Automated Recommendation System for Human-Curated Lists

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Solution Overview

Problem

Users face challenges in efficiently reviewing and determining reliable information from numerous human-curated lists available online, making it difficult to select items such as products or services due to the vast amount of diverse and unreliable information.

Innovation Solution

A method utilizing a processor to identify and analyze human-curated lists, calculate scores for the lists and items, and generate recommendations based on these analyses, thereby providing a reliable source of information without the need for users to review each list individually.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If users review multiple human-curated lists individually to find reliable information, then they can make informed decisions, but the time and effort required increases significantly

Engineering Contradiction:

Improvereliability of informationVSAvoidtime to review lists

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent introduces an automated recommendation system that acts as an intermediary between human-curated lists and users. This system aggregates multiple lists, analyzes their content, and generates synthesized recommendations, eliminating the need for users to manually review each list while maintaining reliability through systematic analysis of source materials.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the mechanical process of manual list review with an automated computational system. The system uses algorithms to process, analyze, and synthesize information from multiple human-curated lists, substituting human cognitive effort with automated information processing while preserving the reliability of source information.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If users manually review each human-curated list to determine reliability, then they can assess information quality, but the complexity of the selection process increases

Engineering Contradiction:

Improvereliability assessmentVSAvoidcomplexity of selection process

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The automated recommendation system serves as an intermediary that handles the complex task of reliability assessment. It systematically evaluates multiple human-curated lists using standardized criteria, managing the complexity of cross-referencing and analyzing multiple sources while presenting simplified, reliable recommendations to users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms the complex qualitative assessment of list reliability into quantifiable parameters and scoring systems. By converting reliability evaluation into measurable metrics, the system simplifies the selection process while maintaining rigorous assessment standards.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If numerous human-curated lists are made available online, then information diversity increases, but the difficulty of identifying reliable information increases

Engineering Contradiction:

Improveinformation diversityVSAvoiddifficulty of identifying reliable information

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The recommendation system acts as an intermediary that manages information diversity from multiple human-curated lists. It systematically processes diverse sources, applies consistency checks, and synthesizes recommendations that reflect the diversity of sources while eliminating the difficulty of navigating and evaluating the diversity manually.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent merges information from multiple diverse human-curated lists into unified recommendations. By combining and synthesizing data from various sources through systematic analysis, it preserves information diversity while making reliable recommendations easier to identify through aggregated evidence.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS10896232B2Generating recommendations based on human-curated lists

Publication Date: 2021.01.19 FUJITSU LTD

AI summary

A method of generating a recommendation for an item is provided. A method may include receiving search results based on a query for an item. The method may also include identifying a plurality of human-curated lists from the search results, wherein each human-curated list of the plurality of human-curated lists includes a plurality of human-recommended items. Further, the method may include determining, via the at least one processor, one or more scores associated with at least one of the plurality of human-curated lists and the plurality of human-recommended items. The method may further include generating, via the at least one processor, a recommendation including at least one recommended item based on the determined one or more scores. Furthermore, the method may include displaying the recommendation for the item.