Broadcast Program Ranking via Viewing Concentration Analysis

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Solution Overview

Problem

Existing television systems fail to reliably rank broadcast programs according to user preferences, leading to difficulty in finding favorite programs amidst numerous options and a risk of missing favorite shows due to inadequate consideration of viewing concentration and program relevance.

Innovation Solution

An apparatus and method that generate a viewing history, calculate user preference degrees based on past viewing data and auxiliary program information, and rank future programs using decision measures that account for viewed and non-viewed programs' suitability, incorporating viewing frequencies and concentration levels to optimize program selection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the number of broadcast programs is increased, then the variety of program options is improved, but the user's ability to find favorite programs deteriorates

Engineering Contradiction:

Improvevariety of program optionsVSAvoiduser's ability to find favorite programs

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system continuously monitors user viewing behavior and automatically updates preference profiles and program rankings without requiring manual user input. The viewing history is analyzed to refine preference degrees, creating a self-improving recommendation system that adapts to user tastes over time

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The television receiver automatically generates viewing history signals, analyzes preference patterns, calculates preference degrees for forthcoming programs, and ranks programs without user intervention. The system serves itself by automatically updating its own recommendation algorithms based on accumulated viewing data

Inventive Principle:

Principle #25Self-service

2Ease of operation

If automatic program selection is implemented, then the ease of program selection is improved, but the accuracy of preference matching deteriorates due to inadequate consideration of viewing concentration

Engineering Contradiction:

Improveautomatic program selectionVSAvoidaccuracy of preference matching

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system differentiates between various types of viewing behavior by introducing the concentration degree parameter. Instead of treating all viewing equally, it assigns different weights based on whether the user viewed the program with high or low concentration, creating a more nuanced and accurate preference measurement

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system introduces multiple parameters including viewing frequency, concentration degree, and preference degree to comprehensively characterize user preferences. By changing from simple frequency counting to multi-parameter analysis, the system achieves more accurate preference matching

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If viewing history is used to determine user preference, then the personalization of program recommendation is improved, but the reliability of preference analysis deteriorates due to lack of consideration for viewing concentration

Engineering Contradiction:

Improvepersonalization of program recommendationVSAvoidreliability of preference analysis

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system applies different evaluation standards based on the quality of viewing engagement. By introducing concentration degree as a differentiating factor, it treats high-concentration viewing as more reliable preference indicators than low-concentration viewing, thereby improving the overall reliability of preference analysis

Inventive Principle:

Principle #3Local quality

4Speed

If keyword matching is used for program evaluation, then the speed of program assessment is improved, but the accuracy of preference matching deteriorates due to reliance on predetermined keywords

Engineering Contradiction:

Improveprogram evaluation speedVSAvoidaccuracy of preference matching

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system pre-calculates and stores preference degrees for numerous keywords in advance. When evaluating a program, it quickly retrieves these pre-computed values through keyword matching, achieving both fast evaluation speed and accurate preference matching by combining the efficiency of predetermined keywords with the precision of comprehensive keyword coverage

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS7487530B2Method and apparatus for ranking broadcast programs

Publication Date: 2009.02.03 RAKUTEN GROUP INC

AI summary

Frequencies of viewing of first weekly broadcast programs by a user are detected in connection with past sections of the first weekly broadcast programs. At least one which has the detected viewing frequency greater than a predetermined threshold value is excluded from the first weekly broadcast programs to get second weekly broadcast programs. Program ranking measures representative of a relation between program attributes and user's preference are generated in response to (1) attributes of the past sections of the second weekly broadcast programs and (2) which of the past sections of the second weekly broadcast programs were viewed by the user. Future sections of the second weekly broadcast programs are ranked in response to (1) the generated program ranking measures and (2) attributes of the future sections of the second weekly broadcast programs.