Automated Project Tagging via Vector Clustering

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Solution Overview

Problem

Existing data analysis projects rely on manual user input for assigning searchable tags, leading to nuanced or absent tags that fail to facilitate effective retrieval of data objects, resulting in irrelevant search results for future users.

Innovation Solution

Automatically assigning searchable metadata to project clusters based on identified filters in project metadata, using a searchable tag identifier module that generates vectors, clusters projects, and assigns tags based on majority filters, improving tag assignment and retrieval efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If users manually assign searchable tags to data analysis projects, then the tagging process allows user customization and control, but the tags become too nuanced or absent to effectively facilitate retrieval of data analysis projects

Engineering Contradiction:

Improveuser control over taggingVSAvoideffectiveness of tag retrieval

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system automatically generates searchable tags by analyzing filter criteria within project metadata, eliminating the need for manual user input. The tagging system serves itself by extracting meaningful keywords from the data analysis project filters, ensuring consistent and effective tags without user intervention.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of user tag assignment is replaced with an automated computational system that parses filter metadata, generates vectors representing filter criteria, clusters similar projects, and assigns tags based on cluster characteristics. This substitution transforms the tagging from a manual operational task to an automated analytical process.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If users manually tag data analysis projects with searchable terms, then individual project tagging is flexible, but the tags provide little or no value for facilitating retrieval and avoiding repetition of data analysis tasks

Engineering Contradiction:

Improvetagging flexibilityVSAvoidretrieval efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system merges multiple individual project tags into cluster-level tags by grouping projects with similar filter criteria. Instead of treating each project independently, the system combines projects into clusters and assigns a single searchable tag to represent the entire cluster, improving retrieval efficiency while maintaining adaptability through the clustering mechanism.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The generated searchable tags serve multiple functions: they enable project retrieval, identify clusters of related projects, prevent task repetition, and provide a standardized interface for searching across diverse data analysis projects. This multi-functionality increases both productivity and adaptability simultaneously.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Device complexity

If searchable tags are manually assigned based on user input, then the tagging process is simple and direct, but the search results fail to produce relevant data analysis projects for future queries

Engineering Contradiction:

Improvetagging process simplicityVSAvoidsearch relevance accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system performs preliminary analysis of filter metadata and vector clustering before tag assignment, ensuring that tags are pre-optimized for search relevance. By analyzing the relationships between filter criteria and projecting them into vector space beforehand, the system ensures that generated tags will accurately represent project clusters and produce relevant search results.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Vector representations serve as an intermediary between the complex filter metadata and the simple searchable tags. The vector clustering process transforms detailed filter criteria into condensed cluster representations, which then inform tag generation. This intermediary process maintains search relevance accuracy while keeping the final tagging process simple and automated.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11366833B2Augmenting project data with searchable metadata for facilitating project queries

Publication Date: 2022.06.21 ADOBE INC

AI summary

Certain embodiments involve augmenting project data with searchable metadata for facilitating project queries. A method includes receiving metadata of the set of projects and identifying a filter within the metadata. The method also includes generating a first vector representing a first project of the set of projects and generating a second vector representing a second project of the set of projects. Further, the method includes grouping the first vector and the second vector into a project cluster based on the first vector and the second vector being within a threshold distance. The project cluster represents a set of filters associated with a subset of projects. Additionally, the method includes assigning a searchable tag to the project cluster based on the filter being a majority filter of the project cluster. The searchable tag includes metadata that facilitates locating the projects responsive to a query to the set of projects.