Contextual Record Retrieval Using NLP and User Behavior
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Solution Overview
Problem
Existing electronic record retrieval systems face challenges in accurately locating and isolating specific information due to the vast number of electronic records, often requiring iterative search queries and resulting in considerable time and effort for users, with search results sometimes being irrelevant.
Innovation Solution
The system employs contextual retrieval methods based on natural language processing (NLP) models, user behavior analysis, and relationships between entities, enhancing search queries by appending relevant keywords and using graph searches to prioritize contextually relevant records, thereby improving search accuracy and personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional electronic search methods are used to retrieve records from a large volume of electronic records, then the system can handle vast amounts of data, but the search accuracy and relevance deteriorate, requiring iterative queries and considerable user time and effort
Solution Approach 1:
The patent introduces contextual information and user behavior data as intermediary elements between the search query and the electronic records. These intermediaries enable the system to understand the intent behind queries and match records based on contextual relevance rather than just keyword matching, thereby improving search accuracy in large volumes of records
Solution Approach 2:
The system dynamically changes search parameters by incorporating user behavior patterns, contextual data, and relevance weights. Instead of using static search criteria, the system adapts parameters based on user interactions and contextual information, enabling more accurate retrieval from large record volumes
2Productivity
If traditional search methods are used, then the system can process search queries, but the time and effort required to find specific information increases due to the need for iterative query reformulation
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing user behavior data, contextual information, and record metadata before actual search execution. This preliminary preparation enables the system to quickly retrieve relevant records without requiring users to iteratively reformulate queries, thereby reducing time loss
Solution Approach 2:
The system implements feedback mechanisms by analyzing user interactions with search results and using this information to improve subsequent search operations. The feedback loop allows the system to learn from user behavior and refine search strategies, reducing the need for iterative queries and improving overall retrieval efficiency
3Ease of operation
If basic keyword search is used, then the search system is simple to operate, but the results are often irrelevant or only thinly relevant to the user's actual information needs
Solution Approach 1:
The patent introduces contextual information and user behavior analysis as intermediary layers between the simple keyword input and the search results. These intermediaries enhance result relevance without complicating the user interface, maintaining ease of operation while significantly improving reliability of search results
Solution Approach 2:
The system performs self-service by automatically analyzing user intent, contextual data, and behavior patterns without requiring users to manually refine their search criteria. This automation maintains operational simplicity for users while dramatically improving the relevance and reliability of returned results
Data Source
AI summary
Provided are systems and methods for the contextual retrieval and contextual display of records. A search query and/or search results may be contextually enhanced based on (i) natural language processing (NLP) models, (ii) user behavior, and/or (iii) relationships between various entities involved in a search, such as between users, records, and/or fields of expertise. Contextually enhanced search results may be delivered and displayed to a user on a user interface in a contextually relevant order.