Context-Aware Digital Assistant Using Knowledge Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital assistants are limited in their ability to perform sophisticated operations for users, as they struggle to manage information and activities effectively across multiple devices and contexts.
Innovation Solution
A contextually aware digital assistant system that uses natural language processing to understand user inputs, interact with web services, and perform actions by combining user context from foreground and background knowledge graphs, allowing for tasks like completing sales transactions or booking trips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current digital assistants use basic search functionality, then they can provide simple information retrieval, but they cannot perform sophisticated operations for users
Solution Approach 1:
The system segments the digital assistant functionality into distinct modules: a natural language processing module that parses user input into structured queries, a context analysis module that retrieves relevant information from knowledge graphs, and an action execution module that performs operations based on parsed intent. This modular architecture enables sophisticated operations while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces an intermediary layer between user input and system execution: a structured query representation that acts as a mediator. The natural language processing module transforms casual user speech into formal structured queries, which then serve as the intermediary representation that the action execution module can reliably process. This intermediary layer bridges the gap between ambiguous natural language and precise system operations.
2Adaptability or versatility
If digital assistants access user information from multiple devices, then they can provide comprehensive services, but they struggle to manage information and activities effectively
Solution Approach 1:
The system implements a universal knowledge graph architecture that serves multiple functions: storing user profile information, maintaining activity contexts across devices, retrieving background information for query disambiguation, and providing contextual data for action execution. This multi-functional knowledge graph eliminates the need for separate information management systems for each device or function.
Solution Approach 2:
The system employs feedback mechanisms where the context analysis module continuously refines query understanding based on retrieved information from knowledge graphs. The structured query representation is iteratively improved by incorporating contextual data about user preferences, recent activities, and device states, ensuring that actions are executed with accurate understanding of the multi-device context.
3Measurement precision
If digital assistants use simple keyword matching, then they can process queries quickly, but they lack deep semantic understanding
Solution Approach 1:
The system performs preliminary action by pre-structuring user input into standardized query representations with defined fields for intent, entities, and parameters. This upfront structuring, combined with pre-computed context information from knowledge graphs, eliminates the need for time-consuming semantic analysis during query execution, achieving both deep understanding and fast processing.
Solution Approach 2:
The patent transforms the query processing approach by changing parameters from unstructured keyword matching to structured parameter extraction. The natural language processing module identifies and extracts specific parameters (entities, values, relationships) from user input, converting ambiguous text into precise structured data that can be efficiently matched against knowledge graph schemas, thereby achieving deep semantic understanding with optimized processing time.
Data Source
AI summary
One embodiment of the present invention provides a system for providing context-based web services to a user. During operation, the system may receive a sentence as input from a user. The system may determine a sentence structure of the sentence, and determine whether there is an entry in a database corresponding to the sentence structure. Responsive to determining that there is no entry in the database corresponding to the sentence structure, the system may engage in a dialog with the user. The system may extract one or more parameters of the sentence based on information from the dialog. The system may obtain contextual and background information associated with the parameters. The system may then determine a set of arguments based on the parameters and the contextual and background information, and interact with web services to perform an action and provide a response to the user.