Leveraging Database Technology with a Machine-Learning Cycle
First written in FY2019. 8 of 11 entries say "Advanced", first in FY2020. Latest entry: 2026-10-15.
0 other companies' latest entry also says "Advanced".
- FY2025AdvancedAnnual report
「This nuisance-information database processes more than 5 billion telephone calls, emails and SMS messages annually (Note 4), and operates at a high level in terms of both scale and accuracy.」
The company newly disclosed the scale of processing more than 5 billion nuisance calls, emails and SMS messages annually, positioning its database technology with a machine-learning cycle as the core platform for multiple services. - FY2022AdvancedAnnual report
「We assign a nuisance score to each telephone number based on information provided by public institutions such as the police, feedback from users, including call-blocking and call-allowance information, and our own research, and store the scores in a database. Using the information stored in this database, our proprietary algorithms based on statistics and machine learning automatically identify as potential nuisance numbers telephone numbers used for crimes such as special fraud and numbers likely to be used for persistent sales calls. Our engineers then make the final decision on whether each number should be added to the nuisance telephone number list, which we create and update accordingly.」
The company detailed the database update process, covering information from public institutions, user feedback, proprietary research, scoring, machine-learning and statistical processing, and final decisions by engineers. - FY2022-Q1AdvancedAnnual report
「As a new business, we developed and began selling “Talk Book,” an AI-powered sales tool that visualizes sales conversations and enables data-driven sales.」
The company advanced to the stage of developing and selling a new AI sales tool based on its accumulated database technology expertise. - FY2021RestatedAnnual report
「We use database technology with our proprietary machine-learning cycle (Note 1) to provide security products and services that help prevent crime, enabling smartphone users to live safely and securely without having to give the matter particular thought.」
The company continues to describe its core technology for converting information from public institutions such as the police, user feedback and proprietary research into database records, then identifying nuisance telephone numbers using AI, statistics and proprietary algorithms. - FY2020AdvancedAnnual report
「We comprehensively collect information on suspicious telephone numbers through information provided by public institutions such as the police, feedback from service users and internet research. We identify nuisance telephone numbers using AI technology that assesses recurring patterns and update the nuisance telephone number list daily. As of the end of October 2020, our database contained information on more than 5.53 million telephone numbers for companies, stores and public facilities, including information on more than 30,000 nuisance telephone numbers.」
The company detailed a system that processes information from the police and other sources, user feedback and proprietary research using AI, statistics and algorithms, and expanded its telephone-number database to more than 5.53 million records. - FY2019First statedAnnual report
「In light of these social conditions, we use database technology with our proprietary machine-learning cycle to provide security products and services that help prevent crime, enabling smartphone users to live safely and securely without having to give the matter particular thought.」
The company describes as a core business capability its technology for aggregating information from public institutions, user feedback and the internet, then identifying nuisance telephone numbers using AI, statistics and proprietary algorithms.