knowledge from a heterogeneous community of knowledge seekers consisting of humans, intelligent machines and animals.

Present technologies treat world community as a homogenous group and offering the same knowledge mix to everybody. The drawback of this is that needs and preferences of different knowledge consumers differ and the same knowledge offering is unlikely to be viewed as optimal by everyone. Present knowledge management technologies could not recognize the diversity of knowledge consumers and so try to please all of them with the same knowledge offerings.

Along with knowledge, as explained before, everybody needs some service at most times. Service requirement can be broadly classified into regular service requirement and immediate service requirement. A service requirement that is to be facilitated regularly or routinely is called a regular service requirement. A service requirement that arises immediately due to any reason is called an immediate service requirement. For example, tuition in a school is a regular service requirement for a student. A doubt clearing service requirement from a student while he is studying at his house is an instantaneous service requirement. Somebody who needs service to open his mobile phone case while he is travelling may be having an instantaneous service requirement.

As in the case of knowledge requirements, service requirements can come from intelligent machines and animals also. Consider an intelligent machine working in a coal mine. While the intelligent machine doing its routine job, it may require some service like an emergency rescue. An animal that is doing an emergency rescue operation may require a service from other humans or intelligent machines or animals during its work. There may be many humans or intelligent machines or animals connected to electronic systems around a human or an intelligent machine or an animal connected to electronic system, which can provide a required service to the human or animal or intelligent machine. For example, for the one who needs a service to open his mobile phone case while he is travelling in a bus there may be other people in the bus who can provide the service. But since there is no platform nowadays to share this kind of service requirements to the required ones, he may have to go to a repairing shop to open his mobile phone.

It is noted that existing knowledge management tools and knowledge dissemination tools are not sufficient for satisfying the growing demands of the present world. It cannot provide highly personalized knowledge that is tailored for each one who requires the knowledge. Existing technologies provide same kind of knowledge to everybody. This is because presently knowledge is arranged under various topics and one needs to go through various knowledge topics to get relevant knowledge. Another problem with existing technologies is that they can only deal with available knowledge. The existing technologies cannot generate knowledge from real world. Existing knowledge management technologies consider humans only as knowledge consumers. They cannot provide a facility for knowledge sharing between humans, animals and intelligent machines. Also existing technologies cannot share capability information between humans, animals and intelligent machines for facilitating some service. Existing technologies fail to understand a world where humans, intelligent machines and animals co-exist and interact for knowledge and services.

SUMMARY OF THE INVENTION

Accordingly, the present invention provides an organized knowledge and service system (OKSS) for providing structured and updated knowledge to a user who is interacting with the OKSS in one way or another by organizing knowledge based on knowledge requirement of the user, wherein the OKSS includes [a] networked computer systems comprising of (i) a means to collect information and metrics from a primary source in the form of computer-readable data (ii) a computer readable database for storing the computer-readable data (iii) a processor and a self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for manipulating the computer-readable data and (iv) at least one hardware and software communication interface for communicating the computer- readable data between systems, subsystems and the user; (b) at least one self-learning, adaptive, and intelligent computer system comprising of: (i) a means of accepting the computer- readable data in various formats, (ii) a means of generating a profile of the user from the computer-readable data (iii) a means of extracting at least one knowledge requirement of the user from the profile of the user, (iv) a means to find at least one knowledge source, (v) the self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for extracting the knowledge from at least one knowledge source and further processing the extracted knowledge and generating an organized knowledge that meet the knowledge requirement of the user, (vi) a means to store the organized knowledge in the form of the computer readable data, (vii) a means to generate a knowledge presentation policy based on the profile of the user and (viii) a means of presenting the organized knowledge generated to the user by executing the knowledge presentation policy and (c) a system for feedback and evaluation of success of the knowledge provided to the user, comprising of: (i) a means for collecting information and metrics from the primary source of knowledge in the form of computer readable data, wherein the collected information and metrics provide details regarding feedback and evaluation of success of the knowledge provided to the user, (ii) a means to store the information and metrics so collected in a computer readable memory for further processing, (iii) a means to use a self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for processing the information and metrics, and (iv) a means to update the organized knowledge or modify the knowledge presentation policy.

BRIEF DESCRIPTIONS OF THE DRAWINGS

Fig. 1 is the overall system architecture of the OKSS;

Fig. 2 is the system architecture for generating information bases for various classes of users namely ULl, UL2 and UL3; Fig. 3 is the flowchart for generating the information bases and automatically generated profile pages; Fig. 4 is the system architecture for automatically generated profile pages; Fig. 5 is the system architecture for generating knowledge segment table (KST]; Fig. 6 is the system architecture for generation of segmented knowledge base; Fig. 7 is the system architecture of segmented knowledge base; and Fig. 8 is the system architecture for knowledge presentation and feedback.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Several of the key concepts underlying the present invention's approach to addressing the identified problems are detailed below.

Generally, the present invention provides an organized knowledge and service system (OKSS) for providing structured and updated knowledge to a user who is interacting with the OKSS in one way or another by organizing knowledge based on knowledge requirement of the user, wherein the OKSS includes (a] networked computer systems comprising of (i) a means to collect information and metrics from a primary source in the form of computer-readable data (ii) a computer readable database for storing the computer-readable data (iii) a processor and a self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for manipulating the computer-readable data and (iv) at least one hardware and software communication interface for communicating the computer- readable data between systems, subsystems and the user; (b) at least one self-learning, adaptive, and intelligent computer system comprising of: (i) a means of accepting the computer- readable data in various formats, [ii] a means of generating a profile of the user from the computer-readable data (iii) a means of extracting at least one knowledge requirement of the user from the profile of the user, (iv) a means to find at least one knowledge source, (v) the self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for extracting the knowledge from at least one knowledge source and further processing the extracted knowledge and generating an organized knowledge that meet the knowledge requirement of the user, (vi) a means to store the organized knowledge in the form of the computer readable data, (vii) a means to generate a knowledge presentation policy based on the profile of the user and (viii) a means of presenting the organized knowledge generated to the user by executing the knowledge presentation policy and (c) a system for feedback and evaluation of success of the knowledge provided to the user, comprising of: (i) a means for collecting information and metrics from the primary source of knowledge in the form of computer readable data, wherein the collected information and metrics provide details regarding feedback and evaluation of success of the knowledge provided to the user, (ii) a means to store the information and metrics so collected in a computer readable memory for further processing, (iii) a means to use a self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for processing the information and metrics, and (iv) a means to update the organized knowledge or modify the knowledge presentation policy. The user may be a human or an intelligent machine or an animal connected to an electronic system.

The primary source may be any one of the sources from where information and metrics are collected in order to profile a user, such as internet, internet of things, social media platforms in internet or other computer networks, collaboration platforms in internet or other computer networks, any computer network or mobile network with which the user interacts, brain-machine interface connected to the user if the user is a human or an animal, knowledge updating or entertainment activities of the user including reading, interaction with television and radio broadcasts if the user is human, real world activities (106), intelligent profiling systems etc. The knowledge source may be any one of the sources of information, metrics, data, knowledge materials, statistics, facts, figures, diagrams, pictures, audio, video, evidence etc. available in computer readable format, such as internet, internet of things, social media platforms in internet or other computer networks, collaboration platforms in internet or other computer networks, any computer network or mobile network with which the user interacts, radio or television broadcasts, any brain machine interface, real world activities, intelligent knowledge generating devices and applications etc.

The real world activities may include activities such as daily activities of humans, machines, animals and other worldly beings from where knowledge can be generated and/or information and metrics can be extracted to profile a user. Intelligent devices and software applications may be provided to generate knowledge from real world activities and to collect information and metrics from real world activities to profile a user.

Feedback and evaluation of success of the knowledge provided to the user may be measured in terms of user satisfaction or improvement in capability of the user or achieving goals intended by the user by learning the knowledge. The organized knowledge may be updated or the knowledge presentation policy is modified if the user is not satisfied with the provided knowledge or the provided knowledge is not sufficient to meet the user's requirement.

The updated organized knowledge or modified knowledge presentation policy may be provided to the user until the user is satisfied with the provided knowledge. The OKSS may be further include a means to identify requirement of at least one service from the user.

The OKSS may further include a means to identify capability of the user to provide at least one service.

The OKSS may further include a means to credit the user in terms of knowledge or capability to render at least one service.

When the user is a human, the human may interact with the OKSS through the primary source. When the user is an intelligent machine, the intelligent machine may interact with the OKSS through the primary source. The intelligent machine has provisions to make use of the knowledge provided by the OKSS.

When the user is an animal connected to an electronic system, the electronic system interacts with the animal through a means such as audio, video, electric shock, visual indications, brain-machine interface etc.

When the user is an animal connected to an electronic system, the electronic system interacts with the OKSS through the primary source. The means of generating a profile of the user from the primary source may use the profiling engine which includes (a] a dynamic data collector (DDC) which continuously collects various information and metrics from the primary source in the form of computer readable data, (b] a user classifier to identify a user as a human or an intelligent machine or an animal connected to electronic system and to classify each identified user into various classes based on his/its capabilities, (c) a means to provide every user with a unique identification (UID) code, (d) a means to store UID code in a computer readable memory, (e) a means to generate various information bases for each user and a means to generate automatically generated profile pages [AGP] for each user.

The means to generate various information bases for each user may include [a] an information base generator to generate user information base (UIB) for each user to store primary profile, (b) the information base generator to generate cross-profile information base (CIB) for each user to store secondary profile, (c) the information base generator to generate preliminary information base (PIB) of a non-user to store tertiary profile and (d) a profile processor (PP) to generate accurate profile base (APB) of a user by processing contents of CIB and UIB together. The primary profile of a user may be the profile generated by collecting information and metrics directly from the user's interaction with the primary source.

The secondary profile of a user may be the profile generated by collecting information and metrics from interaction of other users who are directly or indirectly related to the user.

The tertiary profile of a non-user may be the profile generated by collecting information and metrics from any user who is directly or indirectly related to the non-user.

The user classifier may use knowledge kinetics system to identify a user as a human or an intelligent machine or an animal connected to electronic system.

Primary AGP (AGP1) and secondary AGP (AGP2) may be generated for each user from his/its APB. AGP1 and AGP2 are the front end interfaces of the profile of a user, where any human or intelligent machine or animal connected to electronic system can view the profile of a user physically or through electronic means or a combination of both.

AGP1 may be viewed by any user but cannot be edited by anyone including the user himself/itself.

A user may edit the entries in his/its AGP2, thus enables a means to update some of his profile information manually. Any user may edit the profile suggestion section of a user's AGP2.

A human user may disable editing of profile suggestion section in his AGP2 by other users. When the user is an intelligent machine or an animal connected to an electronic system, the AGP2 of the user may be edited by authorized human users. The authorized users may be automatically detected by the OKSS from the profile of the intelligent machine or the animal connected to electronic system.

AGP1 and AGP2 can be accessed by other systems which need the profile information of the user. The APB of a user may be updated when AGP2 of the user is edited.

The knowledge requirement of a user may be extracted from APB of the user by knowledge requirement extractor (KRE).

The capability of a user to provide knowledge or the capability of the user to provide at least one service may be extracted from APB of the user by capability extractor (CE). The requirement of at least one service from a user may be extracted from APB of the user by service requirement extractor (SRE).

The knowledge requirement of a user may be stored in his/its knowledge requirement table (KRT).

The capability of a user to provide knowledge or the capability of the user to provide at least one service may be stored in his/its capability table (CT). The capabilities of all users may be stored in service capability base (SCB).

The requirement of at least one service from a user may be stored in his/its service requirement table (SRT).

An automatic service information provider (ASIP) may process information from SRT of a user with information from SCB to present information to the user about other users who can provide the required service to the user.

If a required service for a user cannot be found from CT of any user, the required service may be marked as special knowledge requirement and added to the knowledge segment table (KST) to generate a knowledge segment, where the learning of the knowledge segment by a relevant user will give the user a capability to provide the required service. The entries of KRT of a user may be viewed by any user and can be edited only by the user through the AGP2 of the user.

The entries of SRT of a user may be viewed by any user and can be edited only by the user through the AGP2 of the user.

The edited KRT entries in the AGP2 may be written back to the KRT, which enable the user to edit his/its knowledge requirements. The edited SRT entries in the AGP2 may be written back to the SRT, which enable the user to edit his/its service requirements.

A bridge interface between AGP1 and AGP2 of humans, intelligent machines and animals connected to electronic systems may facilitate sharing of information between humans, intelligent machines and animals connected to electronic systems.

Knowledge may be organized into requirement based knowledge segments (RKS), where each RKS corresponds to a requirement from a user or identical requirements from different users.

Each RKS may be further organized into knowledge pools of different complexity levels suitable for different users with different learning capabilities. A similarity correlator may read entries from KRT of all users, identifies identical knowledge requirements if any, gives a title for identical knowledge requirements, gives a unique knowledge segment identifier (KSID) for the title and stores the KSID in a knowledge segment table (KST).

A RKS engine may read each knowledge segment table entry, may identify the required knowledge content from the knowledge source and/or may generate the required knowledge content from real world activities, process the knowledge content and generates the RKS and add it to the a knowledge base.

Each RKS may be further processed by a knowledge pooling engine to generate at least one knowledge pool, where each knowledge pool has a different complexity level which is suitable for a user with a particular learning capability. A knowledge pool may be presented to a user through a presentation medium.

The presentation medium may be any one of the medium through which knowledge is disseminated to a user such as audio, video, visual indicators, text, social media messages, SMS, MMS, machine to machine interfaces, brain-machine interfaces etc.

The knowledge pool may be presented to the user using a knowledge presentation policy.

The knowledge presentation policy is a sequence of processes used in making the user learn the provided knowledge pool, like allocating computing and network resources used to present the knowledge pool to the user, selecting the natural language of presentation, selecting the mode of presentation such as audio, video, text, visual indications, SMS, MMS, social media messages, machine to machine messages, machine to animal messages or brain-machine interface or any combination thereof, selecting right knowledge pool, selecting the time of presentation of the knowledge pool and all other processes required to disseminate the required knowledge pool to the user in the most appropriate way. The information and metrics collected in the form of computer readable data in various format may include personal details of the user such as name, age, gender, family details, details of friends and relatives, location, educational qualifications, subjects of interest, skills, professional qualifications, profession or job, income, if the user is a human.

The information and metrics collected in the form of computer readable data in various formats may include machine centric information such as make, year of manufacture, country of manufacture, details regarding humans and machines used to design the intelligent machine, details regarding humans and machines used to manufacture the intelligent machine, processing power, throughput, operating systems, memory capacity, technologies used, operating environment, functional specifications, mechanical capabilities, field of operation, artificial intelligence level, learning capabilities, capabilities related to movement, safety and security standards, environmental standards etc., if the user is an intelligent machine. The information and metrics collected in the form of computer readable data in various formats may include information such as class of the animal such as mammal, reptiles, etc., animal type such as cow, goat etc., age of the animal, gender, any given name, any given code name, intelligence level, location of living, foods eaten, skills such as bomb detection, emergency rescue etc., any assigned work such as ploughing, housekeeping etc., location of work, topics of interest etc., if the user is an animal connected with an electronic system.

A feedback engine may be used to process feedback from a user regarding the presented knowledge pool or presented information regarding a service and to further communicate the processed feedback information to to update the knowledge pool or modify the knowledge presentation policy or update the information regarding a service,

A non-user may be presented with a knowledge pool or with information regarding a service through a user, if the non-user is directly or indirectly related to the user. The feedback from a non-user may be accessed through a user, provided the user is related to the non-user.

The OKSS may prioritize requirements of the user based on real interest and hobby of the user, wherein the real interest will have a higher priority compared to the hobby.

A user may manually request for knowledge from the OKSS.

The OKSS may provide knowledge hunting engine to manually search for knowledge. The preferred embodiments of the present invention will now be described in relation to the accompanying drawings.

More particularly, the OKSS is an organized knowledge generation, knowledge sourcing and knowledge sharing system. A user who requires knowledge makes use of the huge amount of available knowledge through this platform. The user can also make use of the instantaneous knowledge generation capacity facilitated by the OKSS. OKSS is also used to find out a requirement of at least one service from a user and to facilitate the required service to the user.

A user may be a human [100) or an intelligent machine (101) or animal (102) connected to an electronic system, who is interacting with the OKSS in one way or other. The overall architecture of OKSS is shown in Fig. 1. The OKSS core (104) contains most of the main components of the OKSS. The OKSS core (104) collects information and metrics regarding users and non- users from the primary source (103). The OKSS core (104) further interacts with knowledge source (105) and real world activities to generate a segmented knowledge base (107) and service capability base (110). The OKSS system core (104) interacts with the real world activities (106) through intelligent devices and software applications (IDSA) (111). The required knowledge or information on how to get a service is provided to the user through the presentation unit (108). Feedback from the user is communicated with the OKSS core (104) by the feedback engine (109).

OKSS implements intelligent profiling of a user, by collecting information and metrics from a primary source (103), to identify a requirement of knowledge from the user or to identify a requirement of a service from the user or identify the capability of a user to provide knowledge or to find out the capability of a user to provide a service. The profile, which is created by the intelligent profiling, is called a primary profile. The intelligent profiling is also used to find out a user who can provide knowledge or provide a service to the user/users who require knowledge or a service.

The intelligent profiling of the user is also used to make profile of another user who is related to the user. The process of making profile of a user from information and metrics collected from another human or intelligent machine or animal connected to electronic system, is hereafter called cross profiling in this document. The said profile, which is created by cross profiling, is called secondary profile.

Cross profiling is also used to make profile of a non-user. A non-user is defined as a human (100) or an intelligent machine (101) or an animal (102) connected to an electronic system who is related to a user, but are not interacting with the OKSS. The said profile of the non-user is called a tertiary profile.

Primary profile, secondary profile and tertiary profile are continuously updated in real time so that a user is always provided with most relevant knowledge and/or the user is always facilitated with a most required service.

The primary profile of the user or secondary profile of the user is used to find out at least one knowledge requirement from the user. OKSS then procures the required knowledge from a knowledge source (105) and/or generate the required knowledge from real world activities (106), process the knowledge and present the knowledge to the user in the most relevant form. Thus OKSS provides highly updated, continuous and structured knowledge to a user. Providing the knowledge in the most relevant form is facilitated by the knowledge presentation policy implemented by the presentation unit (108) of the OKSS. The primary profile or secondary profile of the user also provides information regarding the capability of the user to provide at least one service. The tertiary profile provides information regarding the capability of a non-user to provide at least one service. This information regarding the capability of the user or the capability of the non-user to provide a service is communicated with those user/users that require that service. That user who requires the service then can communicate with the user or non-user possessing the capability to provide the service for enabling the service. In this way the OKSS provides knowledge as well as service facilitation to a user or several users.

OKSS uses a feedback engine (109) which verifies whether the user is satisfied with the provided knowledge. If the user is not satisfied with the provided knowledge, the invention updates the provided knowledge or modifies the knowledge presentation policy to make the user satisfy with the provided knowledge. The above process is continuously done to provide the user with the required knowledge always.

The feedback engine (109) also verifies whether the user is satisfied with the facilitation of the required service. If the user is not satisfied with the facilitation of the service, OKSS provides the user with updated information regarding the service to make the user satisfy with the facilitation of the required service. The above process is continuously done to facilitate the user with required services always.

A user needs to interface with the OKSS through the primary source (103) and/or presentation medium (804) in order to get relevant knowledge or facilitation of a service from the OKSS. A non-user can get relevant knowledge or facilitation of a service from the OKSS through a user only.

Primary source (103), knowledge source (105) and presentation medium (804) are explained later in the relevant sections of this patent specification.

As mentioned earlier a user can be a human (100) or an intelligent machine (101) or an animal (102) which is connected to an electronic system, where the animal (102) can interface with the primary source (103) and/or presentation medium through an electronic system connected directly or indirectly to the animal body. This is a unique nature of the invention, where the OKSS provides knowledge not only for humans, but also for intelligent machines (101) and animals (102).

OKSS also facilitates knowledge sharing between humans, intelligent machines (101) and animals (102) connected to electronic systems. OKSS shares capability information among the users so that a user can access service/services from other user/users.

Ever since intelligent machines (101) are created, the need for knowledge rendering becomes a compulsory thing, especially when the machines are used autonomously. Intelligent machines (101) can learn from outside and adapt to the situation, and for learning, knowledge is needed to be provided in the most suitable form. For example, an intelligent and autonomous machine working in a coal mine will be programmed to do its routine task. But it also has provisions to add more knowledge so that the performance can be improved and it can be adapted to new situations. With OKSS, knowledge can be provided to intelligent machines (101) in many ways. Knowledge can be shared between intelligent machines (101) or between intelligent machines (101) and humans (100) and/or animals (102) connected to electronic systems in a highly distributed way so that an intelligent machine (101) working in a coal mine can make use of the knowledge provided by humans (100) working in another coal mine or the intelligent machine (101) can get knowledge from other similar or dissimilar intelligent machines (101) that may be working in another coal mine.

The intelligent machines (101) can also learn from other humans (100) or other intelligent machines (101) that may be working in other domains. For example, an intelligent machine (101) working in a coal mine can get knowledge from or provide knowledge to another intelligent machine (101) working in an emergency rescue situation. The intelligent machine (101) working in a coal mine may be autonomous to learn new activities, but may not be pre-programmed to do any emergency rescue action. It may not have got a chance to learn emergency rescue actions from any practical situations. OKSS can find out a requirement of knowledge from the intelligent machine (101) regarding emergency rescue actions and provide the knowledge in the most appropriate way. The provided knowledge may be sourced from a human (100) or an intelligent machine (101) or an animal (102) that are trained to do emergency rescue actions and are working in emergency rescue situations.

OKSS can also find out a requirement of service from the intelligent machine (101) regarding the emergency rescue situation and provide information regarding another human (100) or intelligent machine (101) or an animal (102) connected to electronic system that can provide the required service. For example if the required service is firefighting, OKSS facilitates the service of a human (100) or an intelligent machine (101) that are capable of fire fighting, to the intelligent machine (101).

Using OKSS humans can get knowledge or service from intelligent machines (101) and animals (102) connected to electronic systems, intelligent machines (101) can get knowledge or service from humans or animals (102) connected to electronic systems, and animals (102) connected to electronic systems can get knowledge or service from humans or intelligent machines (101). Animals co-exist with humans in many ways. They may be with us as domestic pet animals. They may be used for protection of life and properties. They may be grown in farms or poultries for agricultural or for food supply. They are used in emergency situations as war or other causalities like natural disasters.

Animals also require knowledge or some service. They are usually trained by their fellow animals in their conventional domain. For example, a cat is taught by its mother on how to jump, how to climb trees, how to catch its prey etc. Sometimes they are trained by humans or fellow senior cats on special tasks. Bomb detecting sniffer dogs are trained by humans on how to identify a bomb and how to inform people regarding any detected emergencies.

Human and animal brains can be interfaced with machines using electrodes such as EEG electrodes. These systems can read information from the brain and give information to the brain, thus facilitating direct communication with brain and external electronic control. The OKSS also makes use of the brain- machine interface to profile a user and to provide relevant knowledge to the user.

Another unique nature of the invention is generation of knowledge from real world activities (106). Real world activities (106) are those activities of humans, machines, animals and other worldly beings from where information and metrics can be extracted by interfacing with electronic systems, directly or indirectly. For example, an intelligent camera based device provided by the OKSS can monitor the growth of a plant and extract some kind of knowledge regarding growth of plants. This process is explained more in sections explaining generation of segmented knowledge base (107).

The knowledge which is generated instantaneously are converted into computer readable formats and made available to the users. Another uniqueness of the OKSS is segmentation of knowledge based on requirements from different users. Traditionally knowledge is arranged under different topics. The OKSS continuously searches for requirement of knowledge from users and arranges knowledge on a requirement basis. Another uniqueness of the OKSS is intelligent profiling of the users, in order to find out requirement of at least one kind of knowledge. OKSS uses intelligent profiling for finding out a knowledge requirement and/or a service requirement. Intelligent profiling is also used to find out a user's capability in providing some services. The unique part of profiling by OKSS is that a profile can made for be a human (100) or an intelligent machine (101) or an animal (102) connected with an electronic system. OKSS facilitates primary and secondary profiles of a user and tertiary profile of a non-user respectively. As mentioned earlier the amount of available knowledge in the world is increasing. In order to provide the most relevant knowledge to those who require knowledge, in a priority and emergency basis and to update the knowledge from time to time based on a "what is learned basis" and "what is further required" basis, accurate profiling of the knowledge seekers is very important. Knowledge is not only required by humans. Intelligent machines (101) and animals (102) also need knowledge. The intelligent machines (101) also require knowledge for many reasons, for improving their capabilities, avoiding previous mistakes etc. With advancement in technologies like IoT, animals, for example pets or farm animals will be attached to electronic systems. In many cases these electronic systems can be intelligent also. With existing systems or implanted with electronic systems, human and animal thoughts can be captured using brain-machine interface and signals can be directly or indirectly sent to the human and the animal brains. The OKSS can disseminate knowledge through brain-machine interface. Here what we see is that, using the OKSS, not only humans (100] and intelligent machines (101], but also animals can take part in the knowledge revolution.

OKSS profiles not only humans, but also intelligent machines [101] and animals (102) connected with electronic systems. The OKSS profiling engine collects information from a primary source (103), which also includes real time information. In the present digital world, where not only humans but also animals (102) and intelligent machines (101) are connected to computer networks such as internet or internet of things, OKSS facilitates a means to identify accurate knowledge requirement of each user and gives information regarding the capability of the user to consume a particular kind of knowledge and satisfies each user with required knowledge in the most appropriate way. OKSS also facilitates at least one service required by a user.

In one embodiment of the OKSS, first step in user profiling is user identification and classification. A user first interacting with the OKSS through the primary source (103) will be considered as a knowledge seeker by this invention.

The sources from where information and metrics are collected in order to profile a user is called a primary source (103). The unique nature of this invention is that, information and metrics are not only collected from online or internet activities of a user, but also from a variety of other sources that can give information and metrics regarding a user. These information and metrics are used to extract knowledge requirement from the user. These information and metrics are also used to find out capability of the user to provide at least one service. The primary source (103) includes internet, IoT, social media platforms in internet or other computer networks, collaboration platforms in internet or other computer networks, any computer network or mobile network with which the user interacts, brain-machine interfaces, knowledge updating or entertainment activities such as reading, interaction with television and radio broadcasts etc., other daily activities of the user or daily activities related to the user etc. Some of the said sources may be relevant to all kinds of users; but some of the said sources may be relevant to only one kind of user or any combination of different kind of users.

The primary source (103) also includes intelligent devices and/or software applications provided by the invention to profile a user from his/its daily activities. In one embodiment of the OKSS, these devices and software applications provided for profiling a user is called intelligent profiling systems (IPS). Some of the IPS are standalone where some of the IPS need to be connected to existing systems. One example of a standalone IPS is an intelligent profiling box (IPB). The IPB can pick up images and sounds from real world, convert it into computer readable format, and generate profile from the computer readable format. For example, IPB can pick up information and metrics from a user reading a book or watching TV, and convert the information and metrics into computer readable data and communicate the data with OKSS for profiling the user. Another example of the IPS is a digital broadcast assist (DBA) that can be connected to a digital TV or digital radio in order to extract information and metrics from the digital broadcast Here DBA and/or IPB can provide information regarding a user's favourite books, favourite subjects, favourite TV/ radio programmes etc.

DBA is also used as an IDSA [111) as explained later in relevant sections of this patent specification. In some cases intelligent software applications will be provided to execute in user devices such as mobile phones, in order to facilitate profiling of the user.

If the user is an animal [102), IPS can monitor the animal (102) and can extract information and metrics to profile the animal (102). Here the animal need not be connected to electronics in order to profile the animal. Some other device/devices provided by the invention are directly connected to animal (102) bodies and animal (102) brains and collect information and metrics for making the profile of the animal (102).

In one embodiment of the OKSS as shown in Fig 2, the profiling engine 201 will make various profiles of a user, namely primary profile, secondary profile and tertiary profile. The dynamic data collector (202) module in the profiling engine collects the information and metrics from the primary source (103) for profiling. The DDC will continuously search in the primary source (103) to collect as much as information and metrics possible to make primary profile, secondary profile and tertiary profile of as much as humans (100), intelligent machines (101) and animals (102) connected to electronic systems. The user classifier (203) in the profiling engine will identify a user as human (100) or intelligent machine (101) or animal (102) connected to electronic system and then classifies each identified user into various classes based on his/its capabilities. The OKSS makes intelligence-oriented identification (10 identification) and capability-oriented classification (CO classification) of the knowledge seekers. Intelligence-oriented identification identifies and classifies a first time user into human (100) (Level 1 User, UL1), intelligent machine (101) (Level 2 User, UL2) or animal (102) connected with an electronic system (Level 3 User, UL3). UL1, UL2 and UL3 are further classified based on their capabilities. The capabilities include knowledge rendering capability, learning capability, capability to provide any service etc.

Self-information provided can be used as a simple preliminary input for classification. But machines are made more and more intelligent, which is also the case with animals connected with intelligent machines, identification and classification of users into UL1, UL2 and UL3 is not an easy task. Self-introduction is requested by the OKSS from a user when he/it first interacts with the OKSS. But the self-information provided by the user may not be sufficient to classify knowledge seekers into UL1, UL2 and UL3.

In one embodiment of the OKSS, knowledge kinetics system (KKS) is used for IO identification and classification. KKS is a unique part of this invention. KKS can be defined as the effect of hierarchical structured knowledge pulses and corresponding response of users having different levels of intelligence. In one implementation of KKS, a hierarchical structure of automated identification queries is constructed and response for the queries is evaluated on the basis of knowledge movement to and from the users and further intelligence mapping. The OKSS measures the momentum of inward and outward knowledge movement (inward and outward knowledge momentum] from different users, refers to a reference intelligence map and differentiates between human intelligence, machine intelligence and animal intelligence. The reference intelligent map contains reference information on how to map inward and outward knowledge momentum with intelligence levels. After classification every user is given a unique identification (UID) code. These identification codes are stored in user tables (204A, 204B, 204C). The UID code of a user in the user tables (204A, 204B, 204C) points to the databases where the information and metrics regarding the user is stored. There will be unique UT for ULl, UL2 and UL3. The UT for ULl, UL2 and UL3 are shown as 204A, 204B and 204C respectively in Fig 2. The databases where the profiles of the users are kept are called information bases (IB). Information bases are generated by information base generator 205 in the profiling engine 201. There will be four kinds of information bases for storing information collected from the primary source (103) regarding a user, namely preliminary information base (PIB), cross-profile information base (CIB), user information base (UIB) and accurate profile base (APB). There will be unique PIB, CIB, UIB and APB for every user. PIB, CIB, UIB and APB for ULl, UL2 and UL3 are different from each other. Information bases for one user from ULl, UL2 and UL3 is shown in Fig 2. 206, 207, 208, 216 are the PIB, CIB, UIB and APB respectively for a user from ULl. 209, 210, 211, 217 are the PIB, CIB, UIB and APB respectively for a user from UL2. 212, 213, 214, 218 are the PIB, CIB, UIB and APB respectively for a user from UL3. The UID code of a user from corresponding UT (204A/204B/204C) points to all the four information bases of the user. UIB is a primary profile, CIB is a secondary profile and PIB is a tertiary profile.

If the user or non-user is a human, the information and metrics collected from the primary source (103) for making the various information bases include personal details of the user such as name, age, gender, family details, details of friends and relatives, location, educational qualifications, subjects of interest, skills, professional qualifications, profession or job, income. Information and metrics are collected regarding online activities of the user such as websites visited, websites owned, keywords used in search engines and web pages, online trading activities, online text messages, e-mails, audio and/or video discussions, social media activities, collaboration platform activities, file sharing activities, file downloading activities etc. Information and metrics are collected from mobile networks such as mobile phone conversations, social media networks and collaboration platforms, text messages, multimedia messages, application downloads etc. Information and metrics for profiling are collected from the IoT devices around the user such as IoT doors, IoT tables, IoT cups, IoT vehicles etc. IPS collects information and metrics regarding real world activities (106) directly or indirectly related to the user for making profile of the user. For example, an IPS installed in a bus station can extract information and metrics regarding travels of the user. An IPS installed in the user's office can extract details regarding nature of work of the user.

If the user is an intelligent machine (101) the information and metrics collected from the primary source (103) for making the various information bases include machine centric information such as make, year of manufacture, country of manufacture, details regarding humans and machines used to design the intelligent machine (101), details regarding humans and machines used to manufacture the intelligent machine (101), processing power, throughput, operating systems, memory capacity, technologies used, operating environment, functional specifications, mechanical capabilities, field of operation, artificial intelligence level, learning capabilities, capabilities related to movement, safety and security standards, environmental standards etc. Information and metrics regarding online activities of the intelligent machine (101) in internet or IoT or any other networks are also collected by the OKSS. IPS extracts information and metrics for profiling the intelligent machine (101) from the intelligent machine's interaction with other humans or machines or animals or any real world activities (106). If the user is an animal (102) connected with an electronic system, the information and metrics collected from the primary source (103) for making the various information bases include information such as class of the animal (102) such as mammal, reptiles, etc., animal type such as cow, goat etc., age of the animal, gender, any given name, any given code name, intelligence level, location of living, foods eaten, skills such as bomb detection, emergency rescue etc., any assigned work such as ploughing, housekeeping etc., location of work, topics of interest etc. Information and metrics regarding the electronic system, such as machine centric information, method of interaction with the animal (102) etc. are also collected for profiling.

Cross profiling is a unique part of OKSS. Cross profiling collects information and metrics regarding a user from other users. For example, if the user is a human (100), the information and metrics for cross profiling is collected from his family members, friends, teachers, and intelligent machines (101) used directly or indirectly by the user or any humans or animals directly or indirectly related to the user such as his pet animals, and other humans or animals directly or indirectly interacting with the user etc. If the user is an intelligent machine (101) the information and metrics for cross profiling is collected from the owner or authorized users of the intelligent machine (101), other intelligent machines (101) connected to the intelligent machine (101), other systems connected to the intelligent machine (101), animals (102) connected to the intelligent machine (101) etc. If the user is an animal (102) connected to an electronic system, the information and metrics for cross profiling is collected from the animal's owner, user/users related to the owner, other animals related to the animal, intelligent machine (101) connected to the electronic system, other systems connected to the electronic systems etc. PIB (206/209/212) for a user may be made before the user first interacts with the OKSS. PIB (206/209/212) for a user is the repository for information regarding a future user that is prepared from the profile of a current user. For example, when a human 100 user interacts with the OKSS, separate PIBs are made for other humans, intelligent machines or animals that are directly or indirectly related to the human 100 user. This is a unique part of this invention, where a profile base of a future user is extracted from the profile of a current user. A preliminary identification code (PIC) is provided to a user when his/its PIB is first generated. When a user first interacts with the OKSS, the system will generate CIB, UIB, APB, AGP1 and AGP2 for the user. Then the system will try to identify the user from any existing PIB. This means the system will check for any tertiary profile of the new user. If the user cannot be identified from any existing PIB (206/209/ 212], CIB (207/210/213] and UIB (208/211/214] for the user are updated with information and metrics collected from the primary source 103.

The flowchart of generating the IB and AGP is shown Fig 3.

UIB for a user is the repository for user information directly collected from the user. CIB (207/210/213] for a user will be the repository for user information indirectly collected from other users related to the user. If a PIB (206/209/212] for a user existed, the information from the PIB (206/209/212] is copied to the CIB (207/210/213] when a user first interacts with the system. PIB (206/209/212] is deleted after CIB is made. An accurate profile base (APB] will be made by profile processor (PP] 215 after processing the information from CIB (207/210/213] and UIB (208/211/214] together. 216 is the APB of a user from UL1, 217 is the APB of a user from UL2 and 218 is the APB for a user from UL3.

PP 215 makes use of intelligent and self-learning algorithms and applications to prepare APB (216/217/ 218] from CIB (207/210/213] and UIB (208/211/214].

The OKSS continuously collects as much as information and metrics regarding each user from the primary source 103, directly or indirectly, and stores the information in CIB (207/210/213) and/or UIB (208/211/214) of the user. As a result all information bases of all users will be continuously updated. Fig. 2 is the system architecture for generating information bases for various classes of users namely UL1, UL2 and UL3.

One kind of information in the APB (216/217/218) will be correlated keywords generated from CIB (207/210/213) and UIB (208/211/214) of the user. These correlated keywords are made by the PP by processing all keywords generated from the APB (216/217/218) of a user.

Correlated keywords are a set of keywords that points to a particular knowledge requirement. For example if the keywords are rose, hibiscus, lilly, fertilizers, plant shops, watering equipment etc., this can point to a knowledge requirement related to gardening. This correlation is made from processing the entire profile of the user in real time. If the keywords are Rose, Hibiscus, Lilly etc. and the profile of the user says that he is a school student and the real time information collected says that he is having a biology exam in near future, the knowledge requirement may point to a model question paper or current information regarding the plants that are not available in the text books or the requirement may be a service requirement in providing a teaching service.

A unique nature of the OKSS is that keywords not only mean the words generated by a user in computer readable formats during online activities. Keywords are continuously extracted from the profile of a user by the OKSS and correlations are made to extract a knowledge requirement. Keywords can be generated from brain-machine interface of a user, a user's interaction with any audio-visual broadcasts such as radio and television broadcasts or from real world activities (106) of the user. For example, the OKSS generates several keywords related to setting a garden from the brain-machine interface of a user while he is thinking about setting a garden. The intelligent profiling box (IPB) provided by the OKSS can monitor a television programme being watched by a user and generate keywords related to the programme. All the generated keywords related to a user are used to find out a knowledge requirement from the user, or to find out a requirement of a service by the user or to find out the capability of a user to provide some service.

The knowledge requirements of a user are extracted from the APB (216/217/218) by knowledge requirement extractor (KRE). KRE uses intelligent and self-learning algorithm to extract knowledge requirement of a user from his profile in APB (216/217/218). The knowledge requirement from each user is stored in knowledge requirement table (KRT). There will be a unique KRT for each user. The KRT contains information regarding all knowledge requirements of a user.

The capability of a user to provide knowledge or the capability of the user to provide one service is extracted from the APB (216/217/218) by capability extractor (CE). The CE uses intelligent and self- learning algorithm to extract the capabilities possessed by a user from his/its profile in APB (216/217/218). The capabilities possessed by a user to render a service are stored in capability table (CT). There will be a unique CT for each user.

The CT of a user is compared with CT of other users in order to give credit to each user based on his/its capability. For example, if the CT of a human (100) user shows that the user is capable to do electric wiring works, the user's capability to do the electric wiring work is compared with the capability of other users who can do electric wiring work. This generates a list of capable users to do electric wiring works, namely service list of electric wiring works. The service list of electric wiring works is arranged from most capable user to do electric wiring works to least capable person in doing electric wiring works. The maximum credit is given to most capable user to do electric wiring works. The minimum credit is given to the least capable person to do electric wiring works. A service capability base (SCB) 110 is made from information collected from CTs of all users.

The requirement of at least one service from a user (100, 101, 102) is extracted from the APB (216/217/218) by service requirement extractor (SRE). The SRE uses intelligent and self-learning algorithm to extract the capabilities requirement from a user from his/its profile in APB (216/217/218). The services required by a user will stored in a service requirement table (SRT). There will be a unique SRT for each user.

An automatic service information provider (ASIP) will search for requirement of a service from SRT of a user and finds out at least one user who can provide the service from the SCB (110). If at least one user or non-user who can provide the service is found, the information is communicated with user who requires the service. If no user or non-user is found to render the service, ASIP searches the knowledge base 107 to find out any knowledge segment, learning of which will make a user gain the capability to do the service. If no such knowledge segment is found, the knowledge to gain the capability to do the service will be marked as special knowledge requirement and added to knowledge segment table (502). The knowledge to gain the capability to do the service will be generated by the OKSS and added to the knowledge base (107). The knowledge base (107) and knowledge segment table (502) will be explained in relevant sections of this patent specification. An automatically generated profile page (AGP) will be generated for each APB (216/217/218). Two kinds of AGP are generated for each APB (216/217/218), primary AGP [AGPl] (401/403/405) and secondary AGP [AGP2] (402/404/406). AGPl (401/403/405) and AGP2 (402/404/406) are the front end interfaces of the profile of a user, where any human or intelligent machine or animal connected to electronic system can view the profile of a user. AGPl (401/403/405) can be viewed by any user but cannot be edited by anyone including the user himself/itself. This means that a user can only change his profile information in AGPl (401/403/405) by changing the way he/it interacts with the OKSS.

Like AGPl (401/403/405), AGP2 (402/404/406) also can be viewed by any user. The user can edit the entries in AGP2 (402/404/406) thus enabling a means to update some of his profile information manually and make other users view the manually edited profile entries. Other users can also edit the "profile suggestion section" of AGP2 (402/404/406), thus providing other users a means to provide suggestions in the profile of the user. But the profile suggestion section in AGP2 (402/404/406) of the user can be disabled by the user, so that no other users can provide suggestion in AGP2 (402/404/406) of the user. If the user is an intelligent machine (101) or animal (102) connected to an electronic system, AGP2 (402/404/406) can be edited by authorized human users (100). The authorized users are automatically detected by the OKSS from the profile of the intelligent machine (101) or the animal (102) connected to electronic system. For example, a pef s owner can provide suggestion regarding knowledge requirement of his pet through AGP2 (402/404/406) of his pet. AGPl (401/403/405) and AGP2 (402/404/406) can be accessed by other systems which need the profile information of the user. For example, a recruiting agency can get the profile information of a human user (100) for facilitating job requirements. The OKSS provides various hardware and software interfaces for facilitating the above mentioned function.

APB (216/217/218) is updated with edited contents from AGP2 (402/404/406). The entries in the profile suggestion section AGP2 are written back to the APB (216/217/218) based on the relationship of user/users that provided the profile suggestions in AGP2 (402/404/406) of the user. More weightage is given to a user who is more closely related to the user in one way or another. The said relationship is verified from the profile of the user. For example, a parent or teacher can provide suggestion regarding the knowledge requirement of a user who is a student through the profile suggestion section in AGP2 (402). His teacher's suggestion regarding the school exams of the student may be given more weightage than his parent's suggestion regarding the school exam. The entries from KRT of a user are transferred to "knowledge requirement" section of AGP2 [402/404/406] of the user. A user can view the contents of the KRT through AGP2 [402/404/406]. A user is always prompted to view the knowledge requirement section in AGP2 [402/404/406] whenever a new knowledge requirement is extracted. The user can edit the knowledge requirement entries in the AGP2 (402/404/406) and the edited entries are written back to the corresponding section in the KRT. In this way, a user can control and monitor the knowledge requirement generated and can ensure that he/it is provided with relevant knowledge only.

In the case of intelligent machines (101) and animals (102) connected to electronic systems, human users who are authorized to use the intelligent machines (101) or animals (102) connected to electronic systems or human users (100) who own the intelligent machines (101) or the animals, or human users (100) who are related to the intelligent machines (101) or the animals can access the corresponding AGP2 (404/406) of the intelligent machines (101) or the animals (102). The said authorized human users are identified by the OKSS from the profile of the intelligent machine (101) or the animal (102).

Presently there are several social media platforms in the internet. All these social media platforms are for humans. His profile on the social media pages is updated by him or his authorized person(s) or other people can post contents to his social media pages. A unique feature of this invention is that the OKSS facilitates automatically generated social media (AGSM) pages for humans, intelligent machines (101) and animals in the form of AGPl (401/403/405) and AGP2 (402/404/406). AGPl and AGP2 pages may be similar to existing social media pages in appearance, but the unique part is that a user cannot create it himself/itself. Also only those entries from AGP2 (402/404/406) are editable.

AGPl (401/403/405) and AGP2 (402/404/406) of a user contain information regarding the knowledge requirements of a user, information regarding capabilities of the user to provide any service and information regarding the requirement of at least one service by the user. As mentioned earlier AGPl (401/403/405) and AGP2 (402/404/406) of a user can be viewed and accessed manually or through any other systems. The OKSS provides hardware and/or software interfaces for other systems to access AGPl (401/403/405) and AGP2 (402/404/406) of a user thus facilitating interoperability between the OKSS and other systems.

The structure of AGSM pages for humans (100), intelligent machines (101) and animals (102) connected to electronic systems will be different from each other. There will be a bridge interface which connects between AGSM pages for humans (100), intelligent machines (101) and animals (102) connected with electronic systems in order to facilitate cross-profiling. The bridge interface also facilitates knowledge sharing between humans (100), intelligent machines (101) and animals (102) connected to electronic systems. The bridge interface also facilitates finding out capabilities and requirements of service between UL1, UL2 and UL3.

Fig 4 is the system architecture for automatically generated profile pages. AGPl 401 and AGP2 402 of a user from UL1 are prepared from the APB 216 of corresponding user. AGPl 403 and AGP2 404 of a user from UL2 are prepared from the APB 217 of corresponding user. AGP1 405 and AGP2 406 of a user from UL3 are prepared from the APB 218 of corresponding user.

A unique nature of the invention is that knowledge is organized as requirement based knowledge segments (RKS). The OKSS continuously searches for any knowledge requirement from any user from the primary source (103), finds out the required knowledge contents for each requirement by sourcing from a knowledge source (105) and/or generating the required knowledge contents from the real world activities (106), process the knowledge and organizes the processed knowledge into RKS. Each RKS will be further organized into knowledge pools (PK) of different complexity levels suitable for different users with different learning capabilities. Knowledge segmentation can be defined as the identification and organization of portions of a particular kind knowledge that are different from one another, based on a requirement. Segmentation allows knowledge or information to better satisfy the needs of its potential consumers.

The need for knowledge segmentation arises from the fact that different people looks upon a particular knowledge field in different ways. For example, a gardener's requirement regarding knowledge about a particular species of plant may differ from a botanist's requirement regarding knowledge about the particular species of plant.

Knowledge Segmentation calls for understanding knowledge requirement from a particular user, who can be called a knowledge consumer, for satisfying the user's needs in the best way. This is because different users have different knowledge needs, and it rarely is possible to satisfy all knowledge consumers by treating them alike. Even in a particular class of knowledge consumers, there may be differences in requirements, understanding capability etc. Knowledge Segmentation recognizes the diversity of knowledge consumers and does not try to please all of them with the same offering.

Basically the learning capabilities will be different for UL1, UL2 and UL3. Therefore there will be different knowledge pools for UL1, UL2 and UL3 even if the RKS is same. Consider an example of a human (100), an intelligent machine (101) and a dog connected with an intelligent electronic system working in bomb detection. If the human (100), the intelligent machine (101) and the dog are communicating with the OKSS, all of them can make use of the knowledge provided by the OKSS. The physical and intelligence capabilities of the human (100), the intelligence machine and the dog may be different, so are the safety requirements. A human is more valuable than an intelligent machine or dog in sense of safety. All these things affect the organization of knowledge pools for the human, the intelligent machine and the dog, even if the RKS is same.

As mentioned earlier there will be a KRT for each user. The KRT contains information regarding all knowledge requirements of a user. There will be unique KRT for all users interacting with the OKSS. The KRT of a user will be updated when the KRE finds out a new requirement of knowledge from profile bases of the user. The KRT is also updated when the KRE finds out a change in knowledge requirement of the user due to some reasons. One of the said reasons for updating KRT is presentation of the required knowledge by the OKSS and subsequent learning by the user.

One of the important steps in OKSS is the generation of knowledge segment table (KST]. The system architecture for generation of KST (502) is illustrated in Fig 5. 1A, 2A....nA are the KRT of user 1, user 2...user n respectively. A similarity correlator (501) will read entries from KRTs (1A, 2A....nA ) of all users, identifies identical knowledge requirements, gives a common title for the identical knowledge requirements, gives a unique identifier (KSID) for the title and stores the KSID in the KST. Each entry in the KST (502) will correspond to a requirement based knowledge segment (RKS). Different RKS are stored in segmented knowledge base 107. Segmentation of knowledge is done based on the entries in KST (502). The KSID will contain all information to source, generate, process and pool the knowledge contents required for the RKS. The KSID will also contain pointers to UID of users who require the RKS. After the RKS is generated knowledge is presented to the required user/users using the UID of the corresponding user. An RKS engine will read each knowledge segment table (502) entry, find out the required knowledge contents from the knowledge source (105) and/or generate the required knowledge contents from the real world, process the knowledge and generates the RKS and add it to the a knowledge base. The knowledge base will be organized into different RKS and each RKS will be organized into KP.

Fig. 6 is the system architecture for generation of segmented knowledge base 107 using the entries from KST 502.

An RKS engine (601) will read each KST (502) entry, identify the required knowledge contents from the knowledge source (105) and/or generate the required knowledge contents from the real world, process the knowledge and generates the RKS and add it to the a knowledge base. The knowledge base will be organized into different RKS and each RKS will be organized into different PK.

The RKS engine (601) sources the knowledge content for each KST (502) entry from the knowledge source (105) and/or generate from the real world activities (106). The knowledge source (105) are those sources of knowledge, information, metrics, data, knowledge materials, statistics, facts, figures, diagrams, pictures, audio, video, evidence etc. available in computer readable format These sources include internet, internet of things, social media platforms in internet or other computer networks, collaboration platforms in internet or other computer networks, any computer network or mobile network, brain-machine interfaces, audio-visual broadcasts such as TV or radio broadcasts etc. The knowledge source (105) also includes knowledge provided by different users with or without payment. This process is called knowledge rendering.

The knowledge hunting system (61) in the RKS engine will find out all sources of the required knowledge contents from the knowledge source (105) and extracts relevant knowledge contents. The knowledge hunting system (61) will have special applications to extract knowledge content from video, audio and images. The knowledge hunting system (61) will also ask for knowledge rendering from different users who possess the capability to provide the knowledge. The capability related information of a user will be got from his profile bases. A first knowledge pre-processor (603) will process the extracted knowledge contents and/or the knowledge content provided by any users and stores the processed knowledge contents in an intermediate knowledge database [IDB1] (605).

As mentioned before a unique feature of the OKSS is knowledge generation. The RKS engine sources knowledge from real world activities (106) through knowledge generation. The generation of knowledge from real world activities (106) is facilitated using intelligent devices and software applications [IDSA] (111) provided by the invention. The knowledge generator (62) in the RKS engine communicates with IDSA (111) to facilitate knowledge generation. Some IDSA (111) is standalone where some IDSA (111) needs to be connected to existing systems. One example of a standalone IDSA (111) is a knowledge generating box (KGB). The KGB can pick up images and sounds from real world, convert it into computer readable format, and generate knowledge from the computer readable format. Another example of the IDSA (111) is a digital broadcast assist (DBA) that can be connected to a digital TV or digital radio in order to extract from the digital broadcast. In some cases intelligent software applications will be provided to execute in devices like mobile devices, in order to facilitate knowledge generation.

A second knowledge pre-processor (604) will process the generated knowledge contents and stores the processed knowledge contents in another intermediate knowledge database [IDB2] (606). The knowledge processor (607) will process the information from IDB1 (605) and IDB2 (606) together, generates the required knowledge segment and stores the knowledge segment a knowledge data base (608). The segmented knowledge in the knowledge data base (608) is processed by knowledge pooling engine (609) and one or more knowledge pools (PK) are made based on the capability of the users who require the knowledge segment.

The entire PK is stored in the segmented knowledge base (107). Thus the segmented knowledge base 107 contains knowledge organized into various knowledge segments and each knowledge segment is further organized into different knowledge pools. This is illustrated in Fig 7. IB, 2B, 3B....nB are the various RKS.

The knowledge segment IB is further organized into PK (1C, 2C nC). The knowledge segment (2B) is further organized into PK (ID, 2D nD). The knowledge segment (3B) is further organized into PK (IE,

2E nE). Similarly the knowledge segment nB is further organized into PK 1, 2 n. The knowledge base architecture shown in Fig. 7 is an exemplary case only. In some cases the number of PK under different RKS will be different in number.

Knowledge presentation is one of the most important parts in learning. In order to make learning effective and to make the user satisfied with provided knowledge, the knowledge must be presented to the user in the most appropriate way and at the most appropriate time. The complex problem with knowledge presentation is that each user can be unique in many ways even though there are many similarities. Learning is a mix of personal effort and collaborative effort. A unique part of the OKSS is that, it provides an integrated platform for presenting knowledge to humans

(100) , intelligent machines (101) and animals (102) connected to electronic systems. In OKSS, learning is considered as a collaborative effort by many users in order to make a user learn knowledge.

Knowledge presentation enables a human (100) to determine consequences by thinking, practice by acting and reflects by reasoning about the world rather than taking action without the required knowledge. In case of intelligent machines (101), knowledge presentation makes it accomplish its prescribed tasks with maximum efficiency. For intelligent machines (101) knowledge presentation provides for organizing information so as to facilitate making the prescribed inferences. Effect of knowledge presentation to animals (102) is almost similar in concept with humans (100) but the level of acting may succeed thinking and reasoning. For animals (102) knowledge presentation is a form of guidance from humans (100), intelligent machines (101) or other animals (102).

The uniqueness of the OKSS is that, it carefully identifies a world where humans, intelligent machines

(101) and animals (102) connected to electronic systems coexist, and provides a knowledge presentation platform that is collaborative in nature considering all similarities and dissimilarities between humans, intelligent machines (101) and animals (102) connected to electronic systems considered.

The OKSS provides a framework useful for characterizing a wide variety of knowledge presentations. It takes into consideration that knowledge presentation can be captured by a user by understanding how the user views each of the presented knowledge, and that doing so reveals essential similarities and differences. Understanding how the user views a presented knowledge and acknowledging the similarities and dissimilarities between different users has several useful consequences. First, each user, even though in the same class, requires something slightly different form of presentation; each accordingly leads to a unique and different knowledge presentation policy for each user.

The knowledge presentation policy is a sequence of processes used in making the user learn the provided knowledge, like allocating computing and network resources used to present the knowledge to the user, selecting the natural language of presentation, selecting the mode of presentation like audio, video, text or brain-machine interface or any combination thereof, selecting complexity of the provided knowledge, selecting the time of presentation of the knowledge and all other processes required to disseminate the required knowledge to the user in the most appropriate way. As mentioned before knowledge are segmented on the basis of requirements from various users and organized as RKS (IB, 2B, 3B....nB) and the various RKS are stored in the segmented knowledge base (107). Each RKS is further organized into different PK. There will be different knowledge pool for UL1, UL2 and UL3. Under each class, knowledge pool will be different based on learning capabilities. For example, for a particular RKS there will be different PK for UL1, UL2 and UL3. For UL1, the PK for a school student and PK for a university student will be different. Even among school students of same standard and age, there may be different PK for different students. Each PK will be organized in such way that the presented PK provides the most relevant knowledge to the user who requires the knowledge in the most suitable form.

Fig. 8 is the system architecture for knowledge presentation and feedback.

The knowledge presentation policy is generated by the knowledge presentation policy generator [KPPG] (802). KPPG (802) will make use of the profile information of a user from APB (216/217/218) in order to generate a knowledge presentation policy that best suits to the user. The generated knowledge presentation policy will be modified if feedback from the user informs that the user is not satisfied with the presentation method facilitated by the knowledge presentation policy. The process of modifying the knowledge presentation policy is continued until the user is satisfied with the presentation method facilitated by the modified knowledge presentation policy.

The formatting engine [FE] (801) selects a particular PK from the segmented knowledge base (107) based on the immediate requirement of a user. The immediate knowledge requirement of the user is provided by the APB (216/217/218) of the user. The APB (216/217/218) also gives details about the knowledge presentation method most appropriate for a user at the time of presentation. The FE (801) converts the knowledge content from the PK into a format most appropriate for the user, such as text, audio, video, brain-machine interface, social media message, short message service (SMS) or multimedia message service (MMS), machine to machine message etc. The PK formatted by FE (801) is called formatted knowledge pool (FPK).

If a natural language knowledge presentation is required, the natural language knowledge formatter (NLKF) in the FE (801) will convert the PK into natural language knowledge content in various formats such as text, audio, video, brain-machine interface, social media message, SMS or MMS. The natural language selected for a user will be the language most appropriate for the user whether the user is a human (100), intelligent machine (101) or an animal (102) connected to electronic system.

Further if the user is an intelligent machine (102), the selected format of presentation will be most appropriate for the intelligent machine in terms of machine to machine language, communication speeds etc. If the user is an animal (102) connected to an electronic system, the format of presentation will be most appropriate for the animal and the electronic system connected to the animal.

The FPK is delivered to a user by the presentation engine (803) through the presentation medium (804). Presentation medium (804) can be any one of the medium through which knowledge is disseminated to a user such as audio, video, visual indicators, text, social media messages, SMS, MMS, machine to machine interfaces, brain-machine interfaces, TV/radio programmes etc.

The feedback unit (109) will collect information and metrics from the primary source (103) and process the information and metrics to identify the feedback of the user regarding the presented FPK. The feedback engine verifies whether the user learned the provided FPK. The feedback unit (109) will communicate with the presentation engine (108) to continue the knowledge presentation if the user is satisfied with presented FPK. If the user is not satisfied with presentation method of the presented FPK, the feedback unit (109) will communicate with the presentation engine (108) to modify the presentation policy until the user is satisfied with the presentation method. If the user is not satisfied with the format of the presented FPK, the feedback unit (109) will communicate with the presentation engine (108) to change PK or change the format of presentation or both. OKSS also provides facility to a user to manually search for knowledge. One example of manual knowledge search facility provided by OKSS is a knowledge hunting engine (KHE). KHE can accept a keyword from a user and provide the user with most relevant knowledge in a priority order. It makes use of the profile information of the user from information bases such as APB and front end interfaces such as AGP1 and AGP2. KHE compares the keyword provided by the user with various RKS in the segmented knowledge base. If an RKS is found that is most related to the keyword provided by the user, that RKS is selected as a relevant knowledge by KHE. KHE then selects the right knowledge pool for the user based on his/its profile information.

If KHE cannot find an RKS that is related to the keyword, then KHE generates a knowledge requirement from the keyword and the profile information of the user and adds the knowledge requirement to KST. The required knowledge related to the keyword is sourced from the knowledge source and/or generated from the real world activities, corresponding RKS is generated and the RKS is organized into various PK. Then the KHE will provide the user with relevant knowledge corresponding to the keyword.

For example, Mr E is a botany student and he wants more scientific information regarding "rose". When he uses the keyword "rose" in KHE, the KHE presents the user with the right PK that is relevant to Mr E, where the presented PK contains scientific information regarding rose. Consider the case of Mr F who wants to set a garden in his house. He wants to know the availability of rose plants in his locality and its price. When Mr F uses the keyword "rose" in KHE, the KHE presents the user with the right PK that is relevant to Mr F, where the presented PK contains shops selling rose plants in Mr F's locality, its price and more details on gardening.. Following are some examples on how the OKSS is useful for users.

Example 1

Mr. A wants to go to KL Sentral from Bangsar. If Mr A is a user of OKSS, the OKSS suggests all possible ways of travel from KL Sentral to Bangsar. From the profile of Mr. A, OKSS will suggest the cheapest method of transport from KL Sentral to Bangsar, because the profile says that Mr A is jobless and deprived of money. It can also inform Mr A regarding some other users or non-users who are travelling from KL Sentral to Bangsar, and are tolerable to hitchhiking. If Mr. A is a non user, he will still acquire the knowledge from another user as long Mr. A is related to the user.

Example 2

Mr. B is having mathematics exam the next day. If Mr Y is a user of OKSS, the system provides most relevant knowledge to Mr. B. For example, it may come out with a latest social media discussion of a mathematics teacher with his students regarding most important topics. If Mr. B is having some doubts on how to solve an equation, OKSS will provide an explanation on how to solve the equation in the most appropriate form. If OKSS finds that Mr. B is still not able to catch up the explanation, the OKSS can inform Mr. B about any user or non-user who can help Mr Y in clearing the doubt. Example 3

Mr. C got an intelligent toothbrush that is connected to OKSS. From OKSS the intelligent toothbrush learns that there is a new tooth disease spreading in Mr. C's locality and any information regarding the disease is published somewhere. OKSS sourced the information from a discussion between two dentists who are expert in diagnosing the disease. If the user is having any indications of the disease, which undetected by anyone, OKSS will inform the user regarding the indications and provides information regarding the dentists who are expert in diagnosing and treating the disease.

Example 4

Mr. D got a pet cat which is connected to an intelligent electronic device, called a pet collar, which is connected to OKSS. The pet collar is connected to an electrode implanted in the cat's brain which facilitates a brain-machine interface. The cat finds a snake in its owner's bedroom, who is outdoors then. Then information from the caf s brain is taken by OKSS and the information is passed to the owner, who is a user communicating with OKSS.

Claims

1. An organized knowledge and service system (OKSS) for providing structured and updated knowledge to a user who is interacting with the OKSS in one way or another by organizing knowledge based on knowledge requirement of the user, wherein the OKSS includes:

(a networked computer systems comprising of (i) a means to collect information and metrics from a primary source (103) in the form of computer-readable data (202], (ii) a computer readable database for storing the computer-readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218), (iii) a processor and a self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for manipulating the computer-readable data (205) and (iv) at least one hardware and software communication interface for communicating the computer-readable data between systems, subsystems and the user;

(b) at least one self-learning, adaptive, and intelligent computer system comprising of: (i) a means of accepting the computer- readable data in various formats, (ii) a means of generating a profile of the user from the computer-readable data (215) (iii) a means of extracting at least one knowledge requirement of the user from the profile of the user, (iv) a means to find at least one knowledge source (105), (v) the self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for extracting the knowledge from at least one knowledge source (105) and further processing the extracted knowledge and generating an organized knowledge that meet the knowledge requirement of the user (603, 604, 607), (vi) a means to store the organized knowledge in the form of the computer readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218), (vii) a means to generate a knowledge presentation policy based on the profile of the user and (viii) a means of presenting the organized knowledge generated to the user by executing the knowledge presentation policy; and

(c) a system for feedback and evaluation of success of the knowledge provided to the user, comprising of: (i) a means for collecting information and metrics from the primary source (103) of knowledge in the form of computer readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218), wherein the collected information and metrics provide details regarding feedback and evaluation of success of the knowledge provided to the user, (ii) a means to store the information and metrics so collected in a computer readable memory for further processing, (iii) a means to use a self-learning, adaptive, intelligent and knowledge processing computer application and algorithm for processing the information and metrics, and (iv) a means to update the organized knowledge or modify the knowledge presentation policy.

2. The OKSS of claim 1, wherein the user is a human (100) or an intelligent machine (101) or an animal (102) connected to an electronic system.

3. The OKSS of claim 2, wherein the primary source (103) is any one of the sources from where information and metrics are collected in order to profile a user, such as internet, internet of things, social media platforms in internet or other computer networks, collaboration platforms in internet or other computer networks, any computer network or mobile network with which the user interacts, brain-machine interface connected to the user if the user is a human (100) or an animal, knowledge updating or entertainment activities of the user including reading, interaction with television and radio broadcasts if the user is human (100), real world activities (106), intelligent profiling systems etc.

4. The OKSS of claim 2, wherein the knowledge source (105) is any one of the sources of information, metrics, data, knowledge materials, statistics, facts, figures, diagrams, pictures, audio, video, evidence etc. available in computer readable format, such as internet, internet of things, social media platforms in internet or other computer networks, collaboration platforms in internet or other computer networks, any computer network or mobile network with which the user interacts, radio or television broadcasts, any brain machine interface, real world activities (106), intelligent knowledge generating devices and applications etc.

5. The OKSS of claim 3 or 4, wherein the real world activities (106) include activities such as daily activities of humans, machines, animals and other worldly beings from where knowledge can be generated and/or information and metrics can be extracted to profile a user.

6. The OKSS of claim 5, wherein intelligent devices and software applications are provided to generate knowledge from real world activities (106) and to collect information and metrics from real world activities (106) to profile a user.

7. The OKSS of claim 1, wherein feedback and evaluation of success of the knowledge provided to the user is measured in terms of user satisfaction or improvement in capability of the user or achieving goals intended by the user by learning the knowledge.

8. The OKSS of claim 1, wherein the organized knowledge is updated or the knowledge presentation policy is modified if the user is not satisfied with the provided knowledge or the provided knowledge is not sufficient to meet the user's requirement.

9. The OKSS of claim 7, wherein updated organized knowledge or modified knowledge presentation policy is provided to the user until the user is satisfied with the provided knowledge.

10. The OKSS of claim 1, wherein the OKSS further includes a means to identify requirement of at least one service from the user.

11. The OKSS of claim 1, wherein the OKSS further includes a means to identify capability of the user to provide at least one service.

12. The OKSS of claim 1, wherein the OKSS further includes a means to credit the user in terms of knowledge or capability to render at least one service.

13. The OKSS of claim 2, when the user is a human (100], the human (100] interacts with the OKSS through the primary source [103].

14. The OKSS of claim 2, when the user is an intelligent machine (101], the intelligent machine (101] interacts with the OKSS through the primary source (103],

15. The OKSS of claim 14, wherein the intelligent machine (101] has provisions to make use of the knowledge provided by the OKSS.

16. The OKSS of claim 2, when the user is an animal (102] connected to an electronic system, the electronic system interacts with the animal (102] through a means such as audio, video, electric shock, visual indications, brain-machine interface etc.

17. The OKSS of claim 2, when the user is an animal (102] connected to an electronic system, the electronic system interacts with the OKSS through the primary source (103].

18. The OKSS of claim 13, 14 or 17 wherein the means of generating a profile of the user from the primary source (103] uses the profiling engine which includes: a] a dynamic data collector (202] which continuously collects various information and metrics from the primary source (103] in the form of computer readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218];

b] a user classifier (203] to identify a user as a human (100] or an intelligent machine (101] or an animal (102] connected to electronic system and to classify each identified user into various classes based on his/its capabilities;

c] a means to provide every user with a unique identification (U1D] code;

d] a means to store UID code in a computer readable memory;

e] a means to generate various information bases for each user; and

f] a means to generate automatically generated profile pages (AGP] for each user.

19. The OKSS of claim 18, wherein the means to generate various information bases for each user includes:

a) an information base generator (205] to generate user information base (UIB) for each user to store primary profile;

b] the information base generator (205] to generate cross-profile information base (CIB] for each user to store secondary profile; c) the information base generator (205) to generate preliminary information base (PIB) of a non-user to store tertiary profile; and

d) a profile processor (PP) to generate accurate profile base (APB) of a user by processing contents of CIB and UIB together.

20. The OKSS of claim 19, wherein the primary profile of a user is the profile generated by collecting information and metrics directly from the user's interaction with the primary source (103).

21. The OKSS of claim 19, wherein the secondary profile of a user is the profile generated by collecting information and metrics from interaction of other users who are directly or indirectly related to the user.

22. The OKSS of claim 19, where in the tertiary profile of a non-user is the profile generated by collecting information and metrics from any user who is directly or indirectly related to the non- user.

23. The OKSS of claim 19, wherein the user classifier (203) uses knowledge kinetics system to identify a user as a human (100) or an intelligent machine (101) or an animal (102) connected to electronic system.

24. The OKSS of claim 19, wherein primary AGP [AGPl] (401, 403, 405, 403) and secondary AGP (AGP2) are generated for each user from his/its APB.

25. The OKSS of claim 24, wherein AGPl (401, 403, 405, 403) and AGP2 are the front end interfaces of the profile of a user, where any human (100) or intelligent machine (101) or animal (102) connected to electronic system can view the profile of a user physically or through electronic means or a combination of both.

26. The OKSS of claim 24, wherein AGPl (401, 403, 405, 403) can be viewed by any user but cannot be edited by anyone including the user himself/itself.

27. The OKSS of claim 24, wherein a user can edit the entries in his/its AGP2, thus enables a means to update some of his profile information manually.

28. The OKSS of claim 27, wherein any user can edit the profile suggestion section of a user's AGP2.

29. The OKSS of claim 28, wherein a human user (100) can disable editing of profile suggestion section in his AGP2 by other users.

30. The OKSS of claim 28, wherein if the user is an intelligent machine (101) or an animal (102) connected to an electronic system, the AGP2 of the user can be edited by authorized human users (100).

31. The OKSS of claim 30, wherein the authorized users are automatically detected by the OKSS from the profile of the intelligent machine (101) or the animal (102) connected to electronic system.

32. The OKSS of claim 26, wherein AGP1 and AGP2 can be accessed by other systems which need the profile information of the user.

33. The OKSS of claims 27, 28, or 30 wherein the APB of a user is updated when AGP2 of the user is edited.

34. The OKSS of claim 19, wherein the knowledge requirement of a user is extracted from APB of the user by knowledge requirement extractor (KRE).

35. The OKSS of claim 19, wherein the capability of a user to provide knowledge or the capability of the user to provide at least one service is extracted from APB of the user by capability extractor (CE).

36. The OKSS of claim 19, wherein the requirement of at least one service from a user is extracted from APB of the user by service requirement extractor (SRE).

37. The OKSS of claim 34, wherein the knowledge requirement of a user is stored in his/its knowledge requirement table (KRT).

38. The OKSS of claim 35, wherein the capability of a user to provide knowledge or the capability of the user to provide at least one service is stored in his/its capability table (CT).

39. The OKSS of claim 38, where the capabilities of all users are stored in service capability base (SCB).

40. The OKSS of claim 36, wherein the requirement of at least one service from a user is stored in his/its service requirement table (SRT).

41. The OKSS of claim 39, wherein an automatic service information provider (ASIP) will process information from SRT of a user with information from SCB to present information to the user about other users who can provide the required service to the user.

42. The OKSS of claim 40, wherein if a required service for a user cannot be found from CT of any user, the required service will be marked as special knowledge requirement and added to the knowledge segment table (502] to generate a knowledge segment, where the learning of the knowledge segment by a relevant user will give the user a capability to provide the required service.

43. The OKSS of claim 37, wherein the entries of KRT of a user can be viewed by any user and can be edited only by the user through the AGP2 of the user.

44. The OKSS of claim 39, wherein the entries of SRT of a user can be viewed by any user and can be edited only by the user through the AGP2 of the user.

45. The OKSS of claim 42, wherein the edited KRT entries in the AGP2 are written back to the KRT, which enable the user to edit his/its knowledge requirements.

46. The OKSS of claim 43, wherein the edited SRT entries in the AGP2 are written back to the SRT, which enable the user to edit his/its service requirements.

47. The OKSS of claim 24, wherein a bridge interface between AGPl and AGP2 of humans, intelligent machines (101] and animals (102] connected to electronic systems facilitates sharing of information between humans, intelligent machines (101] and animals (102] connected to electronic systems.

48. The OKSS of claim 1, wherein knowledge is organized into requirement based knowledge segments (RKS], where each RKS corresponds to a requirement from a user or identical requirements from different users.

49. The OKSS of claim 48, where in the each RKS is further organized into knowledge pools (607] of different complexity levels suitable for different users with different learning capabilities.

50. The OKSS of claim 48, wherein a similarity correlator (501] will read entries from KRT of all users, identifies identical knowledge requirements if any, gives a title for identical knowledge requirements, gives a unique knowledge segment identifier (KSID] for the title and stores the KSID in a knowledge segment table (502].

51. The OKSS of claim 48, wherein an RKS engine will read each knowledge segment table (502] entry, find out the required knowledge content from the knowledge source (105] and/or generate the required knowledge content from real world activities (106], process the knowledge content and generates the RKS and add it to the a knowledge base.

52. The OKSS of claim 49, wherein each RKS is further processed by a knowledge pooling engine (609) to generate at least one knowledge pool, where each knowledge pool has a different complexity level which is suitable for a user with a particular learning capability.

53. The OKSS of claim 52, where a knowledge pool is presented to a user through a presentation medium.

54. The OKSS of claim 52, wherein the presentation medium can be any one of the medium through which knowledge is disseminated to a user such as audio, video, visual indicators, text, social media messages, SMS, MMS, machine to machine interfaces, brain-machine interfaces etc.

55. The OKSS of claim 53, wherein the knowledge pool is presented to the user using a knowledge presentation policy.

56. The OKSS of claim 55, wherein the knowledge presentation policy is a sequence of processes used in making the user learn the provided knowledge pool, like allocating computing and network resources used to present the knowledge pool to the user, selecting the natural language of presentation, selecting the mode of presentation such as audio, video, text, visual indications, SMS, MMS, social media messages, machine to machine messages, machine to animal messages or brain-machine interface or any combination thereof, selecting right knowledge pool, selecting the time of presentation of the knowledge pool and all other processes required to disseminate the required knowledge pool to the user in the most appropriate way.

57. The OKSS of claim 2, wherein the information and metrics collected in the form of computer readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218) in various format include details of the user such as name, age, gender, family details, details of friends and relatives, location, educational qualifications, subjects of interest, skills, professional qualifications, profession or job, income, online activities, details from IoT devices around the user, details from social media activities, details from intelligent profiling devices etc, if the user is a human (100).

58. The OKSS of claim 2, wherein the information and metrics collected in the form of computer readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218) in various formats include machine centric information such as make, year of manufacture, country of manufacture, details regarding humans and machines used to design the intelligent machine (101), details regarding humans and machines used to manufacture the intelligent machine (101), processing power, throughput, operating systems, memory capacity, technologies used, operating environment, functional specifications, mechanical capabilities, field of operation, artificial intelligence level, learning capabilities, capabilities related to movement, safety and security standards, environmental standards etc., if the user is an intelligent machine (101).

59. The OKSS of claim 2, wherein the information and metrics collected in the form of computer readable data (206, 207, 208, 209, 210, 211, 212, 213, 214, 216, 217, 218] in various formats include information such as such as class of the animal (102) such as mammal, reptiles, etc., animal type such as cow, goat etc., age of the animal, gender, any given name, any given code name, intelligence level, location of living, foods eaten, skills such as bomb detection, emergency rescue etc, any assigned work such as ploughing, housekeeping etc., location of work, topics of interest etc., if the user is an animal (102) connected with an electronic system.

60. The OKSS of claim 41 or 53, wherein a feedback unit (109) is used to process feedback from a user regarding the presented knowledge pool or presented information regarding a service and to further communicate the processed feedback information to to update the knowledge pool or modify the knowledge presentation policy or update the information regarding a service.

61. The OKSS of claim 41 or 53, wherein a non-user can be presented with a knowledge pool or with information regarding a service through a user, if the non-user is directly or indirectly related to the user.

62. The OKSS of claim 60, wherein the feedback from a non-user is accessed through a user, provided the user is related to the non-user.

63. The OKSS of claim 1, wherein the OKSS can prioritize requirements of the user based on real interest and hobby of the user, wherein the real interest will have a higher priority compared to the hobby.

64. The OKSS of claim 1, wherein a user can manually request for knowledge from the OKSS.

65. The OKSS of claim 64, wherein the OKSS to provide a knowledge hunting engine to manually search for knowledge.