Koninklijke Philips Corporation N.v.

Philips Electronics North America Corporation

Other

Koninlijke Philips Electronics N.v.

J Koninklijke Philips Electronics, Nv.

J David Schaffer

Larry James Eshelman

Kwok Pun Lee

Keith E Mathias

Srinivas Gutta

Kaushal Kurapati

Murali Mani

Karen Irene Trovato

Nevenka Dimitrova

Lalitha Agnihotri

Yasser Alsafadi

Thomas F McGee

Sylvie Jeannin

Mark R Siimpson

Srinivas V R Gutta

Richard A Caruana

A television programming recommender combines the explicit viewing preferences, i.e. user specified preferences, of viewers with their television viewing behavior (implicit preferences) to generate program recommendations. The explicit viewing preferences are utilized to generate an explicit recommendation score, E, for an upcoming television program. The implicit viewing preferences are utilized to generate an implicit recommendation score, I, for the program. A combined recommendation score, C, is computed based on the explicit and implicit scores, E and I. The combined recommendation score, C, can be computed using a weighted linear mapping.

Method and apparatus for generating recommendation scores using implicit and explicit viewing preferences

In a genetic optimization method, the genes of a chromosome population are computationally genetically evolved. The evolving includes evolving a number of expressed genes in each chromosome and employing a fitness criterion evaluated without reference to unexpressed genes of each chromosome. An optimized chromosome produced by the genetic evolving is selected.

Methods for optimizing and using medical diagnostic classifiers based on genetic algorithms

System actions such as presentation of information to a user via a remote control device are carried out in accordance with user preferences based on, e.g., observed user behavior over time. The preferences may be learned by observation of user actions and corresponding system state information, and the preferences may be verified by the user. Particular actions may then be repeated under the control of the system, when appropriate conditions are met. Repetition may be automatic, semi-automatic or user initiated. In an illustrative embodiment, information is presented to a user via a remote control device based at least in part on the determined preference. The information presented to the user via the remote control device may include, e.g., an alarm signal associated with a particular parameter specified by the user, a visible icon presented to the user on a display of the remote control device, or other types of information presented via other types of interfaces, such as audio, speech or tactile interfaces.

Method and apparatus for presentation of intelligent, adaptive alarms, icons and other information

A program recommendation system employing a program record module and one of various nearest neighbor modules is disclosed. In response to a reception of a program record, the program record module converts each key field of the program record into a feature value. A single neighbor module selectively generates a recommendation of a program corresponding to the program record based upon a stored program record qualifying as a nearest neighbor of the received program record. A multiple neighbor module selectively generates a recommendation of the program corresponding to the program record based upon N number of stored program records qualifying as N number of nearest neighbors of the received program record. A neighbor cluster selectively generates a recommendation of the program corresponding to the program record based upon the cluster of stored program records qualifying as the nearest neighbor of the received program record.

Nearest neighbor recommendation method and system

An electronic programming guide (EPG) system employing a preference engine and processing system that combines explicit rule profile, history profile, and feedback profile data to generate new predictions. Television shows are presumed to be indexed by many features. These features are extracted and counted for TV shows watched (implicit profile), and for TV shows rated by the viewer (feedback profile). These profiles are straightforward to combine with suitably greater weight being given to the feedback information. In addition, explicit profiles can make recommendations that stand alone or may be used to modify recommendations arising from either of the two sources. The modifications may take the form of additive or multiplicative changes to the existing recommendations or some other suitable mathematical form.

Three-way media recommendation method and system

A television programming recommender is disclosed that selectively obtains feedback information from a user to update one or more profiles associated with the user. Previously obtained implicit and explicit preferences are utilized to selectively focus the collection of feedback information to further update and refine the implicit and explicit preferences. The present invention obtains feedback from a user in a manner that maximizes the value of the obtained information and improves the performance of the television programming recommender. The present invention automatically requests feedback from the user upon the occurrence of predefined criteria. The nature of the requested feedback, and the manner in which the obtained feedback is used to adjust a profile, can vary.

Method and apparatus for selective updating of a user profile

The invention recommends television programs that are similar to shows identified by a user by computing a similarity metric between programs both in a user-specified selected set and in an identified time interval. The selected set comprises programs currently of interest to the user, for allowing the user or viewer to easily select one or more programs of interest. The similarity metric is a sum of weighted scores for each matching attribute between programs in the selected set and programs in the identified time interval. Shows that are actually selected for viewing are observed and the weightings, w, assigned to each matching program attribute are adjusted, so that over time, the similarity metric, S, more accurately reflects the viewer's idea of 'similar shows.'

Method and apparatus for generating television program recommendations based on similarity metric

A method for optimizing the performance of an algorithm for detecting predetermined content in a media information stream, and a program and apparatus that operate in accordance with the method. The algorithm is a function of a set of parameters. The method comprises the steps of performing the algorithm at least once to detect the predetermined content in the media information stream, while employing a respective set of parameters in the algorithm for each performance thereof, and automatically evolving at least one respective set of parameters employed in the algorithm to maximize the degree of accuracy at which the algorithm detects the predetermined content in the media information stream.

Method, apparatus, and program for evolving algorithms for detecting content in information streams

Artificial intelligence applications require use of training sets containing positive and negative examples. Negative examples are chosen using distributions of positive examples with respect to a dominant feature in feature space. Negative examples should share or approximately share, with the positive examples, values of a dominant feature in feature space. This type of training set is illustrated with respect to content recommenders, especially recommenders for television shows.

Adaptive sampling technique for selecting negative examples for artificial intelligence applications

A method and system for controlling the growth of a features frequency profile of a time-ordered sequence of events, wherein each event has features specific to each event. The events are sequentially processed in an order of processing. The processing includes selecting for each event processed at least one feature comprised by the event. The processing updates a frequency count of each feature selected. The frequency counts are periodically reduced in magnitude by a reduction factor. Frequency counts are selected for deletion upon satisfaction of a condition that favors deletion of those frequency counts having a magnitude less than a threshold value. The selected frequency counts are then deleted. The present invention employs an economical use of memory to store data associated with the features frequency profile and uses a features preference profile that is more responsive to recent information than to older information.

Controlling the growth of a feature frequency profile by deleting selected frequency counts of features of events

A recommendation system and method are disclosed. In the system and method, the personal schedule of the user is used to modify the recommendation functions of media events. The personal schedule may be entered by the user or determined through monitoring over time. An exemplary recommendation function modification is if a media event ends after the user's bedtime, as indicated by the personal schedule. In this example, the recommendation function of that event will be reduced in value because the user will likely go to bed before the event is over.

Real-time event recommender for media programming using 'Fuzzy-Now' and 'Personal Scheduler'

A system for recommending television programs makes use of probabilistic calculations and a viewer profile to create a recommendation. The probabilistic calculations preferably are in the form of Bayesian classifier theory. Modifications to classical Bayesian classifier theory are proposed.

Adaptive TV program recommender

Real-time events are electronically recommended using a fuzzy-now function of time. Real-time experiencing apparatus can be automatically tuned to a particular real-time event using a surf ring related to recommendations or automatically without a user request. Recommendations can be presented on a remote control device.

Recommender system using 'fuzzy-now' for real-time events

An electronic programming guide (EPG) system employs a preference engine and processing system that learns viewers' television watching preferences by monitoring their viewing patterns. The system operates transparently to build a profile of a viewer's tastes. The profile is used to provide services, for example, recommending or automatically recording television programs the viewer might be interested in watching. To permit the personalization of the preferences database, a user interface is provided to allow the user to simulate various kinds of interaction with the system. This allows the system to build a profile rapidly without requiring a long interaction history to personalize the system.

Electronic program guide viewing history generator method and system

The features that are presented to an evolutionary algorithm are preprocessed to generate combination features that may be more efficient in distinguishing among classifications than the individual features that comprise the combination feature. An initial set of features is defined that includes a large number of potential features, including the generated features that are combinations of other features. These features include, for example, all of the words used in a collection of content material that has been previously classified, as well as combination features based on these features, such as all the noun and verb phrases used. This pool of original features and combination features are provided to an evolutionary algorithm for a subsequent evaluation, generation, and determination of the best subset of features to use for classification. In this evaluation and generation process, each combination feature is processed as an independent feature, independent of the features that were used, or not used, to form the combination feature. In this manner, for example, a particular phrase that is generated as a combination of original feature words may be determined to be a better distinguishing feature than any of the original feature words and a more efficient distinguishing feature than an unrelated selection of the individual feature words, as might be provided by a conventional evolutionary algorithm. The resultant best performing subset is subsequently used to characterize new content material for automated classification. If the automated classification includes a learning system, the evolutionary algorithm and the generated combination features are also used to train the learning system.

Growing community of inventors

Wappingers Falls, NY, United States of America

J David Schaffer