Strengthening human innovation and excellence

Abstract

Computer-aided system for the pseudonymized analysis of individual innovation characteristics, including: • a data acquisition module (1) with a web-based user interface for entering test data and questionnaires via encrypted connections, • a pseudonymization module (2) with a hash algorithm for the irreversible conversion of identification data into eight-digit alphanumeric identifiers using a salting function, • an AI analysis module (3) with API-based integration of machine learning methods for automated classification of input data, wherein explainable AI mechanisms provide comprehensible explanations of the analysis results, • a reflection module (4) with a database-based dashboard for interactive visualization of the analysis results using configurable diagrams, • a communication module (5) for encrypted anonymous messages between pseudonymized user identifiers and research accounts, wherein the system implements role-based access control and processes personal data exclusively in pseudonymized form.

Description

Technical field

[0001] The invention relates to the field of computer-aided data processing systems for the automated analysis of structured input data. It particularly includes methods and devices for secure data acquisition, irreversible pseudonymization, machine data classification, and encrypted communication systems. State of the art

[0002] German patent DE10218212A1 discloses a method for the automated testing of software with graphical user interfaces. The system generates test cases based on specified behaviors, but has the following technical shortcomings: It lacks pseudonymization of user data, real-time data analysis, and bidirectional anonymous communication.

[0003] From DE10118502C1, a system for capturing system information in distributed software systems is known, which enables a semantic assignment of communication messages. However, this system also lacks irreversible anonymization of the data sources, machine analysis with explainable results, and structured visualization options.

[0004] Well-known online data collection systems use personal authentication methods, which eliminates anonymity. These systems provide static analyses without real-time data processing and without the possibility of anonymous two-way communication between different system users. Object of the invention

[0005] The task is to provide a computer-based system that: • ensures complete and irreversible pseudonymization of input data, • provides real-time data analysis with verifiable results, • enables encrypted, anonymous, bidirectional communication, • all three technical processes combined in an integrated data processing system. Solution to the task

[0006] The task is solved by a computer-based system with a multi-layered client-server architecture, which includes the following modules: 1. Data acquisition module (1): Web-based input interfaces for structured data acquisition 2. Pseudonymization module (2): Irreversible hashing algorithm with salting functions 3. Analysis module (3): Machine learning methods with explainable AI components 4. Visualization module (4): Database-based evaluation and display components 5. Communication module (5): Encrypted anonymous messaging systems Advantages • Technical security: Irreversible pseudonymization prevents data tracing • Algorithmic transparency: Explainable-Al enables comprehensible analysis results • System integration: All components operate within a closed, secure data processing system. • Scalability: Modular architecture enables flexible system expansions Example of implementation

[0007] A user enters identification data via the data acquisition module (1). The pseudonymization module (2) converts this data into an eight-digit alphanumeric identifier using SHA-256 hashing with salting. Structured input data is classified by the analysis module (3) using machine learning methods, with feature importance calculations generating comprehensible explanations. The visualization module (4) presents the results via a database-driven dashboard. The communication module (5) enables encrypted messages between pseudonymized identifiers without revealing identities. Detailed technical design 1. Frontend layer (data acquisition module 1)

[0008] The data acquisition module (1) implements a web-based single-page application architecture. Data is acquired via dynamic input forms with client-side real-time validation to ensure data integrity. Erroneous or incomplete entries are detected and corrected before transmission.

[0009] Communication between frontend and backend takes place via encrypted HTTPS connections using SSL / TLS certificates with at least 256-bit encryption. 2. API Gateway Layer

[0010] An intermediate API gateway is implemented: • JSON Web Token (JWT) based session management with configurable expiration times • Algorithmic rate limiting to prevent system overload • Intelligent request routing based on URL paths and HTTP methods 3. Backend processing layer Pseudonymization module (2)

[0011] The pseudonymization module (2) implements a multi-stage anonymization process: 1. Hash generation: Input data is hashed using the SHA-256 algorithm with system-specific salt values. 2. ID generation: An eight-digit alphanumeric identifier is extracted from the hash using a deterministic algorithm. 3. Collision detection: A verification mechanism prevents duplicate assignments through automatic regeneration in case of hash collisions. Analysis module (3)

[0012] The analysis module (3) integrates machine learning methods via REST API interfaces and implements: • Normalization and structuring of input data according to defined schemas • Application of trained models for automated data classification • Feature importance calculations to generate comprehensible explanations of the classification results 4. Data storage layer Visualization module (4)

[0013] The visualization module (4) uses a NoSQL document database with: • AES-256 encryption for data at rest • Strategic index creation for optimized query performance • Horizontal partitioning for scalability with growing data volumes • WebSocket-based live updates of visualizations • Dynamic chart generation based on configurable parameters 5. Communication Layer Communication Module (5)

[0014] The communication module (5) implements a mailbox architecture with: • Message routing to pseudonymized identifiers without identification • End-to-end encryption of message content • Time-based automatic deletion after configurable time periods 6. Security layer

[0015] System-wide security is ensured through the following technical measures: • Role-based access control (RBAC) with differentiated permissions • Audit logging of security-relevant events without storing personal data • Automated detection of anomalous access patterns using pattern recognition algorithms Data flow

[0016] The processing procedure takes place in the following steps: 1. User authenticates themselves via the data acquisition module (1) using identification data 2. Pseudonymization module (2) generates irreversible alphanumeric identifier 3. Structured input data is encrypted before being transmitted to the backend. 4. Analysis module (3) processes the data using machine learning methods. 5. Classification results are stored in the database under a pseudonymized identifier. 6. Visualization module (4) generates interactive representations of the analysis results 7. Communication module (5) enables anonymous data exchange between system users Technical innovation

[0017] The invention lies in the systemic combination of three technical data processing methods: 1. Irreversible pseudonymization: SHA-256 hashing with salting generates reproducible but untraceable identifiers for secure data processing. 2. Real-time data analysis: API-based integration of machine learning methods with feature importance calculations for traceable classification results. 3. Anonymous data communication: A mailbox system without identity storage enables bidirectional communication between pseudonymized system users.

[0018] The technical innovation lies not in the individual processes, but in their systemic integration into a closed, secure data processing system with consistent pseudonymization and real-time processing. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] DE 10218212A1

[0002] DE 10118502C1

[0003]

Claims

[1] Computer-aided system for pseudonymized analysis of individual innovation characteristics, comprising: • a data acquisition module (1) with a web-based user interface for entering test data and questionnaires via encrypted connections, • a pseudonymization module (2) with a hash algorithm for the irreversible conversion of identification data into eight-digit alphanumeric identifiers using a salting function, • an AI analysis module (3) with API-based integration of machine learning methods for automated classification of input data, with explainable AI mechanisms providing comprehensible explanations of the analysis results, • a reflection module (4) with a database-based dashboard for interactive visualization of the analysis results using configurable diagrams, • a communication module (5) for encrypted anonymous messages between pseudonymized user identifiers and research accounts, wherein the system implements role-based access control and processes personal data exclusively in pseudonymized form. [2] System according to claim 1, characterized by , that the pseudonymization module (2) uses the SHA-256 algorithm with system-specific salt values ​​for hash generation. [3] System according to claim 1 or 2, characterized by , that the AI ​​analysis module (3) implements feature importance calculations to generate comprehensible explanations. [4] System according to any one of the preceding claims, characterized by , that the reflection module (4) provides WebSocket-based live updates of the visualizations. [5] System according to any one of the preceding claims, characterized by , that the communication module (5) implements time-based automatic deletion of messages. [6] System according to any one of the preceding claims, characterized by that data is stored using AES-256 encryption at rest. [7] Computer program product comprising program code for carrying out the process steps according to any one of claims 1-6. [8] Data carrier containing program code according to claim 7.