Digital Assistant Response Aggregation via Historical Interaction Segmentation
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Solution Overview
Problem
Existing information processing systems, particularly those involving digital assistants, lack the capability to accurately and efficiently utilize historical interaction data to enhance user interactions, leading to limited generalization and effectiveness in handling various tasks.
Innovation Solution
Generate aggregation information based on historical interaction data between a target object and service components, and utilize this information to improve interactions with digital assistants by providing tailored responses and content based on time and content creation requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical interaction information is utilized to enhance digital assistant interactions, then interaction accuracy and relevance are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments historical interaction information into structured aggregation information with specific fields (user_id, service_component, interaction_type, time_stamp). This segmentation organizes raw data into manageable units that can be efficiently processed and matched, improving accuracy without proportionally increasing system complexity.
Solution Approach 2:
The system performs preliminary processing by pre-generating aggregation information from historical interactions before actual digital assistant interactions occur. This advance preparation stores processed interaction patterns in a ready-to-use format, reducing real-time processing complexity while maintaining high interaction accuracy.
2Manufacturing precision
If aggregation information is generated from historical interaction data, then content relevance and quality are improved, but data processing time and computational resources increase
Solution Approach 1:
The system generates aggregation information in advance from historical interaction data and stores it for future use. This preliminary generation eliminates the need for real-time analysis during actual digital assistant interactions, significantly reducing data processing time while maintaining high content quality through pre-computed relevant information.
Solution Approach 2:
The patent creates simplified copies of historical interaction data in the form of aggregation information structures. These copied and condensed representations retain essential patterns and preferences without requiring processing of the full original datasets, reducing computational resources and time while preserving content quality.
3Adaptability or versatility
If time information is used to match aggregation information for providing content, then interaction personalization is improved, but processing complexity and response time requirements increase
Solution Approach 1:
The patent applies local quality by incorporating time information specifically into the aggregation information structure at relevant positions. Instead of processing entire datasets, only the time-related fields are used for matching personalization, achieving customized interactions without proportionally increasing overall processing complexity and maintaining response time efficiency.
Data Source
AI summary
The embodiment of the invention provides a method, apparatus, device and a storage medium for processing information. In the method, aggregation information associated with a target object is generated based on historical interaction information of the target object, the historical interaction information being generated based on a set of interaction events between the target object and at least one service component; and the aggregation information is provided for interaction between the target object and a digital assistant. Thus, by generating a response to the digital assistant based on the historical interaction information between the target object and the service component, embodiments of the present disclosure can improve the quality of the response content generated by the digital assistant.