Agent Interaction System for Search Result Enhancement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Internet search engines often fail to provide users with specific information due to the vast volume of data, leading to irrelevant results or omission of valuable information, especially when searches are highly specific.
Innovation Solution
An agent interaction system that allows users to request additional information not readily available in search results by identifying and ranking registered agents based on selection criteria, assigning them timeslots, and sending interaction requests for responses, thereby monetizing business leads through a bid-based ranking system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a simple single keyword search is performed, then the search speed is fast, but the quantity of relevant results is insufficient
Solution Approach 1:
The patent segments the search process into multiple stages: initial broad keyword search, followed by iterative refinement with additional keywords based on user feedback and agent interactions. This allows the system to start fast with broad terms then progressively narrow down to find more relevant results without requiring the user to anticipate the perfect query from the start.
Solution Approach 2:
The system performs preliminary actions by pre-identifying potential relevant agents and resources before the user completes their search. Agents are pre-screened and ranked based on their expertise and availability, so when a user submits a query, the most relevant results are already prepared and can be quickly presented.
2Quantity of substance
If a detailed multiple keyword search is performed, then the quantity of relevant results is improved, but the search complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing user queries, identifying relevant agents and resources, and presenting refined search results without requiring the user to manually construct complex multi-keyword searches. The agent interaction system automatically iterates through potential matches and presents the best results, freeing users from the complexity of crafting detailed search queries.
Solution Approach 2:
The patent introduces agents as intermediaries between users and information resources. These agents act as mediators who understand domain-specific knowledge and can interpret user needs, translating simple user queries into comprehensive search strategies and presenting results in an easily digestible format, thereby reducing search complexity for users.
3Measurement precision
If iterative searches with progressively narrowing keywords are performed, then the precision of results is improved, but the loss of time increases
Solution Approach 1:
The system performs preliminary ranking and filtering of agents based on their profiles, expertise areas, and availability before users submit queries. This pre-positioning allows the system to quickly present the most relevant agents for any given query without requiring multiple iterative searches, thereby achieving high precision results in a single search operation.
Solution Approach 2:
The patent replaces the manual mechanical process of iterative keyword refinement with an automated intelligent system that uses agent profiles, machine learning algorithms, and natural language processing to automatically iteratively refine search results. This substitution eliminates the time users would spend manually adjusting keywords while maintaining or improving result precision.
4Productivity
If more agents are contacted simultaneously, then the productivity of information gathering is improved, but the device complexity increases
Solution Approach 1:
The patent segments the agent population into distinct categories and tiers based on their expertise, availability, and ranking scores. Users can contact multiple agents simultaneously within each segment, and the system manages these parallel interactions through structured communication protocols and automated response aggregation, thereby scaling productivity while controlling complexity through organized segmentation.
Data Source
AI summary
An agent interaction request received from a user of a client device is sent to one or more identified agents based on agent selection criteria. Agent selection criteria can include an agent bid amount. One or more responses to the agent interaction request can be received from the identified agents and sent to the user.