Canon Kabushiki Kaisha

Farhad Fuad Islam

Michael Alexander Oldfield

Mikhail Prokopenko

Dong Mei Zhang

Ryszard Kowalczyk

Neil Eric Carlyle

Margaret Hanna

Marc Butler

Paul Trayers

Thomas Phillip Alexander Howard

Wait Yat Wong

The method represents a plurality of independent data items. The method first assigns a multi-dimensional iconic representation to each data item, then alters at least one dimension of each iconic representation according to a change in a corresponding state of the corresponding data item.

Representing a plurality of independent data items

The present invention relates to a television system () and a method for automatically suggesting suitable programs to a viewer from a large number of available programs. The system () includes a DTV-agent system (). Title information and characteristics of programs are made available as EPG (Electronic Program Guide) data, which including at least one Electronic Program Guide Database (). A learning module () records characteristics associated with each program viewed by the user, and forms sets of these characteristics. The frequency of occurrence of each set is also determined. A recommendation module () uses a number of tasks to compile a list of viewer recommendations (). Various tasks are defined, with each task defining a unique combination of a manner of ordering the viewer profile (), and particular Relevance Filters for filtering the ordered viewer profile. Upon entry of a user request for a list of program recommendations (), a search is performed of the EPG data for programs with characteristics that best match sets selected by the task(s). The user is notified of the availability such programs, allowing selection of a particular program.

Television program recommendation system

A system () for earning functional usage patterns of a user of the system () and for planning a best sequences of events suitable for a particular user in a particular context is disclosed. Information relating to user decisions, such as the context during which the decision was made and the actual user decision, is gathered by the DTV-agent () and delivered to the active avatar agent (). The learning module () operates to identify all generalization patterns from a number of instances. Method () then determines which particular decision is most appropriate by comparing the generalization patterns with the current context for which a decision must be made. Clusters of such behavior patterns are formed with each cluster having the same number of matched attributes. A decision selecting process then selects a decision which is most appropriate to the current context from the behavior patterns.

Functional planning system

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Apex, NC, United States of America

Farhad Fuad Islam