Search Query Phase Classification Using Generative AI
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Solution Overview
Problem
Existing technologies lack the ability to appropriately analyze the relationship between users and objects indicated by search queries, limiting effective utilization in applications such as marketing funnel analysis and advertisement timing.
Innovation Solution
An information processing apparatus that utilizes generative AI to classify search queries into phases and generate descriptive content, displaying the phase classification results with associated description content to enhance understanding of user behavior and trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generative AI is used to phase-classify search queries and generate description content, then analysis accuracy of user behavior is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary phase classification of search queries using generative AI before detailed analysis. By pre-categorizing queries into phases (awareness, consideration, decision), the system prepares structured data in advance that accelerates subsequent marketing analysis and reduces real-time processing requirements.
Solution Approach 2:
The patent segments search queries into multiple distinct phases (awareness, consideration, decision) based on user intent. This segmentation allows the system to apply different analysis methods to each phase, improving overall analysis accuracy while enabling parallel processing that reduces total processing time.
2Loss of information
If search queries are classified into multiple phases with detailed description content, then understanding of user intent is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary classification layer that translates complex search query data into standardized phase categories (awareness, consideration, decision). This intermediary structure simplifies the representation of user intent while preserving essential information, making the data more manageable for marketing analysis without losing critical insights.
Solution Approach 2:
The system transforms search query characteristics into phase classification parameters. By changing the parameter representation from raw query text to structured phase labels with description content, the system maintains comprehensive understanding of user intent while reducing data complexity for subsequent processing and analysis.
3Productivity
If phase classification and description generation are performed for all search queries, then marketing analysis effectiveness is improved, but processing cost increases
Solution Approach 1:
The patent applies phase classification and description generation selectively to search queries that meet specific criteria rather than processing all queries uniformly. By focusing computational resources on high-value queries (e.g., those indicating strong purchase intent or unique user behaviors), the system achieves effective marketing analysis while reducing overall processing costs and energy consumption.
Data Source
AI summary
An information processing apparatus according to the present application includes a specifying unit, a processing unit, and a provision unit. The specifying unit specifies search queries of a plurality of users using a predetermined query for a search. The processing unit causes generative AI to classify the search queries into a plurality of phases and to generate description content describing the phase for each of the phases by inputting information regarding the search queries specified by the specifying unit to the generative AI. The provision unit provides information for displaying information indicating the plurality of phases classified by the generative AI together with the description content generated by the generative AI.