Natural Language Processing Engine for Named Entity Capture
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Solution Overview
Problem
Users face inefficiencies in inputting text repeatedly across different applications, leading to wasted time and effort, as they need to type similar information multiple times, such as names, dates, and locations, without seamless integration or automation.
Innovation Solution
A natural language processing engine is used to analyze text inputs and identify named entities associated with categories, allowing these entities to be automatically input into relevant text fields across applications, reducing the need for repetitive typing by providing suggestions based on semantic relationships and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually type text information in multiple applications, then text input accuracy is maintained, but user time and effort are wasted due to repetitive typing
Solution Approach 1:
The system performs preliminary action by automatically capturing and storing named entities (such as names, locations, dates) from natural language text inputs. When a user subsequently types in any application, the system proactively retrieves these pre-captured entities and presents them as suggestions, allowing the user to insert relevant information with a single selection rather than typing it repeatedly.
Solution Approach 2:
The system creates copies of named entities extracted from text and stores them in a database with associated category information. These copies can then be retrieved and inserted into text fields across different applications, eliminating the need for users to retype the same information multiple times while maintaining accuracy through the use of stored copies.
2Extent of automation
If a natural language processing engine analyzes text inputs to identify named entities, then automated text input across applications is enabled, but system complexity increases
Solution Approach 1:
The system segments the text processing task into distinct components: a natural language processing engine that identifies named entities, a database that stores entities with category information, and an application layer that retrieves and inserts entities. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while enabling automated text input functionality.
3Manufacturing precision
If named entities are automatically identified and suggested based on semantic relationships, then input accuracy is improved, but processing time for text analysis increases
Solution Approach 1:
The system performs preliminary action by pre-processing and categorizing named entities during initial text inputs. The NLP engine extracts entities and stores them in a database with associated category information before they are needed. When users subsequently need to input text, the system quickly retrieves pre-categorized entities based on the current context, providing accurate suggestions without performing full text analysis at the moment of input, thus maintaining accuracy while reducing processing time.
Data Source
AI summary
Disclosed methods and systems are directed to concept identification and capture. The methods and systems may include receiving, by a device, a first natural language input comprising one or more terms, and analyzing the first natural language input via a natural language processing engine to identify one or more named entities associated with the one or more terms, wherein each of the one or more named entities is associated with at least one category of a plurality of categories. The methods and systems may also include detecting a text field configured to receive text, the text field being associated with one of the plurality of categories, and inputting into the text field one of the one or more identified named entities based on the text field being associated with a same category as the one of the one or more named entities.