Automated Search Term Generation for Document Filtering
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Solution Overview
Problem
Users face challenges in efficiently managing and filtering large volumes of online content due to the limitations of conventional search engines, which require manual input of search terms and struggle to provide relevant results without user intervention.
Innovation Solution
A programmed computer system that automatically identifies word groups from user-relevant documents, ranks them based on frequency and rarity, and uses these groups to select and display relevant documents, allowing users to select documents without manual search term generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search engines are used to find relevant documents, then users can search for specific content using search terms, but users must manually generate appropriate search terms which is difficult and time-consuming
Solution Approach 1:
The system performs self-service by automatically analyzing user profiles, historical behavior data, and document collections to generate personalized search terms and queries without requiring manual user input. The system serves itself by autonomously creating the search strategy based on inferred user interests.
Solution Approach 2:
The system performs preliminary action by pre-generating search terms and queries based on user profiles before the user actually needs to search. User interests and preferences are analyzed in advance, and ready-to-use search terms are prepared and stored for immediate retrieval when search is needed.
2Productivity
If Google Alerts service is used to monitor relevant content, then users receive email messages of latest relevant results, but relevance depends on user-selected search terms which are difficult to generate manually
Solution Approach 1:
The system automatically generates search terms for content monitoring based on user profiles and historical behavior, eliminating the need for users to manually create search queries. The monitoring service configures itself by analyzing user interests and autonomously setting up appropriate search term combinations.
3Adaptability or versatility
If users manually manage large volumes of online content, then users can filter and organize content, but the process becomes inefficient and overwhelming with hundreds or thousands of unread items
Solution Approach 1:
The system introduces an intermediary intelligent agent that mediates between the user and the large volume of content. This agent automatically analyzes, filters, and organizes content based on user profiles, acting as a intermediary layer that processes content before presenting it to the user, thereby managing the overwhelming volume efficiently.
Solution Approach 2:
The system segments the large volume of content into manageable groups based on user interests, topics, and relevance. By automatically categorizing and organizing content into segmented groups, the system makes large volumes of content tractable and easier to manage without requiring users to handle everything at once.
4Device complexity
If conventional search engines are used without automated term generation, then the system remains simple to operate, but the system lacks automation and requires continuous user intervention
Solution Approach 1:
The system performs self-service by automatically analyzing user profiles, historical behavior data, and document collections to generate personalized search terms and queries without requiring manual user input. The system serves itself by autonomously creating the search strategy based on inferred user interests.
Data Source
AI summary
A programmed computer receives one or more documents that contain text that is relevant to a user (“interest documents”). The programmed computer automatically identifies groups of words that match the interest documents. The matching word groups are ranked by a weight that is assigned based on how infrequently a word group matches a reference corpus and how frequently the word group matches one or more interest document(s), in comparison to other word groups. A set of word groups are automatically identified based on ranking, and displayed to a user to select documents from a corpus. Selected documents are displayed to the user, e.g. with one or more group of words used in selecting the documents.