Usage-Based Tone Recommendation for Electronic Musical Instruments

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Solution Overview

Problem

Existing electronic musical instruments face challenges in presenting recommendation information that accurately reflects user preferences due to the discrepancy between download/purchase counts and actual usage of content, leading to non-conforming recommendations.

Innovation Solution

A system that tracks and stores usage frequency data of content on a server, calculating correlation degrees between users to generate personalized recommendations based on actual usage counts and times, ensuring that content conforming to user preferences is presented.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional recommendation systems use purchase histories and download numbers to generate recommendations, then recommendation information can be provided, but the recommendations do not accurately reflect user preferences because downloaded contents are not necessarily actually used

Engineering Contradiction:

Improveaccuracy of user preference measurementVSAvoidinability to differentiate between downloaded and used content

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

The system implements feedback by tracking actual usage of downloaded contents through usage frequency information. The recommendation information generation unit receives feedback about which contents are actually used (not just downloaded) and adjusts recommendations based on this usage data, creating a closed-loop system that continuously improves recommendation accuracy.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system introduces usage frequency information as an intermediary metric between content download and recommendation generation. This intermediary data layer allows the system to distinguish between merely downloaded content and actually used content, providing a more accurate basis for recommendations without requiring direct observation of user behavior.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If contents are purchased or downloaded and stored in electronic musical instruments, then users can load and use the stored contents, but the contents are not downloaded multiple times making purchase histories unreliable indicators of user preference

Engineering Contradiction:

Improveability to store and reuse contentsVSAvoidreliability of purchase history as preference indicator

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system changes the measurement parameter from binary purchase/download status to continuous usage frequency information. By tracking how often contents are actually used rather than just whether they were purchased, the system transforms the data into a more meaningful indicator of user preference that accounts for the ability to store and reuse contents.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If recommendation systems are implemented in electronic musical instruments with subscription systems, then contents can be freely added and updated, but contents hardly actually used while purchased many times are still registered in recommendation information

Engineering Contradiction:

Improvecontent addition and update capabilityVSAvoidaccuracy of recommendation information

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback by continuously monitoring usage frequency of subscribed contents and using this information to adjust recommendation generation. This feedback mechanism ensures that the subscription system's content addition and update capabilities are paired with accurate tracking of actual usage patterns.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system makes the recommendation system dynamic by continuously updating usage frequency information as contents are used. Rather than static purchase history, the system adapts recommendations in real-time based on changing usage patterns, allowing it to respond to evolving user preferences in the subscription model.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12620375B2Recommendation information output system and recommendation information output method

Publication Date: 2026.05.05 ROLAND CORP

AI summary

In a server 70, usage counts of tone data of an electronic musical instrument 30, such as load counts of times that the tone data are loaded in the electronic musical instrument 30 are stored for each tone data and each user H utilizing the electronic musical instrument 30. When a recommendation information request is received from a portable terminal 50, the server 70 obtains a similar user similar to a request user included in the recommendation information request from the stored usage counts. Tone data to be recorded in the recommendation information are obtained from the usage count of each tone data of the request and the usage count of each tone data of the similar user, and the recommendation information is generated from the obtained tone data. Accordingly, the recommendation information generated by the user 70 can conform to the preference of the request user.