System and method for supporting peer interactions

• US 8,386,318 B2
• Filed: 12/30/2008
• Issued: 02/26/2013
• Est. Priority Date: 12/30/2008
• Status: Expired due to Fees

• Current Assignee: Satyam Computer Services Limited (Tech Mahindra Limited)
• Original Assignee: Satyam Computer Services Limited (Tech Mahindra Limited)
• Inventors: Varadarajan, Sridhar, Gopalan, Srividya
• Primary Examiner(s): Weiss, John
• Assistant Examiner(s): SIGMOND, BENNETT M
• Application Number:
  US12/346,167
• Publication Number:
  US 20100167709A1
• Time in Patent Office: 1,519 Days
• Field of Search: 705/14.64, 455/518, 455/519
• US Class Current: 705/14.64
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/104 Peer-to-peer [P2P] networks

Abstract

Peer to peer interactions are a popular way of social networking. Such peer to peer interactions play an important role in (a) obtaining relevant and latest information; (b) obtaining relevant experiential information; (c) obtaining of relevant opinions on, say, products; (d) collaborative problem solving, and so on. Consider a scenario of classified ads: a user visiting a portal through a mobile phone to decide on buying of a product can be well assisted by facilitating peer to peer interactions. In many situations, it is required to make the right selection of peers in order to be able to obtain the right information, and furthermore, voice is a preferred mode of interaction if mobile phones are used in peer to peer interactions. Also, in peer to peer interactions, anonymity is important in order to protect the privacy of the peers. A system and method for supporting peer interactions, especially in a mobile scenario involving classified ads, need to address the issues related to (a) selecting and displaying of the right information; (b) selecting of right peers; and (c) facilitating anonymous peer interactions.

Claims

1. A computer-implemented method for supporting a user of a plurality of users browsing a classified ads portal through a mobile phone in order to help reduce a decision cycle time with respect to a targeted advertisement of a plurality of advertisements that excludes a plurality of closed advertisements based on a plurality of peer users of said user, a plurality of radial structures, and a plurality of real time conversational feedback sessions, wherein said plurality of advertisements is associated with said classified ads portal, each of said plurality of peer users is a particular user of said plurality of users, said plurality of radial surface 1 ads is a part of said plurality of advertisements, said plurality of radial surface 2 ads is a part of said plurality of advertisements, and said plurality of radial surface 3 ads is a part of said plurality of advertisements, the method performed on a computer system comprising at least one processor, said method comprising:

determining, with at least one processor, said plurality of closed advertisements, wherein an ad of said plurality of advertisements has not been clicked by said user for a period of time that exceeds a pre-defined threshold and said ad is a part of said plurality of closed advertisements;

determining, with at least one processor, said plurality of radial surface 1 ads based on said plurality of advertisements and said targeted advertisement;

populating, with at least one processor, said plurality of radial structures using said plurality of radial surface 1 ads, said plurality of radial surface 2 ads, and said plurality of radial surface 3 ads;

displaying, with at least one processor, said plurality of radial surface 1 ads, said plurality of radial surface 2 ads, said plurality of radial surface 3 ads on said mobile phone using said plurality of radial structures;

determining, with at least one processor, a number of clicks, a frequency of clicks, a number of initiated real time conversational feedback sessions, a number of participated real time conversational feedback sessions, a total of real time conversational feedback participation time, and a bought status of a product based on said targeted advertisement and a user of said plurality of users;

determining, with at least one processor, a number of products bought, a number of real time conversational feedback units, and a number of forced outs based on said user;

determining, with at least one processor, a weight 1 associated with said number of clicks, a weight 2 associated with said frequency of clicks, a weight 3 associated with said number of initiated real time conversational feedback sessions, a weight 4 associated with said number of participated real time conversational feedback sessions, a weight 5 associated with said total of real time conversational feedback participation time, a weight 6 associated with said number of products bought, and a weight 7 associated with said number of real time conversational feedback units;

determining, with at least one processor, a maximum 1 and a minimum 1 associated with said number of clicks based on said plurality of users;

determining, with at least one processor, a maximum 2 and a minimum 2 associated with said frequency of clicks based on said plurality of users;

determining, with at least one processor, a maximum 3 and a minimum 3 associated with said number of initiated real time conversational feedback sessions based on said plurality of users;

determining, with at least one processor, a maximum 4 and a minimum 4 associated with said number of participated real time conversational feedback sessions based on said plurality of users;

determining, with at least one processor, a maximum 5 and a minimum 5 associated with said total of real time conversational feedback participation time based on said plurality of users;

determining, with at least one processor, a maximum 6 and a minimum 6 associated with said number of products bought based on said plurality of users;

determining, with at least one processor, a maximum 7 and a minimum 7 associated with said number of real time conversational feedback units based on said plurality of users;

computing, with at least one processor, a rating of a plurality of ratings of said user based on said weight 1, said maximum 1, said minimum 1, and said number of clicks, said weight 2, said maximum 2, said minimum 2, and said frequency of clicks, said weight 3, said maximum 3, said minimum 3, and said number of initiated real time conversational feedback sessions, said weight 4, said maximum 4, said minimum 4, and said number of participated real time conversational feedback sessions, said weight 5, said maximum 5, said minimum 5, and said total of real time conversational feedback participation time, said weight 6, said maximum 6, said minimum 6, and said number of products bought and said weight 7, said maximum 7, said minimum 7, and said number of real time conversational feedback units;

ranking, with at least one processor, said plurality of users based on said plurality of ratings resulting in a plurality of ranked users;

removing, with at least one processor, a ranked user of said plurality of ranked users from said plurality of ranked users if a rank associated with said ranked user is less than a pre-defined first threshold and a number of force outs associated with said ranked user is greater than a pre-defined threshold;

selecting, with at least one processor, a pre-defined number of users from said plurality of ranked users as said plurality of peer users, wherein a rank associated with a user of said plurality of ranked users is less than a pre-defined second threshold and a bought status associated with said user is true;

updating, with at least one processor, said plurality of peer users based on said pre-defined number of users from the top of said plurality of ranked users;

determining, with at least one processor, said plurality of real time conversational feedback sessions, wherein each real time conversational feedback session of said plurality of real time conversational feedback sessions is associated with a user of said plurality of peer users and anonymous peer interaction of a plurality of anonymous peer interactions with said plurality of peer users;

determining, with at least one processor, a real time conversational feedback session of said plurality of real time feedback sessions;

sending, with at least one processor, a plurality of text messages to a user of said plurality of peer users, wherein said user is associated with said real time conversational feedback session;

determining, with at least one processor, a plurality of participating peers associated with said real time conversational feedback session;

determining, with at least one processor, a peer user related information associated with a peer user of said plurality of participating peers, wherein said peer user related information comprises a duration of communication and a number of Push To Talk button pushes;

sending, with at least one processor, a thanks message to said peer user based on said peer user related information and a plurality of policies associated with said classified ads portal;

receiving, with at least one processor, a plurality of voice signals from a peer mobile phone of a peer user of said plurality of participating peers;

converting, with at least one processor, said plurality of voice signals to a plurality of synthetic signals; and

sending, with at least one processor, said plurality of synthetic signals to said mobile phone and a plurality of peer mobile phones associated said plurality of participating peers.

2. The method of claim 1, wherein said method of determining of a plurality of radial surface 1 ads further comprises:

determining of a targeted profile of said targeted advertisement;

determining of a plurality of profiles, wherein each of said plurality of profiles is associated with an ad of said plurality of advertisements;

determining of an ad of said plurality of advertisements, wherein said ad is similar to said targeted advertisement based on a profile associated with said ad and said targeted profile;

making of said ad a part of said plurality of radial surface 1 ads; and

removing of said plurality of radial surface 1 ads from said plurality of advertisements.

3. The method of claim 1, wherein said method of populating further comprises:

determining of a screen dimension of said mobile phone;

determining of a number of radial structures based on said screen dimension;

determining of said plurality of radial structures based on said number of radial structures;

putting of said targeted advertisement at the center of a screen of said mobile phone;

dividing of a radial surface of a radial structure of said plurality of radial structures into a plurality of three portions;

populating of a first portion of said plurality of three portions of said radial surface based on said plurality of radial surface 1 ads;

populating of a second portion of said plurality of three portions of said radial surface based on said plurality of radial surface 2 ads; and

populating of a third portion of said plurality of three portions of said radial surface based on said plurality of radial surface 3 ads.

4. The method of claim 1, wherein said method of populating further comprises:

determining of a plurality of remaining ads based on said plurality of advertisements, wherein each of said plurality of remaining ads has not been displayed based on said plurality of radial structures;

determining of a remaining ad of said plurality of remaining ads;

determining of a plurality of clicked users of said plurality of users based on a plurality of clicks by said plurality of clicked users, wherein a number of clicks on said remaining ad by each of said plurality of clicked users exceeds a pre-defined threshold;

determining of a plurality of grouped ads based on said plurality of clicked users, wherein a grouped ad of said plurality of grouped ads is a part of said plurality of remaining ads, and a number of clicks on said grouped ad by a clicked user of said plurality of clicked users exceeds a pre-defined threshold;

determining of an ad group of a plurality of generic ad groups based on said plurality of grouped ads;

determining of a popularity factor of a plurality of popularity factors associated with said ad group of said plurality of generic ad groups based on a plurality of clicks associated with said ad group;

determining of a selected ad group based on said plurality of generic ad groups and said plurality of popularity factors;

ordering of said selected ad group based on a plurality of clicks associated with each ad of said selected ad group resulting in an ordered selected ad group;

making of said ordered selected ad group a part of a plurality of reserve selected ads;

determining of a scroll direction based on an interaction by said user with said mobile phone;

determining of a plurality of radial structures associated with said mobile phone;

determining of a radial structure based on said plurality of radial structures and said scroll direction;

removing of the radial structure from said plurality of radial structures;

repositioning of said plurality of radial structures; and

populating of a radial structure of said plurality of radial structures based on said plurality of ads and said plurality of reserve selected ads.

5. The method of claim 1, further comprising:

dynamically forming of said plurality of peer users based on said targeted advertisement to provide opinions to the user.

6. The method of claim 1, wherein said plurality of real time conversational feedback sessions is voice-based.