Natural Language Response via Concatenated Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interpreting and generating responses to natural language commands is challenging due to the vast amount of relevant information, making it difficult to efficiently identify and concatenate relevant data sets into a coherent response.
Innovation Solution
An apparatus and method that utilize an input device, processor, and memory to identify relevant data sets, generate graphs for each set, concatenate them into a concatenated graph, and produce a response based on this graph, assisted by a neural network for efficient response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If relevant data sets are identified and concatenated into a graph to generate responses, then the completeness and accuracy of the response is improved, but the complexity of the processing system increases
Solution Approach 1:
The patent segments the response generation process into distinct modules: data set identification module, graph generation module, and response synthesis module. Each module handles a specific aspect of the task, making the overall complex system manageable and maintainable while achieving high response accuracy through coordinated operation of these segmented components.
Solution Approach 2:
The patent introduces graphs as an intermediary data structure between the identified data sets and the final response. These graphs serve as a mediator that organizes and represents relationships among data elements, enabling the system to process complex information systematically while improving response accuracy without proportionally increasing system complexity.
2Loss of information
If multiple relevant data sets are concatenated into a concatenated graph, then the completeness of information in the response is improved, but the time required to process and generate the response increases
Solution Approach 1:
The patent performs preliminary actions by pre-identifying and organizing relevant data sets into graph structures before actual response generation. This preliminary organization of data into meaningful graphical representations allows the system to quickly retrieve and synthesize complete information when a response is needed, reducing the time penalty associated with processing multiple data sets.
Solution Approach 2:
The patent transforms flat data sets into multi-dimensional graphs that capture relationships and contexts. This dimensional transformation allows the system to organize comprehensive information in a structured format that can be efficiently navigated and processed, maintaining information completeness while improving processing speed through better data organization.
Data Source
AI summary
For generating a response to a natural language command based on a concatenated graph, a processor identifies one or more relevant data sets in response to a natural language command received from an input device. Each relevant data set includes one of a subject of the natural language command and a subject of another relevant data set. The processor further generates a graph for each of the one or more relevant data sets and concatenates the graphs into a concatenated graph. In addition, the processor generates a response to the natural language command based on the concatenated graph.