System for and Method of Determining Relative Value of a Product

Abstract

A system for and a method of determining relative value of a product. In accordance with an embodiment, a value is obtained that is representative of purchasing activity directed toward a product. This value is stored in a computer-readable storage medium. A value is obtained that is representative of non-purchasing activity that is directed toward the product. This value is also stored in computer-readable memory. The value representative of purchasing activity and the value representative of non-purchasing activity are combined and the result is displayed on a display screen. A result of the combining can be scaled and rounded so that the result is a whole number between 0 and 99. This number is referred to herein as a “Community Value” that reflects the relative value placed on the product by members of a community.

Description

This application claims the benefit of U.S. Provisional Application No. 61/370,771, filed Aug. 4, 2010, U.S. Provisional Application No. 61/374,518, filed Aug. 17, 2010, and U.S. Provisional Application No. 61/375,225, filed Aug. 19, 2010. The entire contents of each of these provisional applications are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates to a system for and a method of determining the relative value of a product. More particularly, the present invention relates to a system for and a method of determining the relative value of a product based on behavior of consumers of the product.

Throughout most of the history of commerce, the value of a product has always been an elusive concept to define. Prior techniques for determining the value of a product have typically focused on a single or narrow set of criteria, such as monetary value.

More recently, with the advent of electronic commerce, there are increasing amounts of information about products as well as increasing numbers of products offered. Therefore, there is a need for tools that assist in the evaluation and selection of products. More particularly, what is a needed is an improved technique for determining the value of a product relative to other products.

SUMMARY OF THE INVENTION

The present invention provides a system for and a method of determining relative value of a product. In accordance with an embodiment, a value is obtained that is representative of purchasing activity directed toward a product. This value is stored in a computer-readable storage medium. A value is obtained that is representative of non-purchasing activity that is directed toward the product. This value is also stored in computer-readable memory. The value representative of purchasing activity and the value representative of non-purchasing activity are combined and the result is displayed on a display screen. A result of the combining can be scaled and rounded so that the result is a whole number between 0 and 99. This number is referred to herein as a “Community Value” that reflects the relative value placed on the product by members of a community.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described with respect to particular exemplary embodiments thereof and reference is accordingly made to the drawings in which:

FIG. 1 illustrates a system that may be used to determine the relative value of a product in accordance with an embodiment of the present invention; and

FIG. 2 illustrates a flow diagram of a method for determining the relative value of a product in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

The present invention is directed toward a system for and a method of determining relative value of a product (referred to herein as “Community Value”). It is believed that the “true” value of a product can be better defined when the result is attained with a community focus. For example, a religious text normally has a low monetary value, while conversely is may have a very high value among members of a particular community. As explained more fully herein, the Community Value for a product preferably reflects non-purchasing, social activity directed toward the product (e.g., discussions, ratings, group associations, reviews, recommendations) as well as purchasing activity directed toward the product.

In an exemplary embodiment, the present invention can be implemented in the context of an electronic commerce system in which products are consumed by consumers accessing a website and purchasing and/or downloading the products. FIG. 1 illustrates a system 100 that may be used to determine the relative value of a product in accordance with an embodiment of the present invention. As shown in FIG. 1, a server 102 is communicatively coupled to a plurality of remote devices 104, 106, and 108 via a network 110. The a server 102 includes a processor 112, a database 114 and input/output devices 116, which may include a display, a keyboard, a mouse, and a network interface. The network 110 may include, for example, a local area network, an intranet, and/or a wide area network, such as the Internet. The remote devices 104, 106, 108, may be implemented as various computing devices, such a desktop or portable personal computer, a “smart” phone, e-book reader device, a PDA or other device. A user accesses the server 102 by using a remote device 104, 106 or 108. For example, the server 102 may host website portal which can be accessed by users of the devices 104, 106 or 108. The remote devices 104, 106, 108 may communicate with the server 102 and with each other by wireless or wired connections. While a single server 102 is shown, it will be understood that the functions of the server 102 may be performed by multiple servers or by a distributed server system.

In an embodiment, the products include digital content, such as a documents which may include text, video, images, audio and combinations thereof. For example, a document may include both text and images. The documents can be electronic books (also referred to as e-books). Users can access the products by downloading and/or viewing the products, and can perform other actions described herein, via a website which may be hosted by the server 102. The products can include other types of products, such as paperback and hardbound books. In this case, the products may be ordered via the website.

In addition to accessing the products, each user can engage in other interactions, including the following:

Each user preferably has a “library.” The library for a user includes a list of products that have been selected by the user (e.g., by purchasing the product or by indicating a preference for the product by adding it to his or her library). Moreover, users can join groups via the website and each group may have a library. Thus, the library for a user or a group reflects product preferences of the corresponding user or group. In a preferred embodiment, purchased products are automatically added to a user's library. However, users and groups can also manually add a product to their libraries without actually purchasing it. The libraries and identifications of their contents are preferably stored in computer-readable storage medium at the server 102.

Additionally, each user preferably has the ability and facility to apply a personally-determined rating to a product. This can be based on scale of one to five, where a higher number indicates a higher rating (one is the lowest rating and five is the highest). The ratings for a product can be averaged to determine a collective average rating of the product. The ratings for products, or at least a parameter that is representative of average of the ratings for each product, is preferably stored in computer-readable storage medium at the server 102.

Each individual user also preferably has the ability and facility to initiate or participate in on-line discussions with other users where the discussions are related to a particular product. The number of discussions that refer to product can be determined, for example, by a user that initiates a discussion indicating the particular product to which the discussion applies. Other users may then add to the discussion. The discussion threads and the number of discussions that refer to a particular product (or other information that is representative of discussion activity) are preferably stored in computer-readable storage medium at the server 102.

Each individual user preferably has the ability and facility to create or become a member of a group. Each group may have a corresponding group library. Group membership information, such as the number of groups that have a particular product in their library, is also preferably stored in computer-readable storage medium at the server 102.

Each individual user preferably also has the ability and facility to recommend a product to another individual user or to a group for addition to that user's or group's library. The recommendations and the number of times a particular product has been recommended (or other information that is representative of recommendation activity) are also preferably stored in computer-readable storage medium at the server 102. A recommendation may be in the form of a binary indication as to whether the user recommends the product.

Each individual user also preferably has the ability and facility to write a personal review of a product. The reviews and the number of reviews a product has received (or other information that is representative of review activity) is also preferably stored in computer-readable storage medium at the server 102. A review may be in the form of text that indicates one or more reasons why the user liked or disliked the product.

A relative value of a product can be determined from purchasing activity as well other user interactions. This information is used to assign a value to a product relative to other products. The determined value of a product is referred to herein as the “Community Value” of the product. The Community Value is a “weight” or “value” assigned to a product that can be used to show a user in a quick glance if the product is considered “good” by a community as a whole.

The Community Value for a particular product preferably takes in account two principle factors. The first factor is determined from purchasing activity directed toward the particular product. For example, this can be a number of times the particular product has been purchased by individuals within the community or by the number of individual's whose libraries than contain the product. The second factor is determined from non-purchasing activity directed at the product by the community. The community for a particular product (also referred to as “users”) may refer to a group consisting of all purchasers and potential purchasers of the product, or to a subset of such persons. For example, the community for a particular product may include all persons who have signed up at a website as members of the website's community or may include only a subset of these persons (e.g., only persons who have indicated a preference for a certain type of product or only persons who purchased a particular product or type of product in the past may be members of the community for a particular product).

FIG. 2 illustrates a flow diagram of a method 200 for determining the relative value of a product in accordance with an embodiment of the present invention. As shown in FIG. 2, in a step 202, a value is obtained that is representative of purchasing activity directed toward a product. This value can stored in a computer-readable storage medium, such as in the server 102 (FIG. 1). In another step 204, a value is obtained that is representative of non-purchasing activity that is directed toward the product. This value can also be stored in the computer-readable memory. In a step 206, the value representative of purchasing activity and the value representative of non-purchasing activity are combined. This result is displayed on a display screen, such as a display screen of a remote device 104, 106 or 108. The result of the combining can be scaled and rounded so that the result is a whole number between 0 and 99. This number is referred to herein as a “Community Value” that reflects the relative value placed on the product by members of a community.

In an embodiment, the first factor, which represents purchasing activity directed toward a particular product, may be determined as a ratio of the number of times that the particular product has been purchased by members of the community compared to the number of times that the best-selling product has been purchased by members of the community. Alternatively, the first factor may be determined as a ratio of the number of times that the particular product has been included in the library of individual members of the community compared to the maximum number of times that any product has been included in individual member's libraries. In either case, a record that is representative of the number of times that each product has been purchased (or other information that is representative of purchasing activity) is preferably stored in computer-readable storage medium at the server 102.

The second factor may be determined based on social interactions among users in the community that are directed toward the product, such as ratings given to the product by members of the community, discussions about the product, inclusion of the product in a group library, recommendations for the product, and reviews of the product These interactions represent the popularity of the product among the community.

In a preferred embodiment, the Community Value is calculated for a product every 24 hours. However, a different frequency of calculation can be selected. For example, the frequency of the calculation may be increased if social activity increases within the community. Each product is preferably given an updated Community Value during every calculation run. Data used for determining the Community Value can be restricted to a certain time period (e.g., by only considering activity within the prior 12 months). Alternatively, the data can encompass all available data. Additionally, all historical Community Values, and possibly also the underlying data, are preferably stored in computer-readable storage medium at the server 102 for future use, such as for data mining for additional features (e.g. for presenting historical graph of a product's Community Value index).

The first and second factors may then be weighted and combined. For example, the Community Value or “CV” can be obtained using the following formula:

CV=((Oa(Ow)+Pa(Pw))*100)−1

Where: Oa is an ownership average percentage, Ow is a weighting factor for ownership, Pa is a popularity average percentage, Pw is the weighting factor for popularity. The weighting factors are preferably selected to be any number between 0.0000 and 0.9999, so that they total 0.9999 (e.g. Ow=0.3333 and Pw=0.6666) (the number 0.9999 is effectively equivalent to one). Multiplication by 100 and subtracting one shifts the resulting CV number to between 0-99. Rounding the result to the nearest whole number ensures that the CV number is a whole number.

The ownership average percentage Oa, preferably takes into account an average percentage of ownership for the product among “active” members of the community as well as ownership among all members of the community. For example, “active” members of the community can be members that have logged in within particular time period (e.g. within the last 7 days). Thus, Oa can be given as follows:

Oa=(Oc+Om)/2

Where Oc is a ratio of the current product ownership compared to the product that has the most ownership (i.e. the best-selling product) and Om is a ratio of the current product ownership among active users compared to the product that has most ownership among active users. Here, ownership refers to purchases of the product and may also include instances in which the product is included in an individual user's library without it having been purchased by the user. For example, if the particular product has been purchased 1500 times and the product that has been purchased the most has been purchased 2486 times, Oc can be determined as 1500/2486 or 0.6034. Similarly, if the particular product has been purchased by active users 126 times and the product that has been the most purchased by the active users has been purchased by the active users 240 times, Om can be determined as 126/240 or 0.5250. Then, the average Oa can be determined as (0.6033+0.5250)/2 or 0.5642. Also, where Ow=0.3333, then Oa(Ow) can be determined as 0.5642*0.3333 or 0.1880.

Thus, Oc can be given as

Oc=U{L[B]}/L[A]x

Where U{L[B]} is current product ownership count and L[A]x is a maximum ownership count among all products in all Libraries.

Similarly, Om can be given as

Om=S{L[B]}/S{L[A]x}

Where S{L[B]} is current product ownership count among active users and S {L[A]x} is a maximum ownership count among all products in all Libraries for only the active users.

As described above, Oc and Om are equally weighted in the average Oa. However, they may be weighted differently. For example, Om may be weighted more heavily than Oc so that activity of active users has a greater influence on the resulting Community Value. Alternatively, ownership average percentage Oa can be based only on Oc or only on Om. However, taking both Oc and Om data into account is preferred since it allows the Community Value to fluctuate based on the most-current activity, while retaining some residual value from historical activity.

The popularity average percentage Pa is preferably based, not on purchases, as is the ownership percentage Oa, but on social interactions among users in the community that are directed toward the product. For example, these can include, one or more of the following: ratings of the product, discussions about the product, inclusion of the product in a group library, recommendations for the product, and/or reviews of the product.

For example, the popularity average percentage Pa may be determined as an average of several values that reflect these interactions among users in the community. Specifically, the popularity average percentage Pa may be determined as

Pa=(Ra+Dp+Gp+Ep+Vp)/5

Where Ra is an average rating of the product, Dp is a ratio of the current product discussion topic associations compared to the product that has the most discussion topic associations, Gp is a ratio of the current product group associations compared to the product that has the most group associations, Ep is a ratio of the current product recommendations compared to the product that has the most recommendations, and Vp is a ratio of the current product reviews compared to the product that has the most reviews.

For determining Ra, the average rating of the product, the ratings given to the product by all users can be averaged. For a rating scale of one to five, the average may be multiplied by 0.2 to obtain a number between 0.0000 and 0.9999.

For determining Dp, the ratio of the current product discussion topic associations compared to the product that has the most discussion topic associations, the following expression may be used:

Dp=D[B]/Dx[A]

Where D[B] is a total number of discussion topics that are associated with the current product and Dx[A] is the maximum number of discussions topics among all products. In other words, Dx[A] refers to the number of discussion topics that are associated with the product having the highest number of discussion topics.

For determining Gp, the ratio of the current product group associations compared to the product that has the most group associations, the following expression may be used:

Gp=G[B]/Gx[A]

Where G[B] is a total number of groups that are associated with the current product and Gx[A] is the maximum number of associated groups among all products. In other words, G[B] refers to the number of groups that have the product in their libraries and Gx[A] refers to the number of groups that are associated with the product for which the greatest number of groups have included it in their library.

For determining Ep, the ratio of the current product recommendations compared to the product that has the most recommendations, the following expression may be used:

Ep=E[B]/Ex[A]

Where E[B] is the number of recommendations received by the current product and Ex[A] is the maximum number of recommendations among all products. In other words, Ex[A] refers to the number of recommendations received by the product that received the most recommendations.

For determining Vp, the ratio of the current product reviews compared to the product that has the most reviews, the following expression may be used:

Vp=V[B]/Vx[A]

Where V[B] is the number of reviews received by the current product and Vx[A] is the maximum number of reviews among all products. In other words, Vx[A] refers to the number of reviews received by the product that received the most reviews.

For example, for a particular product, it may have received an average rating of 4 out of 5. Therefore, Ra is determined as 0.8000. Also, there may have been eleven discussion topics associated with the product whereas the product with which the most discussion topics were associated may have had twenty such discussion topics. In this case, Dp=D[B]/Dx[A]=11/20=0.5500. Additionally, seven groups may have included the product within their libraries, whereas, the product included in the most group libraries may have been included in ten libraries. In this case. Gp=G[B]/Gx[A]=7/10=0.7000. Further, the product may have received 356 recommendations whereas the product that received the most recommendations may have received 500 recommendations. In this case, Ep=E[B]/Ex[A]=356/500=0.7120. Finally, the product may have received 200 reviews, and this product may happen to be the one that received the greatest number of reviews. In this case, Vp=V[B]/Vx[A]=200/200=1.0000.

Thus, using the expression: Pa=(Ra+Dp+Gp+Ep+Vp)/5, Pa=(0.8000+0.5500+0.7000+0.7120+1.0000)=0.7524. Also, where Pw=0.6666, then Pa(Pw) can be determined as 0.7524*0.6666 or 0.5015.

Finally, using the expression CV=((Oa(Ow)+Pa(Pw))*100)−1, CV can be determined as CV=((0.1880+0.5015)*100)−1=68. Therefore, this results in a Community Value of 68 being assigned to this particular product.

The Community Value is preferably computed by the server 102 while the information from which the Community Value is derived is preferably stored by the server 102 (e.g. in database 114). Software used to perform the calculations can be stored at the server 102 and can be executed by the processor 112. The information on which the Community Value is based is collected by the server 102 during its interactions with the devices 104, 106 and 108. Appropriate application software is stored and executed by the server 102 and the devices 104, 106 and 108, respectively, in order to perform their respective functions described herein.

The Community Value can be used to show a user a quick and simple score as to how the community values the product. This can be accomplished by using how many users have the product in their library, groups that have the product as well as rating and review information. This will be compiled into a score for the user.

The Community Value can be available to the user (e.g. by displaying the Community Value at the user device 104, 106 or 108). For example, the value may be displayed when a user is browsing products on the website, or when the user is sorting a catalog list such that the user can sort by highest Community Value to browse product that the community as a whole likes the most. Thus, a plurality of the products can be sorted according to their relative values and displayed in order of community value.

The Community Value for products can be displayed in many locations throughout the website as well as being displayed by application software running on the devices 104, 106 or 108. The Community Value for a product can appear with the title of the product while the user is browsing a catalog of products as well as in product detail pages.

The description above illustrates operation of embodiments of the invention and is not meant to limit the scope of the invention. It will be apparent to one skilled in the relevant art that variations will be encompassed by the spirit and scope of the invention and that the invention may be practiced in other embodiments. The particular division of functionality between the various system components described herein is merely exemplary. Thus, the methods and operations presented herein are not inherently related to any particular computer or other apparatus. Functions performed by a single system component may instead be performed by multiple components, and functions performed by multiple components may instead performed by a single component. It will also be apparent that process steps described herein can be embodied in software, firmware or hardware. Thus, the present invention or portions thereof may be implemented by apparatus for performing the operations herein. This apparatus may be specially constricted or configured, such as application specific integrated circuits (ASICs) or Field Programmable Gate Arrays (FPGAs), as a part of an ASIC, as a part of FPGA, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored on a computer readable medium that can be accessed and executed by the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and or coupled to a computer system bus. Furthermore, the methods described in the specification may be implemented by a single processor or be implemented in architectures employing multiple processor designs for increased computing capability. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention.

Claims

1. A method of determining relative value of a product comprising steps of:

obtaining a value that is representative of purchasing activity directed toward a product and storing the value in computer-readable storage medium;

obtaining a value that is representative of non-purchasing activity that is directed toward the product and storing the value in computer-readable memory; and

combining the value representative of purchasing activity with the value representative of non-purchasing activity and displaying the result on a display screen.

2. The method according to claim 1, wherein the value representative of purchasing activity is representative of a number of times the product has been purchased compared to a number of times a best-selling product in a catalog of products has been purchased.

3. The method according to claim 1, wherein the value representative of non-purchasing activity is determined based on one, two or more of the following: ratings given the product, discussions about the product, inclusion of the product in a group library, recommendations for the product, and reviews of the product.

4. The method according to claim 1, wherein said combining comprises assigning a first weight to the value representative of purchasing activity and assigning a second weight to the value representative of non-purchasing activity, multiplying the values by their respective weights and adding the result.

5. The method according to claim 4, further comprising scaling and rounding a result of the adding so that the result is a whole number between 0 and 99.

6. The method according to claim 1, further comprising determining a relative value for a plurality of additional products.

7. The method according to claim 6, further comprising sorting the product and the plurality of additional products according to the relative value determined for each product and displaying the products according to said sorting.

8. The method according to claim 6, wherein the products comprise digital content.

9. The method according to claim 6, wherein the products comprise e-books.

10. The method according to claim 8, wherein the products comprise products other than digital content.

11. The method according to claim 10, wherein the products comprise paperback and hardbound books.

12. The method according to claim 1, wherein the value representative of non-purchasing activity is determined based on an average rating given the product by members of a community.

13. The method according to claim 1, wherein the value representative of non-purchasing activity is determined based on a number of on-line discussions that refer to the product compared to a number of on-line discussions that refer to a product having the most on-line discussions.

14. The method according to claim 1, wherein the value representative of non-purchasing activity is determined based on a number of group libraries that include the product compared to a number of group libraries in which a product that is included in the most group libraries.

15. The method according to claim 1, wherein the value representative of non-purchasing activity is determined based on a number of recommendations received by the product compared to a number of recommendations received by a product that received the most recommendations.

16. The method according to claim 1, wherein the value representative of non-purchasing activity is determined based on a number of reviews received by the product compared to a number of reviews received by a product that received the most reviews.

17. The method according to claim 1, wherein the method performed by one or more centralized servers communicating with a plurality of remote devices, the remote devices being under control of users who are members of a community.

18. The method according to claim 1, further comprising repeatedly: obtaining an updated value representative of purchasing activity; obtaining an updated value that is representative of non-purchasing activity; and combining the updated values.

19. The method according to claim 1, wherein the value that is representative of purchasing activity and the value representative of non-purchasing activity are determined from activity that occurred only during a specified corresponding intervals.

20. The method according to claim 1, wherein the value that is representative of purchasing activity is determined by determining a weighted average of purchasing activity of only selected members of a community and purchasing activity of all members of the community.

21. A system for determining relative value of a product comprising:

a plurality of remote devices; and

one or more servers in communication with the plurality of remote devices wherein the one or more servers maintain a catalog of products and obtain a value. that is representative of purchasing activity directed toward a product in the catalog by users of the remote devices, and wherein the one or more servers obtain a value that is representative of non-purchasing activity that is directed toward the product by users of the remote devices and wherein the value representative of purchasing activity is combined with the value representative of non-purchasing activity and the result is displayed.

22. Computer-readable media having stored thereon a software program, which when executed, causes one or more computers to perform a method of determining relative value of a product comprising steps of:

obtaining a value that is representative of purchasing activity directed toward a product and storing the value in computer-readable storage medium;

obtaining a value that is representative of non-purchasing activity that is directed toward the product and storing the value in computer-readable memory; and

combining the value representative of purchasing activity with the value representative of non-purchasing activity and displaying the result on a display screen.

23. A system for determining relative value of a product comprising steps of:

means for obtaining a value that is representative of purchasing activity directed toward a product and storing the value in computer-readable storage medium;

means for obtaining a value that is representative of non-purchasing activity that is directed toward the product and storing the value in computer-readable memory; and

means for combining the value representative of purchasing activity with the value representative of non-purchasing activity and displaying the result on a display screen.