Contextual Search for Referential Terms in Collaborative Communications
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Solution Overview
Problem
Existing collaborative communications applications struggle to accurately find relevant communications when users enter referential search terms, such as 'my manager' or 'last meeting', due to limitations in fuzzy keyword matching and heuristic-based methods, often requiring explicit user input or specific query commands.
Innovation Solution
A contextual search service that builds a user-specific data store by processing user data from collaborative applications, extracting contextual information, and augmenting referential search terms in real-time using natural language processing and machine learning to provide accurate search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fuzzy keyword matching and heuristic methods are used for search, then the search system is simple and easy to implement, but the search accuracy for referential terms is poor
Solution Approach 1:
The system performs preliminary action by proactively extracting and storing contextual information about users, their communications, and relationships before the search query is executed. This pre-computed contextual data is then used to enhance search accuracy when referential terms are encountered, resolving the contradiction between simple implementation and accurate search results.
Solution Approach 2:
The patent introduces an intermediary contextual information extraction and storage mechanism that sits between the user data and the search query processing. This intermediary layer extracts meaningful contextual relationships from raw communication data and stores them for later use in enhancing search accuracy, thereby improving measurement precision without requiring complete system redesign.
2Productivity
If explicit user input is required for contextual information, then the system is simpler to operate, but the search process becomes more time-consuming
Solution Approach 1:
The system implements self-service by automatically extracting and maintaining contextual information about users, their communication patterns, and relationships without requiring explicit user input. The system serves itself by continuously learning from communication data and updating its contextual models, enabling fast and accurate search results while maintaining ease of operation.
Solution Approach 2:
The system uses feedback mechanisms where search results and user interactions continuously refine the contextual information stored in the system. This feedback loop allows the system to improve its understanding of user contexts over time, enabling faster and more accurate search without requiring explicit user input for each search operation.
3Measurement precision
If contextual information is extracted and stored for each user, then search accuracy for referential terms improves, but the data processing complexity increases
Solution Approach 1:
The system applies segmentation by organizing contextual information into distinct, manageable segments specific to each user. Instead of processing all user data uniformly, the system segments contextual extraction and storage by user identifier, allowing for targeted processing and retrieval of relevant contextual information during search operations, thereby reducing overall processing complexity while maintaining high accuracy.
Data Source
AI summary
One example provides, on a computing device, a method comprising iteratively receiving, from one or more collaborative communications applications, user data comprising communications of a user, analyzing the received user data to extract contextual information regarding the user, and sending the contextual information to a contextual information data store for the user. The method further comprises receiving a search query originating from within the collaborative communications application, parsing the search query to identify a referential search term, querying the contextual information data store for the user with the referential search term to identify previously stored contextual information regarding the user associated with the referential search term, and sending the previously stored contextual information identified to the collaborative communications application from which the search query was received.