App privacy policy text analysis method based on individual insurance compliance prompt words

By constructing a dedicated dataset and a large-scale foundational model for domain-adaptive training, and combining chain-thinking prompt templates and a compliance judgment logic matrix, the inefficiency and missed detection issues in the compliance analysis of APP privacy policies in existing technologies are resolved, achieving efficient and accurate compliance auditing and actionable rectification suggestions.

CN122433736APending Publication Date: 2026-07-21ASPIRE TECH (SHENZHEN) LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ASPIRE TECH (SHENZHEN) LTD
Filing Date
2026-04-20
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing technologies for APP privacy policy compliance analysis suffer from low efficiency, high subjectivity, omissions, and inability to delve into details. Furthermore, compliance testing products cannot be automatically updated and lack rectification guidance.

Method used

We construct a text analysis method for APP privacy policies based on individual insurance compliance prompts. By building a dedicated dataset and a large-scale foundational model for domain-adaptive training, we use chain-thinking prompt templates and a compliance judgment logic matrix to perform integrated compliance analysis and generate structured diagnostic output.

Benefits of technology

It enables efficient and accurate compliance auditing of APP privacy policies, automatically identifies hidden compliance risks and provides actionable rectification suggestions, thereby improving the accuracy and traceability of compliance analysis.

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Abstract

The application relates to the technical field of data processing and privacy policy, and discloses an APP privacy policy text analysis method based on individual protection compliance prompt words, which comprises the following steps: constructing a special data set for privacy policy text compliance detection; performing field adaptability training and iterative optimization on a base large model; decomposing multi-source regulation requirements into a four-dimensional detection index system; guiding the optimized base large model by using a chain thinking prompt word template, and performing integrated compliance analysis on the privacy policy text according to the four-dimensional detection indexes and the linkage verification rules of the detection indexes; automatically ruling and risk level dividing the integrated compliance analysis result according to a preset compliance judgment logic matrix, and generating a structured diagnostic output; and generating a privacy policy compliance evaluation report according to the risk level division and the generated diagnostic output. By using the application scheme, efficient, accurate and full-link traceable compliance audit capability can be formed.
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