Domain-Name Framework for Digital Assistant Entity Identification
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Solution Overview
Problem
Current bot assistants lack precision in identifying entities and responders, leading to imprecise and incomplete responses to natural language requests, as they are hardwired to match specific keywords and cannot aggregate information from multiple responders effectively.
Innovation Solution
A domain-name based framework that uses unique identifiers to interpret and process natural language requests, enabling digital assistants to accurately identify entities and responders by generating requests based on ontology-defined syntax for accessing multiple responders over a network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bot assistants use hardwired keyword matching to identify responders, then the system structure remains simple, but the precision in identifying entities and responders deteriorates
Solution Approach 1:
The patent introduces an intermediary layer (natural language processing module and entity identification module) between the user input and the responder matching system. This intermediary layer translates natural language requests into structured entity identifiers, enabling precise entity and responder identification without requiring complex hardwired keyword matching throughout the entire system.
Solution Approach 2:
The patent creates a structured representation (copy) of the natural language request in the form of entity identifiers and standardized request formats. This copied structured data is then used for precise responder matching, separating the complexity of natural language interpretation from the responder identification process.
2Adaptability or versatility
If bot assistants are hardwired to match specific keywords with single responders, then the implementation process remains simple, but the adaptability to handle multiple responders and request types deteriorates
Solution Approach 1:
The patent implements a universal entity identification system that can handle multiple types of requests (weather, news, entertainment, etc.) and multiple responders through a common interface. The natural language processing module and entity identifier work universally across different request types, eliminating the need for separate implementations for each responder or request category.
Solution Approach 2:
The patent segments the responder identification process into independent modular components: natural language processing module, entity identification module, and responder matching module. This segmentation allows each module to be developed and maintained independently, improving adaptability while keeping the implementation process manageable through clear separation of concerns.
3Loss of information
If bot assistants cannot aggregate information from multiple responders, then the system complexity remains low, but the completeness of responses to natural language requests deteriorates
Solution Approach 1:
The patent merges information from multiple responders by transmitting the standardized request to a set of responders and aggregating their responses. The system combines results from multiple sources to provide complete and comprehensive answers to natural language requests, such as aggregating weather information from multiple weather responders or entertainment options from multiple entertainment responders.
4Adaptability or versatility
If bot assistants lack standardized integration methods, then each responder implementation remains simple and independent, but the ability to integrate new responders without specialized applications deteriorates
Solution Approach 1:
The patent establishes a universal standardized interface that new responders can implement to integrate with the system. By defining standard request formats and response structures, the system enables easy integration of new responders without requiring specialized application development, while the integration framework manages the complexity of handling diverse responders through consistent interfaces.
Data Source
AI summary
In one embodiment, a domain-name based framework implemented in a digital assistant ecosystem uses domain names as unique identifiers for request types, requesting entities, responders, and target entities embedded in a natural language request. Further, the framework enables interpreting natural language requests according to domain ontologies associated with different responders. A domain ontology operates as a keyword dictionary for a given responder and defines the keywords and corresponding allowable values to be used for request types and request parameters. The domain-name based framework thus enables the digital assistant to interact with any responder that supports a domain ontology to generate precise and complete responses to natural language based requests.