System and method for selective transmission of multimedia based on subscriber behavioral model

• US 8,099,325 B2
• Filed: 05/01/2002
• Issued: 01/17/2012
• Est. Priority Date: 05/01/2002
• Status: Expired due to Fees

• Current Assignee: Satyam Computer Services Limited of Mayfair Centre
• Original Assignee: Saytam Computer Services Limited
• Inventors: Gangadharpalli, Sridhar, Varadarajan, Sridhar, Rao, Korrapati Kaliana
• Primary Examiner(s): Weiss, John
• Assistant Examiner(s): VANDERHORST, MARIA VICTORIA
• Application Number:
  US10/135,675
• Publication Number:
  US 20030208754A1
• Time in Patent Office: 3,548 Days
• Field of Search: 705/14, 707/706, 725/34
• US Class Current: 705/14.49
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: H04N 21/25833 involving client hardware characteristics, e.g. manufacturer, processing or storage capabilities allocation of resources considering hardware capabilities in multiprogramming arrangements G06F9/5044; allocation of resources considering software capabilities in multiprogramming arrangements G06F9/5055
• CPC Class Codes: H04N 21/25841 involving the geographical location of the client retrieval from the Internet by querying based on geographical locations G06F16/9537; arrangements for identifying locations of receiving stations in broadcast systems H04H60/51; location of the user terminal in data switching networks H04L67/52; services making use of the location of users or terminals in wireless networks H04W4/02; locating users or terminals in wireless networks H04W64/00
• CPC Class Codes: H04N 21/25883 being end-user demographical data, e.g. age, family status or address arrangements for identifying locations of users in broadcast systems H04H60/52
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/2668 Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063
• CPC Class Codes: H04N 21/41407 embedded in a portable device, e.g. video client on a mobile phone, PDA, laptop constructional details of equipment or arrangements specially adapted for portable computer application G06F1/1626; arrangements specially adapted for mobile receivers in broadcast systems H04H20/57
• CPC Class Codes: H04N 21/44224 Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/4663 involving probabilistic networks, e.g. Bayesian networks
• CPC Class Codes: H04N 21/812 involving advertisement data advertising per se G06Q30/02
• CPC Class Codes: H04N 7/17318 Direct or substantially direct transmission and handling of requests

Abstract

An advertisement system that provides subscribers with the most relevant advertisements based on their individual purchase prediction of the various products and contextual information such as location, ongoing public events, festivals and personal events. The system customizes the advertisements to suit the audiovisual, language, and textual caption preferences of the subscribers. System provides for tracking of various promotional offers made to the subscribers from the time the offer is made to the time it is redeemed by the subscriber. System consists of methods to learn (a) purchase predictions (b) preferred streaming schedule and (c) the subscriber'"'"'s preferences in terms of audiovisual aspects of the advertisements. Purchase prediction for individual subscriber is derived from historical purchase data by time series analysis and the influence of events such as co-purchases of other products and organized events such as sales exhibitions and festivals is derived based on Bayesian network models.

Claims

1. A system for selecting, customizing and streaming a plurality of promotional advertisements of a plurality of products supported by a plurality of sponsors to a plurality of 3G capable terminals of a plurality of subscribers of said system satisfying a plurality of sponsor requirements of said plurality of sponsors and based on a plurality of past purchases of said plurality of subscribers, a plurality of events, a plurality of malls, a plurality of festivals, and a plurality of planned streamings, involving streaming of a plurality of customized advertisements related to said plurality of promotional advertisements to a subscriber of said plurality of subscribers and to a plurality of grouped subscribers of said plurality of subscribers, said system comprising:

means for obtaining said subscriber of said plurality of subscribers;

means for predicting a plurality of predicted products of said subscriber based on a plurality of subscriber specific past purchases associated with said subscriber, wherein said plurality of subscriber specific past purchases is a part of said plurality of past purchases and said means further comprises means for obtaining a plurality of product hierarchical relations associated with said plurality of products, means for computing a plurality of product purchase predictions based on said plurality of hierarchical relations and said plurality of subscriber specific past purchases, and means for determining a predicted product of said plurality of predicted products based on said plurality of product purchase predictions and a predefined threshold;

means for predicting said plurality of predicted products based on said plurality of predicted products and a plurality of co-purchases associated with said plurality of subscriber specific past purchases;

means for predicting said plurality of predicted products based on said plurality of predicted products, said plurality of events, and a plurality of influence values, wherein said means further comprises means for computing said plurality of influence values based on said plurality of subscriber specific past purchases and a plurality of event specific products associated with an event of said plurality of events, wherein an event specific product of said plurality of event specific products is a part of said plurality of products;

means for predicting said plurality of predicted products based on said plurality of predicted products, said plurality of festivals, and a plurality of influence values, wherein said means further comprises means for computing said plurality of influence values based on said plurality of subscriber specific past purchases and a plurality of festival specific products associated with a festival of said plurality of festivals, wherein a festival specific product of said plurality of festival specific products is a part of said plurality of products;

means for predicting said plurality of predicted products based on said plurality of predicted products, said plurality of malls, and a plurality of influence values, wherein said means further comprises means for computing of said plurality of influence values based on said plurality of subscriber specific past purchases and a plurality of mall specific products associated with a mall of said plurality of malls, wherein a mall specific product of said plurality of mall specific products is a part of said plurality of products;

means for selecting a plurality of selected advertisements of said plurality of promotional advertisements based on said plurality of predicted products;

means for selecting said plurality of selected advertisements based on a plurality of grouped subscribers, wherein each of said plurality of grouped subscribers is a subscriber of said plurality of subscribers and is a part of a private group participating in a movie viewing or is part of a group involved in a video conference;

means for customizing said plurality of selected advertisements based on said subscriber resulting in a plurality of customized advertisements, wherein said means for customizing further comprises means for obtaining an advertisement of said plurality of selected advertisements, means for determining a human character present in said advertisement, means for changing an attribute of an object of said human character wherein said object comprises a shirt, shoe, and belt resulting in a customized advertisement, and means for making said customized advertisement a part of said plurality of customized advertisements;

means for changing a property of an audio content associated with said customized advertisement wherein said property comprises type, language, voice, and instrumental, and means for changing a property of a background of said customized advertisement wherein said property comprises of type, color, shape, and size;

means for scheduling said plurality of customized advertisements with respect to said subscriber resulting in a plurality of subscriber specific planned streamings, wherein each of said plurality of subscriber specific planned streamings is a part of said plurality of planned streamings, each of said plurality of subscriber specific planned streamings is associated with a customized advertisement of said plurality of customized advertisements, and said means further comprises means for obtaining a sponsor of said plurality of sponsors, means for obtaining a sponsor requirement associated with said sponsor, wherein said sponsor requirement is a part of said plurality of sponsor requirements, means for obtaining an advertisement of said plurality of customized advertisements, means for determining a number of streaming sessions based on said sponsor requirements,

means for determining of a number of repeat streamings allowed for said subscriber based on said sponsor requirement,

means for determining of a number of required streamings based on said subscriber, and

means for weekly planning based on said number of streaming sessions, said number of repeat streamings allowed, and said number of required streamings resulting in said plurality of subscriber specific planned streamings; and

means for streaming an advertisement of said plurality of customized advertisements to said subscriber as per said plurality of subscriber specific planned streamings, wherein said means further comprises means for real time streaming of a video portion of said advertisement along with captions during a voice call of said subscriber and means for streaming said advertisement to said plurality of grouped subscribers.