A method and system for constructing a roche corpus for depression screening and a storage medium

By constructing the Rorschach corpus system, the problems of poor correlation of multimodal data and difficulty in version management in Rorschach test research were solved, realizing efficient integrated management of data and support for deep learning, and improving the standardization and efficiency of research.

CN122132424APending Publication Date: 2026-06-02NAT UNIV OF DEFENSE TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NAT UNIV OF DEFENSE TECH
Filing Date
2026-02-12
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing Rorschach test research suffers from poor correlation of multimodal data (speech, text, video, and encoding), difficulty in version management, low collaboration efficiency, non-standard metadata recording, and difficulty in supporting deep learning model training.

Method used

We construct a Rorschach corpus system for depression screening. By collecting and preprocessing multimodal data, we establish a core data model for structured storage, provide data association and retrieval modules, support deep learning model integration, and enable cross-modal queries and anonymized export.

Benefits of technology

It achieves high correlation and integrated management of multimodal data, improves the standardization and reproducibility of research, enhances collaboration and analysis efficiency, directly supports deep learning research, adapts to different coding systems, and has flexibility and scalability.

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Abstract

This invention proposes a method, system, and storage medium for constructing a Rorschach corpus for depression screening. It collects multimodal raw data related to the Rorschach test, and performs editing and preprocessing. A core data model is constructed to store the multimodal data and their relationships. Through a data entry and import module, the multimodal data is stored in the core data model. Through a data association and retrieval module, cross-modal relational queries and a unified view are provided. Through a data export and sharing module, the data is exported in a specified structured format. Through a deep learning model integration module, the multimodal data is used for predictive analysis and stored in the core data model. The method, system, and storage medium for constructing a Rorschach corpus for depression screening provided by this invention avoid the problems existing in current Rorschach test research data management, such as poor multimodal data correlation, difficult version management, low collaboration efficiency, non-standard metadata recording, and difficulty in supporting deep learning model training.
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