Object Evaluation Reports with Language Models and Graph Neural Networks
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Solution Overview
Problem
Existing report generation methods are time-consuming, inefficient, and error-prone, especially for large amounts of object data, and they often fail to produce comprehensive reports due to manual effort and limited analysis of specific objects.
Innovation Solution
A method utilizing a language model and a graph neural network to generate reports based on user evaluations of objects, incorporating relationships between multiple objects to enhance analysis and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual effort is used to analyze object data and generate reports, then the analysis can be performed with simple tools, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems including language models for text generation and graph neural networks for relationship analysis. This substitution enables efficient processing of large datasets without manual intervention, directly resolving the contradiction between simplicity and efficiency.
Solution Approach 2:
The system performs self-service by automatically generating reports through automated data processing pipelines. The language model generates text content and the graph neural network analyzes relationships without human intervention, enabling the system to serve itself in producing comprehensive reports efficiently.
2Measurement precision
If manual report generation methods are used, then the process is simple to implement, but the reports lack comprehensiveness and accuracy
Solution Approach 1:
The patent segments the report generation process into distinct functional components: language model for text generation, graph neural network for relationship analysis, and integration module for combining results. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system combines multiple AI technologies (language models and graph neural networks) into a composite analytical system. This composite approach leverages the strengths of different models to achieve comprehensive and accurate analysis that neither model could achieve alone, resolving the contradiction between accuracy and complexity.
3Adaptability or versatility
If analysis focuses on specific objects only, then the process remains manageable, but the reports fail to provide comprehensive insights across multiple objects
Solution Approach 1:
The graph neural network is designed with universal applicability to analyze relationships across multiple objects simultaneously. The system can handle diverse object types and relationship structures through a unified framework, enabling comprehensive multi-object analysis without proportionally increasing complexity.
Data Source
AI summary
The present disclosure relates to a method, a device, and a computer program product for generating a report. A method in an illustrative embodiment includes: acquiring object data associated with a user's evaluation of an object, generating first text of the object by a language model according to the object data, and generating a report according to the first text and a graph neural network, wherein the graph neural network is associated with a plurality of objects. In this way, a report on an object can be generated by a machine, which is more convenient and time-saving and improves accuracy and efficiency; and a plurality of other objects can be taken into account according to a report on object data of one object, so that a more comprehensive analysis result can be obtained.