System

Abstract

An object of a system according to an embodiment is to enable a user to efficiently find and use a business application that matches the user's own company.SOLUTION: A system according to an embodiment includes an application registration unit, a recommendation determination unit, and a sales unit. The application registration unit registers a business application created by a user on a platform. The recommendation determination unit determines whether to recommend a business application that meets the needs of the user by using AI learned in advance. The sales unit allows other users to purchase and use the business application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies make it difficult for users to find business applications that suit their company, which can hinder efficient business operations.

[0005] The system according to the embodiment aims to enable users to efficiently find and use business applications that suit their company. [Means for solving the problem]

[0006] The system according to the embodiment includes an application registration unit, a recommendation determination unit, and a sales unit. The application registration unit registers business applications created by users on the platform. The recommendation determination unit uses pre-trained AI to determine whether to recommend a business application that meets the user's needs. The sales unit makes the business application available for purchase and use by other users. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently find and use business applications that suit their company. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The business application platform according to an embodiment of the present invention is a system in which users register their created business applications on the platform, a generation AI determines whether to recommend a business application that meets the user's needs, and other users can purchase and use it. This enables the business application platform to efficiently sell and recommend business applications.

[0029] A business application platform according to an embodiment includes an application registration unit, a recommendation determination unit, and a sales unit. The application registration unit registers business applications created by users on the platform. For example, a developer may register an inventory management application developed by their company on the platform, making it available for purchase and use by other companies. The application registration unit provides detailed application information and demo versions, allowing users to try out the application before purchasing. The recommendation determination unit uses pre-trained AI to determine whether to recommend a business application that meets a user's needs. For example, when a user inputs a need such as "I want to improve inventory management efficiency," the generation AI recommends the optimal application based on that need. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to make recommendations based on the user's needs. The sales unit enables other users to purchase and use the business application. For example, a user purchases the inventory management application recommended by the AI ​​and implements it in their company's business. The sales unit also centrally manages purchase procedures and licenses. This enables the business application platform to efficiently sell and recommend business applications.

[0030] The application registration unit can use generation AI to automatically generate application descriptions and demo videos and provide them to users. The application registration unit, for example, uses generation AI to automatically generate application descriptions. For example, when a developer inputs the application's functions and features, the generation AI creates a detailed description based on that. The application registration unit also uses generation AI to automatically generate demo videos. For example, it generates demo videos that show how to operate the application and its functions and provides them to users. This makes application registration more efficient and provides users with information that is easy to understand.

[0031] The application registration unit can automatically evaluate the code quality of registered applications and display a quality score. The application registration unit, for example, builds a system that automatically analyzes the code of registered applications and calculates a quality score. For example, it evaluates the readability and maintainability of the code and displays the score. The application registration unit also evaluates errors and performance in the code and calculates a quality score. For example, it calculates a quality score based on the number of errors in the code and the execution speed. This automates the evaluation of code quality and provides users with highly reliable applications.

[0032] The application registration unit can link with other platforms to enable automatic code import. For example, the application registration unit adds a linking function with other platforms (e.g., GitHub or Bitbucket) and builds a system that automatically imports code from repositories. For example, the application registration unit obtains code using the GitHub API. The application registration unit also provides an interface for automating code import from other platforms. For example, the user can automatically import code by simply entering the platform URL. This automates code import and streamlines the registration process.

[0033] The application registration unit can provide a user-customizable template for a registered application, allowing the application to be easily adapted to company specifications. The application registration unit, for example, builds a system that provides a user-customizable template for a registered application. For example, it provides a template that allows easy modification of the UI or functions. The application registration unit also provides guidelines for users to customize an application to meet company specifications. For example, it provides guidelines that explain setting items and customization options in detail. This allows users to easily create an application that meets company specifications.

[0034] The recommendation determination unit can make more accurate recommendations by taking into account the user's past purchase history and usage history. For example, the recommendation determination unit adds a function to the recommendation algorithm that takes into account the user's past purchase history. For example, the recommendation determination unit recommends related applications based on data on applications purchased in the past. The recommendation determination unit also adds a function that takes into account the user's usage history. For example, the recommendation determination unit recommends the most appropriate application based on usage time and frequency. This makes it possible to recommend more appropriate applications based on the user's past behavioral data.

[0035] The recommendation determination unit can add a function that allows users to provide feedback on recommendation results and utilize that feedback as learning data. For example, the recommendation determination unit builds a system that adds a function that allows users to provide feedback on recommendation results. For example, it allows users to input ratings and comments on recommended applications. The recommendation determination unit also adds a function that utilizes user feedback as learning data. For example, it improves the recommendation algorithm based on the feedback data. This makes it possible to improve the recommendation algorithm based on user feedback.

[0036] The recommendation determination unit can incorporate trend data from different industries and make recommendations that address industry-specific needs. For example, the recommendation determination unit builds a system that incorporates trend data from different industries into a recommendation algorithm. For example, the recommendation determination unit makes recommendations based on the latest technology trends and market needs. The recommendation determination unit also collects data to address industry-specific needs. For example, the recommendation determination unit identifies industry-specific needs based on industry reports and social media data. This allows the recommendation unit to recommend applications that address industry-specific needs.

[0037] The recommendation determination unit can directly incorporate the recommendation results into the user's business process, enabling the immediate introduction of the recommended application. The recommendation determination unit, for example, builds a system that directly incorporates the recommendation results into the user's business process. For example, it makes it possible to introduce the recommended application with one click. The recommendation determination unit also provides an interface for adapting to the user's business process. For example, it provides customization options that match the business process. This allows the recommended application to be introduced into the business quickly.

[0038] The Purchasing Department can use generation AI to automatically generate contracts and license documents and provide them to users. For example, the Purchasing Department can build a system that automatically generates contracts using generation AI. For example, it can automatically generate contract templates based on information entered by users. The Purchasing Department can also automatically generate license documents using generation AI. For example, it can automatically generate software licenses and license agreements and provide them to users. This automates the generation of contracts and license documents, allowing them to be provided to users quickly.

[0039] The purchasing department can add a wizard function that automates initial setup after purchase, allowing users to quickly start using the application. For example, the purchasing department builds a system that provides a wizard function that automates initial setup after purchase. For example, the user can complete application setup simply by entering required information. The purchasing department also uses the wizard function to enable users to intuitively perform setup. For example, the wizard function provides a step-by-step guide to simplify the setup process. This allows users to quickly start using the application.

[0040] The purchasing department can add a recommendation function that takes into account the purchase history and ratings of other companies. For example, the purchasing department builds a system that provides a recommendation function that takes into account the purchase history of other companies during the purchasing process. For example, it recommends applications purchased by other companies in the same industry. The purchasing department also recommends the most suitable application to the user based on the ratings of other companies. For example, it analyzes the rating data of other companies and recommends highly rated applications. This makes it possible to recommend the most suitable application to the user based on the purchase history and ratings of other companies.

[0041] The purchasing department can automate post-purchase support with a chatbot and respond to user questions in real time. The purchasing department, for example, builds a system that automates post-purchase support with a chatbot. For example, it provides a chatbot that answers user questions in real time. The purchasing department also uses the chatbot to quickly resolve user problems. For example, it provides troubleshooting guides and FAQs to solve user problems. This automates post-purchase support and allows for quick responses to user questions.

[0042] The evaluation unit can use the generation AI to automatically generate evaluation reports and provide them to developers. The evaluation unit, for example, builds a system that uses the generation AI to automatically generate evaluation reports during the evaluation and feedback process. For example, it creates a detailed report based on user evaluation data. The evaluation unit also uses the generation AI to automatically generate evaluation reports and provide them to developers. For example, it generates a report including performance evaluation and code review and provides it to developers. This automates the generation of evaluation reports and enables them to be provided to developers quickly.

[0043] The evaluation unit can collect feedback in real time, allowing the developer to respond immediately. The evaluation unit, for example, builds a system for collecting feedback in real time. For example, it allows users to input ratings and comments in real time when using an application. The evaluation unit also immediately notifies the developer of the feedback collected in real time. For example, it allows the developer to respond quickly based on the feedback data. This allows feedback to be collected and responded to quickly.

[0044] The evaluation unit can add a mutual evaluation function that takes into account the evaluations of other users. The evaluation unit, for example, builds a system that provides a mutual evaluation function that takes into account the evaluations of other users in the evaluation and feedback process. For example, it displays the evaluations of users who have used the same application. The evaluation unit also provides highly reliable evaluations based on the evaluations of other users. For example, it introduces a peer review system to improve the reliability of the evaluations. This makes it possible to provide more reliable evaluations by taking into account the evaluations of other users.

[0045] The evaluation unit can provide a dashboard that visualizes the feedback and enables developers to intuitively understand it. The evaluation unit, for example, builds a system that provides a dashboard that visualizes the feedback. For example, the evaluation unit displays evaluation scores and comments in graphs and charts. The evaluation unit also provides visualization tools that enable developers to intuitively understand the content of the feedback. For example, the evaluation unit provides real-time data displays and customizable widgets. This enables developers to intuitively understand the content of the feedback.

[0046] The update unit can automatically generate update information and notify the user. The update unit, for example, builds a system that automatically generates update information and notifies the user. For example, it automatically generates information about new functions and bug fixes for an application. The update unit also provides an interface for notifying the user of the generated update information. For example, it provides the user with the update information via email notification or push notification. This automates the generation and notification of update information.

[0047] The update unit can collect user feedback on the update content in real time and reflect it in the next update. The update unit, for example, builds a system that collects user feedback on the update content in real time. For example, it allows users to input their impressions of using the application after the update and any problems they may have. The update unit also provides a process for reflecting the collected feedback in the next update. For example, it creates a plan for the next update based on the feedback data. This allows user feedback to be reflected in the next update.

[0048] The update unit can widely notify update information by linking with other platforms and social media. For example, the update unit builds a system that links with other platforms and social media to notify update information. For example, it provides a function for automatically posting to Twitter and Facebook. The update unit also widely notifies update information by linking with other platforms. For example, it shares update information on LinkedIn and Slack. This allows update information to be widely notified.

[0049] The update unit can provide infographics that visualize the update content and allow the user to intuitively understand it. The update unit, for example, builds a system that provides infographics that visualize the update content. For example, the update content is displayed using diagrams or icons. The update unit also uses infographics to allow the user to intuitively understand. For example, it provides a template that displays the update content in a visually easy-to-understand manner. This allows the update content to be intuitively understood.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The application registration unit can automatically evaluate the security of business applications created by users and display a security score. For example, it can analyze the application code and identify vulnerabilities and security risks. It can also suggest areas for improvement based on the security score. This allows users to provide highly secure applications.

[0052] The application registration unit can automatically evaluate compatibility with applications registered by other users and display a compatibility score. For example, it can analyze the application's API and data format to evaluate compatibility. It can also recommend highly compatible applications based on the compatibility score, allowing users to select highly compatible applications.

[0053] The application registration section supports data import from other platforms, allowing users to easily migrate existing data. For example, it supports data import from CSV files and Excel files. It also provides a function to check data integrity and automatically correct errors when importing data, allowing users to migrate data smoothly.

[0054] The application registration unit can automatically evaluate the market competitiveness of applications registered by users and display a competitiveness score. For example, it can compare the application with other applications in the same category and evaluate the competitiveness of features and price. It can also suggest areas for improvement based on the competitiveness score. This allows users to provide highly competitive applications.

[0055] The recommendation determination unit can automatically analyze a user's business process and recommend the best application for that business process. For example, it can analyze the user's business flow and tasks and identify applications that will help improve efficiency. It can also suggest areas for improving the business process. This allows the user to select the best application for their business process.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The application registration unit registers business applications created by users on the platform. For example, a developer can register an inventory management application they developed in-house on the platform, making it available for other companies to purchase and use. The application registration unit provides detailed information about the application and a demo version, allowing users to try it before purchasing. Step 2: The recommendation decision unit uses pre-trained AI to determine whether to recommend a business application that meets the user's needs. For example, if a user inputs a need such as "I want to improve the efficiency of inventory management," the generation AI will recommend the optimal application based on that need. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to make recommendations based on the user's needs. Step 3: The sales department allows other users to purchase and use the business application. For example, a user purchases an inventory management application recommended by AI and implements it in their company's business. The sales department can also centrally manage purchasing procedures and licenses.

[0058] (Example 2) The business application platform according to an embodiment of the present invention is a system in which users register their created business applications on the platform, a generation AI determines whether to recommend a business application that meets the user's needs, and other users can purchase and use it. This enables the business application platform to efficiently sell and recommend business applications.

[0059] A business application platform according to an embodiment includes an application registration unit, a recommendation determination unit, and a sales unit. The application registration unit registers business applications created by users on the platform. For example, a developer may register an inventory management application developed by their company on the platform, making it available for purchase and use by other companies. The application registration unit provides detailed application information and demo versions, allowing users to try out the application before purchasing. The recommendation determination unit uses pre-trained AI to determine whether to recommend a business application that meets a user's needs. For example, when a user inputs a need such as "I want to improve inventory management efficiency," the generation AI recommends the optimal application based on that need. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to make recommendations based on the user's needs. The sales unit enables other users to purchase and use the business application. For example, a user purchases the inventory management application recommended by the AI ​​and implements it in their company's business. The sales unit also centrally manages purchase procedures and licenses. This enables the business application platform to efficiently sell and recommend business applications.

[0060] The application registration unit can use generation AI to automatically generate application descriptions and demo videos and provide them to users. The application registration unit, for example, uses generation AI to automatically generate application descriptions. For example, when a developer inputs the application's functions and features, the generation AI creates a detailed description based on that. The application registration unit also uses generation AI to automatically generate demo videos. For example, it generates demo videos that show how to operate the application and its functions and provides them to users. This makes application registration more efficient and provides users with information that is easy to understand.

[0061] The application registration unit can automatically evaluate the code quality of registered applications and display a quality score. The application registration unit, for example, builds a system that automatically analyzes the code of registered applications and calculates a quality score. For example, it evaluates the readability and maintainability of the code and displays the score. The application registration unit also evaluates errors and performance in the code and calculates a quality score. For example, it calculates a quality score based on the number of errors in the code and the execution speed. This automates the evaluation of code quality and provides users with highly reliable applications.

[0062] The application registration unit can use the emotion estimation function to analyze the developer's emotion at the time of registration and provide feedback to elicit positive emotions. The application registration unit, for example, uses the emotion estimation function to build a system that analyzes the developer's emotion at the time of registration in real time. For example, the application registration unit analyzes the developer's facial expressions and voice and calculates an emotion score. The application registration unit also uses the emotion estimation function to provide feedback to elicit positive emotions from the developer. For example, the application registration unit provides encouraging messages and advice to encourage the developer to have positive emotions. This improves developer motivation and makes it possible to provide better applications.

[0063] The application registration unit can link with other platforms to enable automatic code import. For example, the application registration unit adds a linking function with other platforms (e.g., GitHub or Bitbucket) and builds a system that automatically imports code from repositories. For example, the application registration unit obtains code using the GitHub API. The application registration unit also provides an interface for automating code import from other platforms. For example, the user can automatically import code by simply entering the platform URL. This automates code import and streamlines the registration process.

[0064] The application registration unit can provide a user-customizable template for a registered application, allowing the application to be easily adapted to company specifications. The application registration unit, for example, builds a system that provides a user-customizable template for a registered application. For example, it provides a template that allows easy modification of the UI or functions. The application registration unit also provides guidelines for users to customize an application to meet company specifications. For example, it provides guidelines that explain setting items and customization options in detail. This allows users to easily create an application that meets company specifications.

[0065] The application registration unit can use the emotion estimation function to collect users' initial reactions to registered applications and provide feedback to the registrant. The application registration unit, for example, uses the emotion estimation function to build a system that collects users' initial reactions to registered applications in real time. For example, the application registration unit analyzes the user's facial expressions and voice and calculates an emotion score. The application registration unit also provides feedback to the registrant based on the user's initial reactions. For example, the application registration unit identifies areas for improvement in the application based on the user's emotion score and notifies the registrant. This allows the registrant to identify areas for improvement in the application based on the user's initial reactions.

[0066] The recommendation determination unit can make more accurate recommendations by taking into account the user's past purchase history and usage history. For example, the recommendation determination unit adds a function to the recommendation algorithm that takes into account the user's past purchase history. For example, the recommendation determination unit recommends related applications based on data on applications purchased in the past. The recommendation determination unit also adds a function that takes into account the user's usage history. For example, the recommendation determination unit recommends the most appropriate application based on usage time and frequency. This makes it possible to recommend more appropriate applications based on the user's past behavioral data.

[0067] The recommendation determination unit can add a function that allows users to provide feedback on recommendation results and utilize that feedback as learning data. For example, the recommendation determination unit builds a system that adds a function that allows users to provide feedback on recommendation results. For example, it allows users to input ratings and comments on recommended applications. The recommendation determination unit also adds a function that utilizes user feedback as learning data. For example, it improves the recommendation algorithm based on the feedback data. This makes it possible to improve the recommendation algorithm based on user feedback.

[0068] The recommendation determination unit can use the emotion estimation function to analyze the emotion of the user when entering input and make recommendations based on the emotion. The recommendation determination unit, for example, uses the emotion estimation function to build a system that analyzes the emotion of the user when entering input in real time. For example, it analyzes the user's facial expression and voice and calculates an emotion score. The recommendation determination unit also makes emotion-based recommendations based on the emotion score. For example, it preferentially recommends applications for which the user has positive emotions. This makes it possible to recommend appropriate applications according to the user's emotions.

[0069] The recommendation determination unit can incorporate trend data from different industries and make recommendations that address industry-specific needs. For example, the recommendation determination unit builds a system that incorporates trend data from different industries into a recommendation algorithm. For example, the recommendation determination unit makes recommendations based on the latest technology trends and market needs. The recommendation determination unit also collects data to address industry-specific needs. For example, the recommendation determination unit identifies industry-specific needs based on industry reports and social media data. This allows the recommendation unit to recommend applications that address industry-specific needs.

[0070] The recommendation determination unit can directly incorporate the recommendation results into the user's business process, enabling the immediate introduction of the recommended application. The recommendation determination unit, for example, builds a system that directly incorporates the recommendation results into the user's business process. For example, it makes it possible to introduce the recommended application with one click. The recommendation determination unit also provides an interface for adapting to the user's business process. For example, it provides customization options that match the business process. This allows the recommended application to be introduced into the business quickly.

[0071] The recommendation determination unit can use the emotion estimation function to monitor the user's emotional response to the recommendation results in real time and continuously improve the recommendation algorithm. The recommendation determination unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the recommendation results in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The recommendation determination unit also continuously improves the recommendation algorithm based on the user's emotional response. For example, it uses the emotion score as learning data to optimize the algorithm. This makes it possible to improve the recommendation algorithm based on the user's emotional response.

[0072] The Purchasing Department can use generation AI to automatically generate contracts and license documents and provide them to users. For example, the Purchasing Department can build a system that automatically generates contracts using generation AI. For example, it can automatically generate contract templates based on information entered by users. The Purchasing Department can also automatically generate license documents using generation AI. For example, it can automatically generate software licenses and license agreements and provide them to users. This automates the generation of contracts and license documents, allowing them to be provided to users quickly.

[0073] The purchasing department can add a wizard function that automates initial setup after purchase, allowing users to quickly start using the application. For example, the purchasing department builds a system that provides a wizard function that automates initial setup after purchase. For example, the user can complete application setup simply by entering required information. The purchasing department also uses the wizard function to enable users to intuitively perform setup. For example, the wizard function provides a step-by-step guide to simplify the setup process. This allows users to quickly start using the application.

[0074] The purchasing unit can use the emotion estimation function to analyze the user's emotions at the time of purchase and make suggestions to provide a positive purchasing experience. The purchasing unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions at the time of purchase in real time. For example, the purchasing unit analyzes the user's facial expressions and voice and calculates an emotion score. The purchasing unit also makes suggestions to provide a positive purchasing experience based on the emotion score. For example, the purchasing unit may provide benefits or discounts so that the user will have positive emotions. This improves the user's purchasing experience.

[0075] The purchasing department can add a recommendation function that takes into account the purchase history and ratings of other companies. For example, the purchasing department builds a system that provides a recommendation function that takes into account the purchase history of other companies during the purchasing process. For example, it recommends applications purchased by other companies in the same industry. The purchasing department also recommends the most suitable application to the user based on the ratings of other companies. For example, it analyzes the rating data of other companies and recommends highly rated applications. This makes it possible to recommend the most suitable application to the user based on the purchase history and ratings of other companies.

[0076] The purchasing department can automate post-purchase support with a chatbot and respond to user questions in real time. The purchasing department, for example, builds a system that automates post-purchase support with a chatbot. For example, it provides a chatbot that answers user questions in real time. The purchasing department also uses the chatbot to quickly resolve user problems. For example, it provides troubleshooting guides and FAQs to solve user problems. This automates post-purchase support and allows for quick responses to user questions.

[0077] The purchasing department can use the emotion estimation function to monitor the user's emotions after a purchase and provide feedback to improve satisfaction. The purchasing department, for example, uses the emotion estimation function to build a system that monitors the user's emotions after a purchase in real time. For example, the purchasing department analyzes the user's facial expressions and voice to calculate an emotion score. The purchasing department also provides feedback to improve satisfaction based on the emotion score. For example, the purchasing department provides advice and support to help the user increase their satisfaction. This makes it possible to provide feedback to improve the user's satisfaction.

[0078] The evaluation unit can use the generation AI to automatically generate evaluation reports and provide them to developers. The evaluation unit, for example, builds a system that uses the generation AI to automatically generate evaluation reports during the evaluation and feedback process. For example, it creates a detailed report based on user evaluation data. The evaluation unit also uses the generation AI to automatically generate evaluation reports and provide them to developers. For example, it generates a report including performance evaluation and code review and provides it to developers. This automates the generation of evaluation reports and enables them to be provided to developers quickly.

[0079] The evaluation unit can collect feedback in real time, allowing the developer to respond immediately. The evaluation unit, for example, builds a system for collecting feedback in real time. For example, it allows users to input ratings and comments in real time when using an application. The evaluation unit also immediately notifies the developer of the feedback collected in real time. For example, it allows the developer to respond quickly based on the feedback data. This allows feedback to be collected and responded to quickly.

[0080] The evaluation unit can use the emotion estimation function to analyze the user's emotions at the time of feedback and make suggestions to improve negative emotions. The evaluation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions at the time of feedback in real time. For example, the evaluation unit analyzes the user's facial expressions and voice and calculates an emotion score. The evaluation unit also makes suggestions to improve negative emotions based on the emotion score. For example, the evaluation unit identifies points that the user is dissatisfied with and proposes improvements. This makes it possible to make suggestions to improve the user's negative emotions.

[0081] The evaluation unit can add a mutual evaluation function that takes into account the evaluations of other users. The evaluation unit, for example, builds a system that provides a mutual evaluation function that takes into account the evaluations of other users in the evaluation and feedback process. For example, it displays the evaluations of users who have used the same application. The evaluation unit also provides highly reliable evaluations based on the evaluations of other users. For example, it introduces a peer review system to improve the reliability of the evaluations. This makes it possible to provide more reliable evaluations by taking into account the evaluations of other users.

[0082] The evaluation unit can provide a dashboard that visualizes the feedback and enables developers to intuitively understand it. The evaluation unit, for example, builds a system that provides a dashboard that visualizes the feedback. For example, the evaluation unit displays evaluation scores and comments in graphs and charts. The evaluation unit also provides visualization tools that enable developers to intuitively understand the content of the feedback. For example, the evaluation unit provides real-time data displays and customizable widgets. This enables developers to intuitively understand the content of the feedback.

[0083] The evaluation unit can use the emotion estimation function to analyze the developer's emotional response to the feedback and make suggestions to improve the developer's motivation. The evaluation unit, for example, uses the emotion estimation function to build a system that analyzes the developer's emotional response to the feedback in real time. For example, the evaluation unit analyzes the developer's facial expressions and voice and calculates an emotion score. The evaluation unit also makes suggestions to improve the developer's motivation based on the emotion score. For example, the evaluation unit provides incentives or encouraging messages. This allows suggestions to improve the developer's motivation.

[0084] The update unit can automatically generate update information and notify the user. The update unit, for example, builds a system that automatically generates update information and notifies the user. For example, it automatically generates information about new functions and bug fixes for an application. The update unit also provides an interface for notifying the user of the generated update information. For example, it provides the user with the update information via email notification or push notification. This automates the generation and notification of update information.

[0085] The update unit can collect user feedback on the update content in real time and reflect it in the next update. The update unit, for example, builds a system that collects user feedback on the update content in real time. For example, it allows users to input their impressions of using the application after the update and any problems they may have. The update unit also provides a process for reflecting the collected feedback in the next update. For example, it creates a plan for the next update based on the feedback data. This allows user feedback to be reflected in the next update.

[0086] The update unit can use the emotion estimation function to analyze the user's emotions at the time of update and make suggestions to provide a positive update experience. The update unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions at the time of update in real time. For example, the update unit analyzes the user's facial expressions and voice and calculates an emotion score. Furthermore, the update unit makes suggestions to provide a positive update experience based on the emotion score. For example, the update unit provides benefits or improvements so that the user will have positive emotions toward updates. This makes it possible to make suggestions to improve the user's update experience.

[0087] The update unit can widely notify update information by linking with other platforms and social media. For example, the update unit builds a system that links with other platforms and social media to notify update information. For example, it provides a function for automatically posting to Twitter and Facebook. The update unit also widely notifies update information by linking with other platforms. For example, it shares update information on LinkedIn and Slack. This allows update information to be widely notified.

[0088] The update unit can provide infographics that visualize the update content and allow the user to intuitively understand it. The update unit, for example, builds a system that provides infographics that visualize the update content. For example, the update content is displayed using diagrams or icons. The update unit also uses infographics to allow the user to intuitively understand. For example, it provides a template that displays the update content in a visually easy-to-understand manner. This allows the update content to be intuitively understood.

[0089] The update unit can use the emotion estimation function to monitor the user's emotion after the update and provide feedback to improve satisfaction. The update unit, for example, uses the emotion estimation function to build a system that monitors the user's emotion after the update in real time. For example, the update unit analyzes the user's facial expression and voice to calculate an emotion score. The update unit also provides feedback to improve satisfaction based on the emotion score. For example, the update unit provides benefits or improvements so that the user will have positive emotions about the update. This makes it possible to provide feedback to improve user satisfaction.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The application registration unit can automatically evaluate the security of business applications created by users and display a security score. For example, it can analyze the application code and identify vulnerabilities and security risks. It can also suggest areas for improvement based on the security score. This allows users to provide highly secure applications.

[0092] The application registration unit can use the emotion estimation function to analyze the user's stress level at the time of registration and provide relaxation content to reduce stress. For example, it can analyze the user's facial expressions and voice to calculate a stress score. It can also provide relaxation music or guided meditation based on the stress score. This can reduce the user's stress and provide a comfortable registration experience.

[0093] The application registration unit can automatically evaluate compatibility with applications registered by other users and display a compatibility score. For example, it can analyze the application's API and data format to evaluate compatibility. It can also recommend highly compatible applications based on the compatibility score, allowing users to select highly compatible applications.

[0094] The application registration unit can use the emotion estimation function to analyze the user's excitement level at the time of registration and provide feedback according to the excitement level. For example, it can analyze the user's facial expressions and voice to calculate an excitement score. It can also identify points that make the user feel excited and provide positive feedback based on the excitement score. This helps maintain the user's excitement and enjoy the registration process.

[0095] The application registration section supports data import from other platforms, allowing users to easily migrate existing data. For example, it supports data import from CSV files and Excel files. It also provides a function to check data integrity and automatically correct errors when importing data, allowing users to migrate data smoothly.

[0096] The application registration unit can use the emotion estimation function to analyze the user's concentration level at the time of registration and suggest environmental settings to enhance concentration. For example, it can analyze the user's facial expressions and voice and calculate a concentration score. It can also suggest appropriate lighting and music settings based on the concentration score. This allows the user to concentrate while registering an application.

[0097] The application registration unit can automatically evaluate the market competitiveness of applications registered by users and display a competitiveness score. For example, it can compare the application with other applications in the same category and evaluate the competitiveness of features and price. It can also suggest areas for improvement based on the competitiveness score. This allows users to provide highly competitive applications.

[0098] The recommendation determination unit can use the emotion estimation function to analyze the emotion of the user at the time of input and make recommendations based on the emotion. For example, it can analyze the user's facial expression and voice and calculate an emotion score. It can also recommend applications that are suitable for when the user is relaxed based on the emotion score. This makes it possible to recommend appropriate applications according to the user's emotions.

[0099] The recommendation determination unit can automatically analyze a user's business process and recommend the best application for that business process. For example, it can analyze the user's business flow and tasks and identify applications that will help improve efficiency. It can also suggest areas for improving the business process. This allows the user to select the best application for their business process.

[0100] The recommendation determination unit uses the emotion estimation function to monitor the user's emotional response to the recommendation results in real time and continuously improve the recommendation algorithm. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. It can also optimize the recommendation algorithm based on the emotion score. This allows the recommendation algorithm to be improved based on the user's emotional response.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The application registration unit registers business applications created by users on the platform. For example, a developer can register an inventory management application they developed in-house on the platform, making it available for other companies to purchase and use. The application registration unit provides detailed information about the application and a demo version, allowing users to try it before purchasing. Step 2: The recommendation decision unit uses pre-trained AI to determine whether to recommend a business application that meets the user's needs. For example, if a user inputs a need such as "I want to improve the efficiency of inventory management," the generation AI will recommend the optimal application based on that need. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to make recommendations based on the user's needs. Step 3: The sales department allows other users to purchase and use the business application. For example, a user purchases an inventory management application recommended by AI and implements it in their company's business. The sales department can also centrally manage purchasing procedures and licenses.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an application registration unit that registers business applications created by users on the platform; A recommendation decision unit that uses pre-trained AI to determine whether to recommend business applications that meet the user's needs; a sales department that enables other users to purchase and use the business application. A system characterized by:

2. The application registration unit Automatically evaluate the code quality of registered applications and display a quality score The system of claim 1 .

3. The recommendation determination unit Make more accurate recommendations by taking into account a user's past purchase and usage history The system of claim 1 .

4. The purchasing department Add a wizard function to automate initial setup after purchase, allowing users to quickly start using the application. The system of claim 1 .

5. The evaluation section Providing a dashboard that visualizes feedback and makes it intuitive for developers The system of claim 1 .

6. The update section is Gather user feedback on updates in real time and incorporate it into the next update The system of claim 1 .

7. The application registration unit Using the emotion estimation function, we analyze the developer's emotions at the time of registration and provide feedback to elicit positive emotions. The system of claim 1 .

8. The recommendation determination unit Emotion estimation function analyzes the emotions of users when they type and makes emotion-based recommendations The system of claim 1 .

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