ダイオーミウラ Product Lineup

In regional recruitment, it is important to approach the areas where the target job seekers are actually active or places they are likely to be interested in. Particularly in recruitment activities for companies and stores rooted in the community, effective outreach to local residents and those commuting to or from specific areas is required. However, traditional advertising methods have faced challenges in achieving precise targeting based on such offline behaviors. "GEO-targeting advertising" utilizes location data obtained from GPS and other sources to enable targeting based on offline behaviors. This allows for accurate approaches to the target demographic in regional recruitment. 【Usage Scenarios】 - Distribution of job advertisements to specific regions or around stores - Approaching individuals who have previously visited specific facilities (e.g., commercial complexes, event venues) - Targeting job seekers based on estimated residential or work areas 【Effects of Implementation】 - Increased efficiency in recruitment activities focused on the region - Improved reach to highly interested job seekers - Optimization of recruitment costs

In quality control within the manufacturing industry, accurate identification of products and ensuring traceability are required. Particularly in environments where a wide variety of labels and forms are used, visual confirmation can be time-consuming, and there is a possibility of errors occurring due to handwritten records. Additionally, the differences in accuracy between new and veteran employees can lead to individual reliance, and managing progress and history on paper can pose challenges. These issues can increase the risk of human error and potentially lead to a decline in work quality. Our proposed 'Generative AI-OCR' addresses these challenges and contributes to improving quality control. 【Usage Scenarios】 ▪ Manufacturing lines with a wide variety of label types and formats ▪ Inspection tasks that require significant time for visual checks ▪ Situations where there is a desire to reduce errors from handwritten records ▪ Environments where there is a need to eliminate individual reliance and promote standardization 【Benefits of Implementation】 ▪ Improved work quality through error reduction ▪ Increased efficiency and standardization of tasks ▪ Visualization of work history and progress ▪ Reduction of human errors

In the retail industry, accurate inventory management and efficient shipping and receiving operations are required. In particular, the verification of various product labels and invoices, as well as inspection tasks, can take a long time due to visual checks, which also carry the risk of human error. Challenges such as mistakes from handwritten records, the complexity of managing a wide variety of label types and forms, and the personalization of tasks due to differences in accuracy between newcomers and veterans can impact the accuracy of inventory management and operational efficiency. Managing with paper can make it difficult to check progress and history, potentially hindering rapid decision-making. Our company proposes 'Generative AI-OCR,' which contributes to the efficiency of shipping and receiving operations and reduces inspection errors with flexible OCR capabilities to address these challenges. [Usage Scenarios] - Reading product labels upon arrival - Confirming product information during inventory checks - Inspection tasks during shipping - Digitizing handwritten invoices [Benefits of Implementation] - Improved work quality through reduced errors - Increased efficiency and standardization of tasks - Visualization of work history and progress - Reduction of human errors

In the logistics industry, accuracy and speed in shipping and receiving operations are essential. Particularly in environments where a wide variety of labels coexist, the time taken for visual confirmation can be significant, raising concerns about the risk of human error. Issues such as mistakes from handwritten records, the complexity of managing numerous types and forms of labels, and the variability in accuracy between new and experienced staff contribute to inefficiencies in operations. Managing with paper can make it difficult to track progress and history, hindering the overall visibility of business workflows. Our company’s "Generative AI-OCR" addresses these challenges by contributing to the efficiency of shipping and receiving operations and reducing inspection errors with flexible OCR capabilities. 【Use Cases】 ▪ Reading information such as product names, lot numbers, and quantities on labels ▪ Inspection tasks during shipping and receiving ▪ Inventory management within warehouses ▪ Confirming packages along delivery routes 【Benefits of Implementation】 ▪ Improved work quality through error reduction ▪ Increased efficiency and standardization of tasks ▪ Enhanced visibility of work history and progress ▪ Reduction of human errors

In the construction industry, it is essential to accurately grasp the progress of the site and advance projects without delays. In particular, managing paper-based records such as material delivery logs and work reports poses challenges due to manual input errors and the time required for verification. These issues can lead to delays and increased costs for the entire project. Our "Generative AI-OCR" contributes to the efficiency of progress management and the reduction of errors by automatically reading information such as item names and quantities simply by photographing various labels and forms that occur on-site. [Usage Scenarios] - Reading label information during material delivery - Digitizing work reports - Recording and verifying progress status [Benefits of Implementation] - Increased efficiency and standardization of work - Visualization of progress and history - Reduction of human errors

In the energy industry, it is essential to accurately understand the safety and operational status of equipment during inspections. In particular, the labels on equipment used in the field contain important information such as model numbers, manufacturing dates, and inspection histories. Accurately and quickly reading this information is crucial for improving the quality and efficiency of inspection work. Manual verification takes time and carries the risk of transcription errors. Our company’s "Generative AI-OCR" addresses these challenges by automatically reading information such as product names, lot numbers, and quantities simply by photographing labels and forms, contributing to improved accuracy and speed in inspection tasks. [Use Cases] - Inspection records of equipment labels within plants - Verification of model numbers and manufacturing dates of maintenance parts - Reading and digitizing inspection history labels [Benefits of Implementation] - Speeding up inspection tasks - Reducing human errors due to recording mistakes - Improving the accuracy of inspection data and facilitating management

In the agricultural sector, it is essential to accurately grasp the growth status of crops and carry out appropriate management. Particularly in managing growth data and materials for each field, the accuracy and efficiency of records are crucial. Handwritten records and the verification of various material labels require time and effort, and they also carry the risk of human error. In response to these challenges, our "Generative AI-OCR" supports the collection of data necessary for growth predictions by automatically reading information such as product names and quantities simply by photographing labels and forms. Since AI reads characters and content in the same way that a person would look at a label, it can flexibly handle a wide variety of labels. [Use Cases] - Reading material labels - Digitizing growth records - Collecting information for yield predictions [Benefits of Implementation] - Increased efficiency in recording tasks - Reduction of data entry errors - Improved accuracy of growth predictions