Enterprise Search Context Dialog for Personalization
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Solution Overview
Problem
Enterprise search systems struggle to provide personalized and accurate results due to varying user intents over time and different data sources, leading to inefficiencies in query refinement and resource utilization.
Innovation Solution
The system leverages periodically updated user context to understand query intent and facilitate multi-modal interactions, allowing users to refine queries and provide feedback, which updates the user context and improves query intent understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprise search systems index data specific to each enterprise user from various enterprise sources, then the personalization of search results is improved, but the device complexity and data processing requirements worsen
Solution Approach 1:
The patent segments the enterprise data into multiple indexed sources (files, communications, contacts, etc.) and organizes them by user-specific contexts. This segmentation allows the search system to efficiently retrieve personalized results without processing the entire enterprise data corpus for each query, thereby reducing computational complexity while maintaining personalization.
Solution Approach 2:
The system performs preliminary indexing of enterprise data sources before search queries are submitted. By pre-processing and organizing data into searchable indexes categorized by user context, the system avoids the need to process raw data during query execution, significantly reducing real-time processing requirements while enabling personalized results.
2Measurement precision
If the system leverages periodically updated user context to understand query intent, then the accuracy of search results is improved, but the loss of time for context updates worsens
Solution Approach 1:
The system implements periodic updates of user context information rather than continuous real-time updates. Context is refreshed at scheduled intervals or triggered by significant user actions, ensuring search accuracy is maintained while avoiding excessive processing overhead and time loss associated with constant context synchronization.
Solution Approach 2:
The user context update mechanism operates autonomously, automatically refreshing context information based on predefined triggers or schedules without requiring manual intervention. This self-service approach minimizes the time burden on users while maintaining accurate context for improved search result precision.
3Ease of operation
If the system treats search session as a dialog between user and digital assistant with multi-modal interaction, then the ease of operation is improved, but the device complexity worsens
Solution Approach 1:
The search system is designed with multi-functional capabilities that handle various interaction modes (text queries, voice commands, follow-up questions, feedback mechanisms) through a unified dialog-based architecture. This universal design allows the same system framework to support multiple interaction types, improving ease of operation without proportionally increasing complexity through separate specialized systems.
Solution Approach 2:
The digital assistant serves as an intermediary layer between the user and the enterprise search index. It manages the dialog flow, interprets user intent, and coordinates multi-modal interactions, thereby simplifying the user interface complexity while maintaining sophisticated search capabilities through the mediator's structured processing.
4Productivity
If feedback mechanisms are implemented to update user context, then the productivity of search operations is improved, but the loss of time for feedback processing worsens
Solution Approach 1:
The system implements feedback mechanisms where user interactions with search results (selections, rejections, refinements) are captured and used to update user context. This feedback loop continuously improves search accuracy and productivity by adapting to user preferences and behaviors, while the automated nature of the process minimizes time loss through efficient processing of feedback signals.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for enterprise search that leverage periodically updated user context of an enterprise user for intent understanding and treat a search session as a dialog between the user and a digital assistant to allow multi-modal interaction. For example, a query input by the user may be received from a client application having search functionality and an integrated assistant. A current state of the user context may be leveraged to understand the query. Based on the understanding, one or more responsive entities may be retrieved from an enterprise search index as results. Based on the results, a response may be generated that includes the results and a prompt to cause the user to further refine the query and/or provide feedback. The response may be provided to the client application for output as part of a search session dialog between the user and assistant.