WO2013056086A1

WO2013056086A1 - Reverse-auction methodology with triggered social network analysis

Info

Publication number: WO2013056086A1
Application number: PCT/US2012/060012
Authority: WO
Inventors: Mohamed Falilu JALLOH
Original assignee: Jalloh Mohamed Falilu
Priority date: 2011-10-13
Filing date: 2012-10-12
Publication date: 2013-04-18

Abstract

In accordance with the principles of the present invention, a computer-implemented method for facilitating the sale of an item between a buyer and at least one seller is provided. Information on a good or a service is received from a buyer. The buyer designates the flexibility of the buyer and information from a social network. The information on the good or service is broadcasted to at least one seller. Without the seller knowing how much the buyer is willing to spend, the identity of the buyer, identities of competing sellers, and how much other sellers are offering for the item, the seller makes an offer. A relative-fidelity score is assessed and a relative-price score is determined. Information from the social network is analyzed with respect to the buyer and the seller, and the results are communicated to the buyer.

Description

REVERSE-AUCTION METHODOLOGY WITH TRIGGERED SOCIAL NETWORK

ANALYSIS

RELATED APPLICATION

[0001] This application claims priority from U.S. Provisional Patent Application 61/627,531 filed 13 October 2011.

FIELD OF THE INVENTION

[0002] The present invention relates to reverse auctions integrated with social network analysis, and to the electronic processing of such reverse auctions.

BACKGROUND OF THE INVENTION

[0003] Over the years, technologies connecting buyers and sellers continue to morph as more consumers opt to purchase goods and services on the Internet due to the ease of browsing desired goods and services, availability of discounted offers from sellers, and the enhanced level of interactivity provided by Web-based computing platforms.

[0004] Web-based auctions emerged as the popularity of the Internet grew in the past few decades. As an alternative to traditional auctions, reverse auctions also become popularized. There are various methods, processes, and models for conducting buyer-initiated reverse auctions. Since the late 1990s and the early 2000s, reverse auctions have become increasingly popular due to myriad advancements in Internet-based technologies and computerized systems used to facilitate such auctions.

[0005] In 1998, Priceline.com, 800 Connecticut Avenue, Norwalk, Connecticut 06854 launched a Website that connects buyers and sellers using a reverse auction methodology that allows users to submit a request for an airline ticket for instance, set a specific amount they are willing to pay for the flight, and receive binding offers from competing airlines.

[0006] Variations of reverse auctions or reverse biddings continue to see a rise in popularity. For instance in 2011, Carwoo, Inc., 1212 Rollins Road, 2nd Floor, Burlingame, California 94010 developed a Web-based platform that facilitates the automobile buying process by providing an anonymous communication infrastructure for buyers shopping for an automobile to receive offers from multiple auto dealerships. NakedApartments, Inc., 544 3rd Street, New York, New York 11215 launched a Web-based platform in 2010 that enables prospective renters to conduct a "reverse search" for an apartment - a buyer provides a description of a desired apartment, which gets broadcasted to real-estate agents, and the renter receives a range of offers from the real-estate agents.

[0007] While the prior art takes on a number of variations, it could be broadly categorized into the following types of reverse auctions: traditional reverse auction and sealed-bid reverse auction. In a traditional reverse auction, multiple sellers respond to a buyer's request for good(s) and/service(s). Once a starting bid has been established, participating sellers could offer amounts lower than the current bid. The downward bidding process continues for a set period of time, and the seller with the lowest bid would be considered the winner. In most cases, the buyer must accept the good(s) and/or service(s) of the lowest-bid seller. In other cases, the buyer has the discretion to select any bidder from the pool of participating sellers. A derivative of the traditional reverse auction reveals a semi-blind reverse auction that includes multiple rounds of bidding among competing sellers, wherein seller are able to view the bid amounts of competing sellers at the end of each round of bidding, and the buyer is provided with the details of each bid at the end of each round of bidding.

[0008] In some other variations of reverse auctions, the buyer may reject all the offers made, and the sellers may or may not be given a chance to enter iterative rounds of bidding to win the buyer's business. In the traditional form of reverse auctions, the sellers are aware of the current lowest bid (current winning bid), and the sellers know exactly the amount by which they need to under-bid the current lowest bid in order to become the best offer. Also, the sellers may have some knowledge of an ideal amount that the buyer is hoping to spend on the specified good(s) and service(s). Furthermore, the buyer would be aware of the current biddings (identify of the sellers and/or price offers) as the process transpires over a set period of time.

[0009] In a sealed-bid reverse auction, however, the sellers offer "sealed bids" in response to a single buyer's request for good(s) and/or service(s) - thereby providing limited blinding of the sellers such that the sellers are not aware of the details of each other's bids and/or offers. The bidding may occur in a single or multiple rounds. Once all sealed-bids have been submitted, the seller with the lowest bid amount wins the buyer's business. In both versions of the reverse auction method, a common feature is that there are multiple sellers vying to win a prospective buyer's business by providing competitive offers - with sealed bids providing a limited blinding environment amongst sellers. Compared to traditional auctions (also known as English auctions or open ascending price auctions), reverse auctions facilitate downward bidding among multiple sellers in an attempt to create market conditions that are favorable to the buyer. The auctioneer may set a starting amount.

[00010] In the prior art, when sellers reduce their prices as a reaction to another seller's current best offer, they are simply seeking to underbid the best offer by the smallest allowable amount needed to gain the top rank in the auction. Traditional reverse auctions provide processes and methods that essentially empower the sellers with the collective bargaining power to decide what they desire the lowest price to be, an inaccurate proxy of true market values since the sellers are disproportionately driving the market conditions. Therefore, in a lucid indirect fashion, such collective bargaining power end up disempowering the buyer in a process/methodology that is hypothesized to favor the buyer by stimulating true market values.

[00011] Unlike traditional reverse auctions, sealed-bid reverse auctions attempt to address a sellers' ability to inflate market prices by providing a market environment where the sellers are unaware of other sellers' offers for requested good(s) and/or service(s). In sealed-bid reverse auctions, however, the buyer is usually aware of the seller(s) identity - thereby resulting in a one-sided, blinded market environment. As such, the buyer is able to introduce biases in the market environment - either advantaging or disadvantaging one or more sellers over their competitors responding to the same request for bids/offers/proposals.

[00012] In variations of sealed-bid reverse auctions, an auctioneer(s) may also be aware of all or some of the details of the bids submitted by the sellers or specifications provided by the buyer - another limiting factor to a bias-neutral market environment.

[00013] Instead of having sellers collectively bargain amongst themselves to determine the lowest market price and thereby ensuring robust profitability on their end, what would be desirable would be a solution that inherently incents a seller to first compete with him/her/itself, and then given a subsequent single opportunity to enhance the competitiveness of their offer. Sellers usually know what is the lowest profit margin they could afford on a good or service in order to remain a solvent business.

[00014] Another limitation of the prior art is the ability to provide an integrated, computer- implemented infrastructure of conducting a structured social network analysis among buyers, sellers, and individuals within buyers' and sellers' social networks as a function of a seller's credibility in a blinded reverse auction. [00015] The array of limitations of the prior art described above result in inefficiencies and biases that destabilize market conditions and values that tend to disfavor both buyers and sellers. What is desired are systems, processes, and methodologies that provide optimal objectivity in the market place and produce reliable outcomes that enhance the buyer-seller relationship, yielding efficiency in conducting a buyer initiated reverse auction.

SUMMARY OF THE INVENTION

[00016] The present invention provides a solution that inherently incentives a seller to compete with him/her/itself. In accordance with the principles of the present invention, an objective, triple-blinded, reverse-auction methodology is provided. In accordance with the principles of the present invention, a computer-implemented method for facilitating the sale of an item between a buyer and at least one seller is provided. Information on a good or a service is received from a buyer. The buyer designates the flexibility of the buyer and information from a social network. The information on the good or service is broadcasted to at least one seller. Without the seller knowing how much the buyer is willing to spend, the identity of the buyer, identities of competing sellers, and how much other sellers are offering for the item, the seller makes an offer. A relative-fidelity score is assessed and a relative-price score is determined. Information from the social network is analyzed with respect to the buyer and the seller, and the results are communicated to the buyer.

BRIEF DESCRIPTION OF THE DRAWING

[00017] Figure 1 is a flow-chart setting forth an example process for an objective, triple- blinded, reverse-auction methodology in accordance with the principles of the present invention.

[00018] Figure 2 is a flow-chart and diagram setting forth an example process, system, and method for continuously passing multiple variables to a relational database from a webserver in accordance with the principles of the present invention.

[00019] Figure 3 is a diagram that depicts a relationship between a buyer and sellers as well as individuals within the buyer's and seller's respective social networks in accordance with the principles of the present invention.

[00020]Figure 4 is a non-limiting example of a hardware infrastructure that can be used to run a system that implements an objective, triple-blinded, reverse-auction methodology of the present invention. DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

[00021] In accordance with the principles of the present invention, an objective, triple -blinded, reverse auction methodology using social networks to produce market intelligence is provided. The objective, triple-blinded, reverse-auction methodology using social networks to produce market intelligence of the present invention is an improvement and transformation of conducting reverse auctions using computerized infrastructures, networks, and mechanisms. Not only does the present invention provide an enhancement to the prior art, an objective, triple-blinded, reverse-auction methodology using social networks to produce market intelligence of the present invention further employs and connects novel methods, systems, and components that work in concert to help a buyer identify and purchase desired good(s) and/or service(s) at an optimal cost that is aligned with market values of such good(s) and/or service(s).

[00022] Via the objective, triple-blinded, reverse-auction methodology of the present invention, the participating parties are blinded; including the buyer, the seller, and the auctioneer.

[00023] Specifically, the sellers are blinded to the amount the buyer may be willing to spend, and the sellers are also blinded to other offers being made by their competitors. Until the end of biddings, sellers are blinded to their rank in the competition and those of their competitors. In order to eliminate intended or unintended buyer-induced biases that are likely to favor/disadvantage one seller over others, the buyer is blinded to the identity and offers of the sellers. Following the same rationale, the auctioneer or "host" is also blinded to the identity and offers of the sellers.

[00024] Computer technologies, including web-based technologies, are used to facilitate and enable the blinding of the aforementioned parties involved in the reverse auction. While just one feature of the present invention, the triple-blinded nature provides grounding for optimal objectivity in the market, and sets the stage for the subsequent methods, process, systems, and components of the present invention to operate in an exemplary embodiment that produce an efficient, reliable, and stabilized outcome in the market place.

[00025] The objective, triple-blinded, reverse-auction methodology of the present invention creates a blinded process that incentivizes sellers to offer the lowest possible price at the highest fidelity level to the buyer's request. The objective, triple-blinded, reverse-auction methodology of the present invention is an enhanced solution that favors the buyer by creating an environment where sellers are incentivized to submit offers that are aligned with market values, thereby avoiding seller's propensity to inflate prices and rates in order to maximize profits. The objective, triple-blinded, reverse-auction methodology of the present invention provides a neutral environment that allows for the ultimate objectivity and stability in the marketplace using efficient methods, processes, systems, and components that interact to provide a buyer with aligned good(s) and/ service(s) at a market value while producing market intelligence.

[00026] The objective, triple-blinded, reverse-auction methodology of the present invention is a method and system for purchasing goods and/or services via a computerized infrastructure, such as for example the Internet or other web-based systems. A plurality of sellers provide tailored offers in response to a buyer's request and level of flexibility in a triple-blinded market environment that is grounded to be optimally objective, stable, and reliable. The objective, triple-blinded, reverse-auction methodology of the present invention creates market conditions that favor the buyer by ensuring that the buyer receives the best possible goods and or services that are closely aligned to the buyer's identified priorities at a market price among competing sellers.

[00027] The objective, triple-blinded, reverse-auction methodology of the present invention ensures that sellers provide their best offers - in terms of closely matching and aligning with a buyer's request while offering the lowest possible price - in order to gain a competitive advantage over other participating sellers. When multiple sellers only get a blinded opportunity to present a competitive offer, the buyer ends up with the best possible matched good(s) and/or service(s) at the lowest possible price on the market of competing sellers in an efficient, objective, and reliable manner.

[00028] The sellers would not know how much a buyer might be willing to spend, nor would the sellers be aware of other offers being made by participating sellers. As a result, the market environment is incentivized to produce the best possible match(es) to the buyer's request at the lowest possible price.

[00029] Furthermore, until offers are submitted, processed, and made final, the buyer is blinded to the identity of the participating sellers as well as the offers made by them. Additionally, once the buyer submits a request, the buyer is unable to influence the offers that are being submitted or create market biases that favor or disadvantage a prospective seller(s) over their competitors.

[00030] Once the sellers' offers are submitted, the sellers' offers are analyzed and assessed in a double-staged procedure. A reliable, relative-fidelity score is assessed to gauge the alignment of each seller's offer with the buyer's request. A relative-price score is also assessed to gauge how competitive a seller's price is in relation to other participating sellers and known market prices.

[00031] In an exemplary embodiment of the invention, sellers could be delineated into quadrants based on an assessment of their respective combined scores, such as for example: (Ql) high fidelity - low price; (Q2) high fidelity - high price; (Q3) low fidelity- low price; (Q4) low fidelity - high price. Sellers with the most competitive mix of match (fidelity) and price are invited to a second phase while the least competitive sellers are dropped at this phase. In the second phase, sellers get a single opportunity to reduce (or not reduce) their prices in a tripled-blinded environment as an attempt to enhance their final ranking.

[00032] A social network analysis module is implemented to assess past relationships between a buyer and a seller in order to adjust the final ranking of the seller in the reverse auction. The module will further assess prior interactions between the seller and individuals within the buyer's social network. The seller's final ranking is correlated with the results of the quantitative evaluation of past interaction with the buyer and/or individuals within the buyer's social network such that, for example, the more positive the quantitative evaluation results, the higher the number of points attributed to the final weighted rank of the seller.

[00033] The buyer may then select a preferred seller(s) from the list of top ranked offer(s). If a preferred seller is selected, the buyer pays a service fee to the "host" in order to receive the contact information of the seller. From this point onward, the buyer and seller will directly communicate in order to finalize the purchase of the desired good(s) and/or service(s).

[00034] In more detail, referring to Figures 1-4 flow-charts, diagrams, and visual depictions setting forth an example process for an objective, triple-blinded, reverse-auction methodology in accordance with the principles of the present invention are seen. The numbering of the steps is meant to provide a preferred example embodiment of how to easily communicate the order of the processes, actions, systems, and other components of the present invention. For example, in another embodiment of the invention, steps 1 and 2 could be combined and re- organized and renamed to "Step A" while the subsequent steps could also be regrouped in the order of procession (Steps B, C, D...) that stays within an embodiment of the present invention.

Step 1 A: User Registration for Buyers & Sellers

[00035] In more detail, referring to Figure 1A buyers and sellers are provided a computerized infrastructure - such as a Web site or mobile computing platform over a network of servers and other electronic devices - that facilitates a reverse auction. The system enables a plurality of sellers to respond to a buyer's request for specified good(s) and or service(s).

[00036] In order to submit a request to initiate the reverse auction, a buyer registers to use the systems and methods of the present invention by providing some basic information including, for example: name, email address, password address, phone number. The buyer provides a password that will be used to securely access the reverse auction systems and methods of the present invention. In one exemplary embodiment, HyperText Markup Language (HTML) form elements may be used to capture the aforementioned data, including, for example: radio buttons, reset buttons, submit buttons, checkboxes, dropdown lists, file uploaders, text boxes, text areas, etc. In order to enhance the end-user experience, a graphical user interface (GUI) may strategically leverage technologies such as HTML, Cascading Style Sheets (CSS), Asynchronous JavaScript (AJAX), JavaScript, jQuery, Personalized Home Page (PHP), Extensible Markup Language (XML), JavaScript Object Notation (JSON), Ruby, Perl, Python, etc.

[00037] The registered buyer's information is then stored as a unique record in a relational database, such as Structured Query Language (SQL), MySQL, the relational database management system (RDBMS) available from ORACLE Corporation, 500 Oracle Parkway, Redwood Shores, California 94065. Pulling from data in the relational database, a user profile will then be generated for the buyer. Once a user profile has been generated, a buyer is then able to submit a request for good(s) and/or service(s) for a plurality of sellers to respond to accordingly.

[00038] Sellers likewise register to use the systems and methods of the present invention by providing some basic information including, for example: company name, Employer Identification Number (EIN), email address, address, and phone number, primary contact person, role of primary contact person (e.g., manager, coordinator, etc.). The seller provides a password that will be used to securely access the reverse auction systems and methods of the present invention. In one exemplary embodiment, HTML form elements may be used to capture the aforementioned data, including but not limited to: radio buttons, reset buttons, submit buttons, checkboxes, dropdown lists, file up loaders, text boxes, text areas, ETC. In order to enhance the end-user experience, a GUI may strategically leverage technologies such as HTML, CSS, AJAX, JavaScript, j Query, PHP, XML, JSON, Ruby, Perl, Python, ETC.

[00039] The registered seller's information is then stored as a unique record in a relational database, such as SQL, MySQL, ORACLE'S RDBMS. Pulling from data in the relational database, a user profile is generated for the seller. Once a user profile has been generated, a seller is then able to respond to buyers' requests for good(s) and/or service(s) using the systems and methods of the present invention.

Step IB: Buyer Inputs Specifications

[00040] In more detail, referring to Figure 1A a buyer provides a set of specification(s) of desired good(s) and/or service(s) using the systems and methods of the present invention. For instance, when the present invention is implemented in one embodiment to facilitate apartment rental between a potential tenant (buyer) and a plurality of landlords (sellers), the buyer may indicate information regarding the desired apartment, such as for example: city; state; zip; number of bedrooms; number of bathrooms; preference for hardwood floors in bedrooms, living areas, kitchen, or other parts of the apartment; preference for carpet in rooms, living areas, kitchen, or other parts of the apartment; preference for a dishwasher; preference for laundry washer & dryer; proximity to public transportation; proximity to grocery stores, proximity to recreational park, etc.

[00041] In one exemplary embodiment, HTML form elements may be used to capture the aforementioned data regarding the desired apartment including but not limited to: radio buttons, reset buttons, submit buttons, checkboxes, dropdown lists, file uploaders, text boxes, text areas, etc. In order to enhance the end-user experience, a GUI may strategically leverage technologies such as HTML, CSS, AJAX, JavaScript, j Query, PHP, XML, JSON, Ruby, Perl, Python, etc. The captured data would then be stored in a relational database, such as SQL, MySQL, ORACLE'S RDBMS, and/or any other suitable relational database system. Step 2: Buyer Indicates Levels of Flexibility

[00042] In order to enable the sellers to provide tailored offers, the buyer can indicate the level of flexibility on each specification provided regarding the desired good(s) and service(s) indicated in Step 1. One measure of flexibility could include operationalized but not limited to a scale such as, for example: (a) not flexible, (b) somewhat flexible, and (c) very flexible. While such scale is an example of how to assess the buyer's levels of flexibility, other measures and scales of flexibility could be utilized at this phase in other embodiments of the systems and methods of the present invention. The buyer's request (specifications and flexibility levels), in an exemplary embodiment, could be stored and processed via a computerized infrastructure and sent out to a plurality of participating sellers. Within the database, a relationship is then created between a given buyer's specification and indicated level of flexibility associated with that particular specification.

Step 3 : Buyer Provides Referees for Social Network Analysis

[00043] The buyer provides an array of individuals within the buyer's social network whose opinions are valued by the buyer. For instance, in one exemplary embodiment of the present invention, the buyer may provide a list of email addresses of individuals in the buyer's social network including, for example, family, relatives, friends, acquaintances, colleagues, etc.

[00044] The list of email addresses of individuals in the buyer's social network would then be used to conduct a structured social network analysis. The list of email addresses are structurally queried within the database of the present invention in order to assess past relationships between a seller and an array of individuals within a buyer's social network.

Step 4A: Buyer's Request Broadcasted to Sellers

[00045] Sellers that are registered to participate in the systems and methods of the present invention would be able to securely gain access to the pending request submitted by a buyer. A description of the buyer's specifications and indicated levels of flexibility would be presented in a user-friendly manner to the sellers. A mix of enhanced, computerized graphical interfaces, systems, and technologies would be utilized in presenting the sellers with a description of the buyer's request - thereby enabling the sellers to comprehend the request and be well positioned to submit their best possible offer to the buyer. Step 4B: Sellers Respond to Buyer's Request

[00046] Without knowing how much the buyer is willing to spend, the buyer's identity, competing sellers' identities, and how much other sellers are offering for the good(s) and service(s), each seller submits a calculated and competitive offer to the buyer using the systems and methods of the present invention. Sellers are incentivized to present the best possible offer in order to gain a competitive advantage over other participating sellers. The competing sellers may even be willing to minimize some of their profit margins on their goods and/or services in order to improve their odds of moving to the next phase of the reverse auction of the present invention. In submitting an offer, each seller would independently respond to the specifications indicated on a buyer's request for good(s) and/or service(s).

[00047] In one exemplary embodiment, HTML form elements may be used to capture the aforementioned data from the sellers, including but not limited to: radio buttons, reset buttons, submit buttons, checkboxes, dropdown lists, file uploaders, text boxes, text areas, etc. In order to enhance the end-user experience, a GUI may strategically leverage technologies such as HTML, CSS, AJAX, JavaScript, j Query, PHP, XML, JSON, Ruby, Perl, Python, etc.

Step 5 : Assessment of a Relative-Fidelity Score

[00048] Based on the sellers' offers, a computerized infrastructure - including but not limited to scripting technologies, programmatic engines, Web servers, relational database systems - analyzes the specifications of the offers and subsequently assesses a relative-fidelity score. The relative-fidelity score is obtained by comparing the specifications of each seller's offer with the request made by a buyer.

[00049] The systems and methods of the present invention present an objective means of assessing the alignment of each seller's offer in relation to the request submitted by a buyer. To provide transparency and equal opportunity in the market place, sellers could be given a full description of the method used in determining the relative-fidelity score so that they could galvanize and leverage their full-scaled resources in order to ensure that they put forth their best possible offers to the buyer.

[00050] In one embodiment of the present invention for example, each seller would be awarded (or not awarded) points based on fidelity to the buyer's specification. For example, a seller could earn a single point for each matched specification in the buyer's submitted request. Furthermore, the seller's accrued point could be moderated based on the buyer's indicated level of flexibility on the specification. For instance, if a buyer desires a four-bedroom apartment, and a seller offers a four-bedroom apartment, the seller gains one point for matching the buyer's specification on the number of desired bedrooms in the apartment. If the buyer had indicated that the buyer is not flexible on the specified number of bedrooms, (on a 3-point scale of: very flexible (x lpoint), somewhat flexible (x 2 points), not flexible (x 3points)) the seller's accrued point of 1 is then multiplied by 3 - bringing the seller's weighted accrued point to 3 instead of 1.

[00051] The seller could be rewarded for matching a desired specification on which the buyer is not flexible. The same point accrual process could be applied to all specifications and indicated levels of flexibility submitted by the buyer. So in a scenario where a buyer submits 10 desired goods and/or services, a seller matching all 10 specifications could initially accumulate a total of 10 points, which would then be multiplied by up to 3 if the buyer had indicated not being flexible on all 10 submitted specifications in the request. In such scenario, a seller matching all 10 specifications in the request would accrue a weighted fidelity score of 30 (3 per each non-flexible, matched buyer specification).

[00052] Once a weighted fidelity score has been determined for each seller responding to a buyer's request, an average/mean score would then be calculated for the pool of competing sellers. Using computerized programmatic technologies, each seller's weighted fidelity score would be analyzed in comparison to the average/mean score for the pool of competing sellers. Sellers with weighted fidelity scores above the average/mean can be delineated into a "high-match" quadrant while sellers with weighted fidelity scores at or below the average/mean can be delineated into a "low-match" quadrant.

Step 6: Determining a Relative -Price Score

[00053] A relative-price score is assessed for each participating seller in relation to the average/mean price of competing sellers as well in comparison to other known market prices. Therefore, the lower an offered price is from the average/mean price and other known prices for a given good or service, the higher a seller's weighted relative-price score.

[00054] No one particular formula is inherently required or essential to the invention; however, using the systems and methods of the present invention a pre-defined formula or algorithm is used in assessing seller's relative-price scores. The formula used may be modified or tweaked over a period of time in order to ensure its optimal objectivity and reliability. Sellers could be informed accordingly of the pre-defined formula used to assess the relative -price sore. Such openness and transparency would maximize sellers' confidence and self-efficacy in competing in the reverse auction system and methodology of the present invention.

[00055] Sellers with weighted relative-price scores above the average/mean can be delineated into a "high-price" quadrant while sellers with weighted relative-price scores at or below the average/mean can be delineated into a "low-price" quadrant.

Step 7A: Formulation of the Match - Price Matrix

[00056] The sellers' relative-fidelity and relative -price scores would influence their overall relative competitiveness. Meaning, sellers can be delineated into quadrants, such as for example: (Ql) high fidelity - low price; (Q2) high fidelity- high price; (Q3) low fidelity- low price; (Q4) low fidelity - high price.

[00057] A seller whose offer is most aligned with the buyer's request while maintaining the lowest price could be considered most competitive in relation to other participating sellers; conversely, a seller whose offer is least aligned with the buyer's request while maintaining the highest price could be considered least competitive. Other offers would fall somewhere between these two opposite ranges. In one embodiment of the present invention for example, the least competitive offers are eliminated, while the remaining sellers advance to the next stage of the reverse auction of the present invention.

[00058] Referring now to Figure IB, as a reward for being competitive and making it to the next phase of the reverse auction, sellers could then be provided with aggregated, summative, and non-identifying information regarding the pool of competitors. In one embodiment of the present invention, sellers are provided with the mean/average price submitted by the pool of participating sellers at this phase. Sellers may opt to reduce (or not reduce) their prices in order to influence their final ranking in the reverse auction; however, a reduction in price does not guarantee an improved standing in the competition since the final rank is a function of several other weighted factors; not price alone. The buyer and sellers would be blinded to the price adjustments. Step 7B: Triggered Social Network Analysis

[00059] Referring also to Figure 3, a social network analysis module is triggered to assess past relationships between a buyer and a seller in order to adjust the final ranking of the seller in the reverse auction. Queries can include, for example: Has the buyer purchased from the seller before? If so, what was the buyer's evaluation of the good(s) and/or service(s) received from the seller? Have other individuals within the buyer's social network (such as family members, friends, and colleagues) previously purchased good(s) and/or service(s) from the seller?

[00060] The list of email addresses of individuals in the buyer's social network serves as the basis for conducting a structured social network analysis. The list of email addresses are used to conduct a structured query of the database in order to assess past relationships between a seller and individuals within a buyer's social network.

[00061] If so, queries can include, for example: What was their respective evaluations of the good(s) and/or service(s) received from the seller? Past evaluations of the quality and frequency of interactions among the buyer, individuals within the buyer's social network as well the seller and other affiliated sellers within seller's social network are analyzed in order to adjust a seller's ranking in the reverse auction. For example, a seller who has previously provided numerous services to a buyer or someone within the buyer's social network and received positive reviews (e.g. score of 95/100) could have a weighted adjusted ranking in the reverse auction that is higher in comparison to another competing seller who has not.

[00062] If the seller has not previously interacted with a buyer, the module can trigger another level of social network analysis. The module will assess other prior interactions between the seller and individuals within the buyer's social network. In the event that a seller has had prior interactions with individuals within the buyer's network, quantitative evaluations from such interactions will inform the adjustment of the seller's ranking in the reverse auction. Consequently, the seller's final ranking is directly correlated with the results of the quantitative evaluation of past interaction with the buyer and/or individuals within the buyer's social network such that the more positive the quantitative evaluation results, the higher the number of points attributed to the final weighted rank of the seller. Step 8A1 : Buyer Reviews Offers

[00063] Referring to Figure IB, the top ranked seller(s) are presented to the buyer for a review and selection of a preferred seller. For instance, a cut-off could be set to limit the "top ranked" sellers to those with the three highest weighted combined score.

[00064] After the offers have been made final, the buyer is then notified of the top offer(s). For each of the top selected sellers, the buyer is provided with a detailed comprehensive report of the top offer(s) including, for example:

• Relative -Fidelity Score Analysis o Factors that positively impacted the score o Factors that negatively impacted the score

• Relative -Price Score Analysis o Factors that positively impacted the score o Factors that negatively impacted the score Step 8A2: Buyer Selects Preferred Seller

[00065] After reviewing the top offer(s), the buyer makes a final decision and chooses his/her/its preferred seller.

Step 8 A3 : Good Faith Demonstration

[00066] In one embodiment of the present invention, the buyer and/or the seller would pay a service fee in order to be provided with each other's contact information. Receipt of a service fee serves as a good faith demonstration. The selected preferred seller is bounded to honor his/hers/its offer to the buyer - as specified in the terms and conditions in the service agreement agreed upon by both the buyer and seller prior to participating in the reverse auction systems and methodology of the present invention.

Step 8A4: Selected Seller Notified

[00067] The selected seller is likewise notified that they have been chosen as the winning offer. Other non-selected sellers are provided with summarized, aggregated data of the winning offer (excluding the identity of the buyer or selected seller) as a reward of their participation in the reverse auction system and methodology of the present invention. Step 8A5: Direct Buyer-Seller Contact

[00068] At the end of the bidding, the buyer and seller will be in direct contact and negotiations without the involvement of the service provider/host; however, both the buyer and seller are bounded by the terms and conditions agreed upon prior to participating in the reverse auction system and methodology of the present invention.

Step 8B1 : Process, Transfer, and Store Buyer and Seller Related Information

[00069] In an exemplary embodiment of the invention, the requests and offers submitted by sellers and buyers are continuously transmitted to various types of variables to be stored in distinct fields and records within a relational database associated with the systems, methods, and processes of the present invention.

Step 8B2: Streamline Codes & Assign Values to Variables in a Relational Database

[00070] Referring also to Figure 2, the variables are defined consistently across buyers and sellers. Each variable can store multiple corresponding data values and codes. An indefinite number of codes and values could be appended to the variables.

Step 8B3: Assess Generalizability

[00071] As the number of unique records increase over a period of time, a large, representative sample is attained for each product/good/service, which could enable the cumulative dataset to provide the basis for robust data analyses that are empirically reliable, valid, and generalizable beyond the specific samples to make statistically significant inferences on the goods, products, and systems.

Step 8B4: Produce Objective Market Intelligence

[00072] When the steps outlined above are implemented in a triple-blinded market - of a single buyer and multiple sellers - the outcome is an invention that stimulates optimal objectivity, reliability, and stability, in purchasing good(s) and service(s) using a computerized infrastructure. The methods, processes, and systems of the objective, triple-blinded, reverse- auction methodology of the present invention work in concert to produce an efficient, market- based solution that favors the needs and priorities of the buyer at a market value while producing objective market intelligence from which sellers and buyers could benefit.

[00073] For instance, the empirical dataset established via the systems and methodologies of the present invention could predict the market value of various good(s) and/ service(s) that have aggregated a large enough sample size within the relational database. In addition, the systems and methodologies of the present invention could determine how various attributes of the good(s) and/or service(s) affect the value of the good(s) and/or service(s). For example, the systems and methodologies of the present invention could determine which attributes significantly increased the value of the good(s) and/or service(s), and which ones did not. In an exemplary embodiment, a product intelligence center of the present invention could provide empirical analyses using an interactive user interface to address such question. The intelligence generated from the systems and methods of the present invention may shift and adjust current inflated prices to market values.

[00074] Referring to Figure 4, a non-limiting example of a high-level hardware implementation can used to run a system of the present invention is seen. The infrastructure should include but not be limited to: wide area network connectivity, local area network connectivity, appropriate network switches and routers, electrical power (backup power), storage area network hardware, server-class computing hardware, and an operating system such as for example Redhat Linux Enterprise AS Operating System available from Red Hat, Inc, 1801 Varsity Drive, Raleigh, North Carolina.

[ooo75].The methods, processes, and systems of the objective, triple-blinded, reverse-auction administrative applications software server can run for example on an HP ProLiant DL 360 G6 server with multiple Intel Xeon 5600 series processors with a processor base frequency of 3.33 GHz, up to 192 GB of RAM, 2 PCIE expansion slots, 1GB or 10GB network controllers, hot plug SFF SATA drives, and redundant power supplies, available from Hewlett-Packard, Inc, located at 3000 Hanover Street, Palo Alto, California. The database server can be run for example on a HP ProLiant DL 380 G6 server with multiple Intel Xeon 5600 series processors with a processor base frequency of 3.33 GHZ, up to 192 GB of RAM, 6 PCIE expansion slots, 16 SFF SATA drive bays, an integrated P410i integrated storage controller, and redundant power supply, available from Hewlett-Packard.

[00076]. While the invention has been described with specific embodiments, other alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, it will be intended to include all such alternatives, modifications and variations set forth within the spirit and scope of the appended claims.

Claims

What is claimed is:

1. A computer-implemented method for facilitating the sale of an item between a buyer and at least one seller comprising:

into a memory, receiving information from a buyer on an item;

into a memory, receiving information from a buyer on the flexibility of the buyer with respect to an item;

into a memory, receiving information from a buyer about a social network;

from a processor in communication with the memory, broadcasting information regarding a buyer request to buy to at least one seller;

into a memory and without the seller knowing how much the buyer is willing to spend, the identity of the buyer, identities of competing sellers, and how much other sellers are offering for the item, receiving an offer from a seller;

with a processor in communication with the memory, assessing a relative-fidelity score;

with a processor in communication with the memory, determining a relative-price score;

with a processor in communication with the memory, analyzing the information from a social network with respect to the buyer and the seller; and

communicating to the buyer regarding offers to buy the item;

2. The computer-implemented method of claim 2 further comprising receiving information from a seller a scale of the flexibility of the seller.

3. The computer-implemented method of claim 1 further comprising receiving an identity from a buyer of an array of individuals within a social network.

4. The computer-implemented method of claim 3 further comprising conducting a structured social network analysis of the array of individuals within a social network.

5. The computer-implemented method of claim 4 further comprising structurally querying in order to assess past relationships between a seller and the array of individuals within a social network.

6. The computer-implemented method of claim 1 further comprising assessing a relative-fidelity score by comparing the specifications of each offer of a seller with the request made by a buyer.

7. The computer-implemented method of claim 6 further comprising assessing a relative-fidelity score by awarding a seller points based on fidelity to the specification of the buyer.

8. The computer-implemented method of claim 6 further comprising assessing a relative-fidelity score by moderating an accrued point of a seller based on the indicated level of flexibility of the buyer on the specification.

9. The computer-implemented method of claim 1 further comprising calculating an average/mean score for a pool of competing sellers.

10. The computer-implemented method of claim 9 further comprising analyzing a fidelity score of a seller in comparison to the average/mean score for the pool of competing sellers.

11. The computer-implemented method of claim 9 further comprising determining a relative -price score for each seller.

12. The computer-implemented method of claim 1 further comprising formulating a match-price matrix.

13. The computer-implemented method of claim 12 further comprising

eliminating least competitive offers in the match-price matrix and advancing remaining sellers to a next stage.

14. The computer-implemented method of claim 1 further comprising conducting a structured social network analysis.

15. The computer-implemented method of claim 14 further comprising assessing past relationships between a buyer and a seller in the social network.

16. The computer-implemented method of claim 14 further comprising assessing prior interactions between a seller and individuals within a social network of a buyer.

17. The computer-implemented method of claim 1 further comprising adjusting a rank of a seller based on analyzing the information from a social network with respect to the buyer and the seller.

18. The computer-implemented method of claim 1 further comprising providing aggregated data to a seller and allowing a seller to adjust their price in order for a chance to improve overall rank.

19. The computer-implemented method of claim 1 further comprising presenting ranked sellers to the buyer for a review and selection of a preferred seller

20. The computer-implemented method of claim 1 further comprising presenting ranked sellers to the buyer for a review and selection of a preferred seller.

21. The computer-implemented method of claim 1 further comprising presenting ranked sellers to the buyer for a review and selection of a preferred seller. .

22. The computer-implemented method of claim 21 further comprising presenting relative-fidelity score analysis and relative-price score analysis for ranked sellers to the buyer for a review and selection of a preferred seller.

23. The computer-implemented method of claim 1 further comprising a buyer and/or a seller paying a service fee in order to be provided with each other's contact information.

24. The computer-implemented method of claim 1 further comprising providing non-selected sellers with summarized, aggregated data of the winning offer (excluding the identity of the buyer or selected seller).

25. The computer-implemented method of claim 1 further comprising the buyer and seller contacting and negotiating directly.

26. The computer-implemented method of claim 1 further comprising transmitting requests and offers submitted by sellers and buyers to various types of variables to be stored in distinct fields and records within a relational database.

27. The computer-implemented method of claim 1 further comprising transmitting requests and offers submitted by sellers and buyers to various types of variables to be stored in distinct fields and records within a relational database.

28. The computer-implemented method of claim 1 further comprising creating a database based on the sales of items between sellers and buyers and analyzing the database for market intelligence.

29. The computer-implemented method of claim 2 further comprising receiving information from a seller on a good.

30. The computer-implemented method of claim 2 further comprising receiving information from a seller on a service.