First Introduction in Automotive R&D Department: "AI IPGenius" Integrates Fragmented Data — Visualizing Development Stagnation Factors by Cross-Analyzing Test, Design, and Defect Information

LegalTech Inc. (Minato-ku, Tokyo; Representative Director: Tomoyuki Hirai) announced that its "AI IPGenius" has been introduced in the research and development department of an automotive manufacturer (company name undisclosed) and is being utilized for cross-sectional analysis of dispersed data such as test, design, and defect information.

This marks the first introduction for the company in an automotive R&D department. It supports the visualization of development stagnation factors and the advancement of technical review by cross-sectionally analyzing fragmented unstructured data.

This initiative is a pioneering case in the automotive R&D domain for integrally utilizing fragmented information to identify development bottlenecks.

**Background**

In the research and development sites of automotive manufacturers, data of different formats and granularities are accumulated in the design, evaluation, and quality departments. Although this information is interrelated, opportunities for integrated reference are limited.

As a result, identifying the root causes of defects and considering design improvements often requires manually cross-referencing information from multiple departments, which tends to be time-consuming and labor-intensive.

Furthermore, knowledge from past test results and defect countermeasures is often not sufficiently reused, leading to similar issues being addressed in an individually optimized manner.

**Use Case: AI Cross-Analyzes Research Notes and Meeting Minutes to Extract the Core of Technology**

In the research and development department of an automotive manufacturer (company name undisclosed), the following materials were input into AI IPGenius for utilization:

* Research notes * Meeting minutes * Prototype logs * Past technical documents

AI IPGenius on IDX cross-sectionally analyzed these to organize:

* Technical improvement points * Potentially novel points * Structural connections to similar themes * Relationships with past verification results

Researchers used the extracted points as a starting point for discussions, theme review, and reorganization.

Additionally, a system was established to link the extracted data with MyTokkyo.Ai for immediate similar technology searches.

**About AI IPGenius on IDX**

AI IPGenius is a knowledge base that analyzes unstructured data accumulated within companies to support the structuring and utilization of technical information.

Its main features are as follows:

* **Unstructured Data Analysis** * Capable of cross-sectional analysis of diverse data formats, including PDF, Word, PowerPoint, image data, and scanned documents. * **Technical Point Extraction** * Organizes the relationships between technical issues, solutions, and effects, and suggests invention candidates and improvement perspectives. * **Similar Technology Search** * Supports searching for related technologies and past cases based on extracted technical elements. * **Shared File Analysis** * Comprehensively grasps internal documents such as design data, quality data, and test reports, suppressing the compartmentalization of knowledge.

**Expected Effects**

The utilization of AI IPGenius is expected to bring about the following effects:

* Reduction in information search time during development theme exploration. * Promotion of technical information organization and visualization. * Support for invention extraction and patent review. * Improved efficiency in theme review by identifying development stagnation factors. * Promotion of knowledge sharing between departments.

All of these are positioned as aids and are premised on parallel use with existing processes.

**Future Development**

Moving forward, with an eye towards expanding utilization in automotive R&D and quality/defect analysis domains, we will advance the accuracy of analysis and the sophistication of knowledge extraction from unstructured data such as test, design, and defect information.

This aims to enhance decision-making in both development process improvement and quality enhancement.

**Product Page**

https://www.legaltech.co.jp/ipgenius/

**Company Overview**

Company Name: LegalTech Inc. Established: March 2021 Capital: 379 million JPY (including capital reserve) Representative Director and President: Tomoyuki Hirai Location: Toranomon 40MT Building 4F, 5-13-1 Toranomon, Minato-ku, Tokyo URL: https://www.legaltech.co.jp/

**Business Overview**

* Patent search and invention extraction platform "MyTokkyo.Ai" * Knowledge assetization knowledge base "AI IPGenius" * Confidential information sharing data room "LegalTech VDR"