Patent application number | Description | Published |
20090254541 | Method and Apparatus for Automated Selection, Organization, and Recommendation of Items Based on User Preference Topography - A computer system representing user preferences in an N-dimensional preference topography and making recommendations based on such topography. The preference topography depicts user ratings of products in a recommendation database. Each product is represented by a product vector associated with N objectively measurable characteristics. The user rating of a product, therefore, represents the user's preference for the particular combination of the N objectively measurable characteristics making up the product. In making a recommendation of products to the user, the system assigns a rating to each product in the recommendation database based on the preference topography. The system then selects a plurality of maximally unique choices from the rated products for recommendation to the user. These maximally unique choices are calculated to be as diverse from one another as possible but still to the user's liking. In another embodiment of the invention, the system identifies portions of the N-dimensional rating space for which the user has indicated a positive association (a positive preference cluster) or a negative association (a negative preference cluster). In making a recommendation of a potential product, the system determines the similarities of products that fall in the positive preference cluster with the potential product. The system also takes into account the products that fall in the nearest negative cluster and determines the similarities with such products and the potential product. In one particular aspect of the invention, the system presents a virtual character for making the usage of the system more user-friendly and interesting. The virtual character is programmed to interact with the user for obtaining user ratings of products and thus determining where the user preferences lie. | 10-08-2009 |
20120116872 | Selecting Advertisements from One or More Databases for Sending to a Publisher - An advertising system determines the context of a user accessing a publication media (e.g., an online web site). The advertising system retrieves candidate advertisements from one or more databases based on the user's context (e.g., a user search request). The advertising system selects particular advertisements and then sends them to the user (e.g. for display on the user's terminal or device). | 05-10-2012 |
20120130813 | SELECTING ADVERTISEMENTS FOR USERS VIA A TARGETING DATABASE - A data processing system collects messages to create user profiles for users of user devices. The messages are collected from various systems and include data regarding user events on user devices. The user profiles are stored in a targeting database. In response to a request for information corresponding to a user device, a user profile is identified using data from the request and information from the profile is provided to the requester. | 05-24-2012 |
20120150627 | RANKING ADVERTISEMENTS SELECTED FROM ONE OR MORE DATABASES BY GEORELEVANCE - A search request is received from a user and the user's context is determined. A publisher is determined for the search request. Candidate advertisements are retrieved from at least one advertisement database to create an advertisement candidate pool, the retrieving based on the user context and the search request. A set of advertisements are selected from the advertisement candidate pool. A georelevance is determined for each of the set of advertisements. The set of advertisements is sorted, wherein the advertisements are sorted based on, at least in part, the georelevance of each of the set of advertisements. The sorted set of advertisements is then transmitted to the user. | 06-14-2012 |
20120150630 | SELECTING AND RANKING ADVERTISEMENTS FROM ONE OR MORE DATABASES USING ADVERTISER BUDGET INFORMATION - An advertising system logs performance data regarding user interactions with advertisements from a plurality of advertisers. The advertising system uses the performance data to calculate various performance metrics, which are, in turn, used to determine budget weighting values for each of the plurality of advertisers. The advertising system retrieves candidate advertisements from one or more databases based on the user's context (e.g., a user search request). The advertising system selects particular advertisements and/or sorts the advertisements using the budget weighting values, and then sends them to the user (e.g., for display on the user's terminal or device). | 06-14-2012 |
20150046259 | SYSTEMS AND METHODS FOR PERSONALIZED ORCHESTRATION OF BUSINESS INFORMATION - Systems and methods for personalized orchestration of business information are provided. Responsive to a request, personalized curation information for an identified end user may be accessed and may include geo-relevant graph information, collection information specifying collection characteristics corresponding to collections associated with the identified end user, and/or window information specifying window characteristics corresponding to business participants in a listing directory service and having been previously associated with the identified end user consequent to indications of end-user interest. A set of business information related to the one or more business participants may be accessed, may include business listing information, and may be windowed as a function of the window information. The windowing may include processing the set of business information based on the personalized curation information to form a windowed set of information, and may further include transmitting the windowed set to facilitate display of the personalized business information. | 02-12-2015 |
Patent application number | Description | Published |
20080208823 | Sharing Playlists in a Recommendation System - A system for sharing playlists and playlist essence with different users. A user desiring to share his or her playlist generates the playlist and a playlist characterization. The playlist characterization is based on acoustic analysis data of one or more songs in the playlist. The playlist and playlist characterization is then transmitted to another end user device. The end user device receiving the shared playlist searches the user's music collection for the songs in the playlist. If a gap is detected in the playlist because the receiving user does not own a particular song, the receiving end user devices automatically selects another song that is owned by the user to fill-in the gap. The song is selected based on the playlist characterization with the aim of preserving the essence of the shared playlist. | 08-28-2008 |
20080215173 | System and Method for Providing Acoustic Analysis Data - A music recommendation system receives a user selection of desired music, retrieves analysis data associated with the selected music, and generates a playlist of songs based on the analysis data. The analysis data is generated based on a processing of one or more audio signals associated with the selected music. The analysis data may downloaded from a central server. If the analysis data is not available from the central server, it is generated locally at a user end, and uploaded to the central server. A plurality of user-selectable shuffling mechanisms are provided to allow the order of the songs to be shuffled according to the selected shuffling mechanism. The end user device may also receive recommendation of new music from different providers based on the analysis data of music for which the recommendation is to be based. | 09-04-2008 |
20080294277 | System and Method for Shuffling a Playlist - A music recommendation system receives a user selection of desired music, retrieves analysis data associated with the selected music, and generates a playlist of songs based on the analysis data. The analysis data is generated based on a processing of one or more audio signals associated with the selected music. The analysis data may downloaded from a central server. If the analysis data is not available from the central server, it is generated locally at a user end, and uploaded to the central server. A plurality of user-selectable shuffling mechanisms are provided to allow the order of the songs to be shuffled according to the selected shuffling mechanism. The end user device may also receive recommendation of new music from different providers based on the analysis data of music for which the recommendation is to be based. | 11-27-2008 |
20090012635 | System and Method for Providing Recommendations by a Remote Server Based on Acoustic Analysis Data - A music recommendation system receives a user selection of desired music, retrieves analysis data associated with the selected music, and generates a playlist of songs based on the analysis data. The analysis data is generated based on a processing of one or more audio signals associated with the selected music. The analysis data may downloaded from a central server. If the analysis data is not available from the central server, it is generated locally at a user end, and uploaded to the central server. A plurality of user-selectable shuffling mechanisms are provided to allow the order of the songs to be shuffled according to the selected shuffling mechanism. The end user device may also receive recommendation of new music from different providers based on the analysis data of music for which the recommendation is to be based. | 01-08-2009 |
20090254554 | MUSIC SEARCHING SYSTEM AND METHOD - A music searching system and method conducting a metadata search of music based on an entered search term. Music identified from the metadata search is used as seed music to identify other acoustically complementing music. Acoustic analysis data of the seed music is compared against acoustic analysis data of potential candidates for determining whether they are acoustically complementing music. The acoustically complementing music is then displayed to the user for listening, downloading, or purchase. | 10-08-2009 |
20120331386 | SYSTEM AND METHOD FOR PROVIDING ACOUSTIC ANALYSIS DATA - A music recommendation system receives a user selection of desired music, retrieves analysis data associated with the selected music, and generates a playlist of songs based on the analysis data. The analysis data is generated based on a processing of one or more audio signals associated with the selected music. The analysis data may downloaded from a central server. If the analysis data is not available from the central server, it is generated locally at a user end, and uploaded to the central server. A plurality of user-selectable shuffling mechanisms are provided to allow the order of the songs to be shuffled according to the selected shuffling mechanism. The end user device may also receive recommendation of new music from different providers based on the analysis data of music for which the recommendation is to be based. | 12-27-2012 |