System

Abstract

An object of a system according to an embodiment is to realize an idea by appropriately utilizing generated AI.SOLUTION: A system includes a prompt instruction personnel aggregation part, an idea collection part, and a generation AI coding part. A prompt instruction personnel collecting part collects personnel capable of instructing an appropriate prompt to the generated AI. An idea collection part receives an idea from a person having the idea. The generated AI coding unit performs coding of the smartphone application using the generated AI on the basis of the idea entrusted by the idea collection unit.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] With conventional technology, many people had ideas but were unable to properly utilize generative AI, making it difficult to realize their ideas.

[0005] The system according to the embodiment aims to realize ideas by appropriately utilizing generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a prompting talent aggregation unit, an idea collection unit, and a generative AI coding unit. The prompting talent aggregation unit gathers talent who can provide appropriate prompts to the generative AI. The idea collection unit receives ideas from people who have them. The generative AI coding unit codes smartphone apps using the generative AI based on the ideas received by the idea collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize ideas by appropriately utilizing generative AI. [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) A platform according to an embodiment of the present invention is a system that utilizes generative AI to realize ideas as smartphone apps. This system gathers and incorporates human resources who can provide appropriate prompts to the generative AI, accepts ideas from people with ideas, and uses the generative AI to code the smartphone app. This allows the platform to realize ideas as smartphone apps using the generative AI.

[0029] A platform according to an embodiment includes a prompting talent aggregation unit, an idea collection unit, and a generative AI coding unit. The prompting talent aggregation unit gathers talent who can provide appropriate prompts to the generative AI. For example, it may gather talent who can provide prompts to the generative AI, such as "create a chatbot that analyzes text entered by a user and generates appropriate replies." The prompting talent aggregation unit also incorporates itself to provide organizational support for realizing ideas. The idea collection unit receives ideas from people who have them. For example, if an idea provider has an idea for "creating a health management app," the unit evaluates the marketability and feasibility of the idea. The generative AI coding unit uses the generative AI to code a smartphone app based on the idea received from the idea collection unit. For example, if a prompt is input, such as "create an app that allows a user to enter food records, analyze the data, and display nutritional balance," the generative AI generates app code based on the instructions. This allows the platform according to an embodiment to utilize the generative AI to realize ideas as smartphone apps.

[0030] The prompt instruction personnel aggregation unit uses generation AI to analyze the success rate and efficiency of past prompts of prompt instruction personnel, and can select the most suitable personnel. For example, the prompt instruction personnel aggregation unit creates a database of the past prompt performance of prompt instruction personnel, and analyzes the success rate and efficiency using generation AI. For example, it evaluates the execution results and required time of each prompt and selects the most suitable personnel. This improves the accuracy of selecting prompt instruction personnel.

[0031] The prompt instruction personnel aggregation unit can use generation AI to automatically generate training programs for prompt instruction personnel and provide customized training according to individual skills. For example, the prompt instruction personnel aggregation unit uses generation AI to evaluate the skill level of prompt instruction personnel and automatically generate training programs according to individual skills. For example, it can provide training ranging from basic training for beginners to advanced training for advanced personnel. This allows for the improvement of the skills of prompt instruction personnel.

[0032] The prompt instruction human resources aggregation unit actively recruits experts from different industries and fields, and can promote the creation of prompts from diverse perspectives. The prompt instruction human resources aggregation unit, for example, actively recruits experts from different industries and fields, and can promote the creation of prompts from diverse perspectives. For example, it can gather experts from the fields of technology, design, and marketing. This makes it possible to create prompts from diverse perspectives.

[0033] The prompting talent aggregation unit can use the generation AI to implement a virtual project and test the prompting talent's actual prompt creation ability. The prompting talent aggregation unit, for example, uses the generation AI to implement a virtual project and test the prompting talent's actual prompt creation ability. For example, a virtual app development project is set up and the talent is asked to create prompts within it. This allows the prompting talent's actual ability to be evaluated.

[0034] The idea collection unit can use the generation AI to analyze the idea provider's past successes and failures, thereby improving the accuracy of the evaluation. For example, the idea collection unit creates a database of the idea provider's past successes and failures, and analyzes the data using the generation AI. For example, it analyzes the success rate and causes of failure, improving the accuracy of the evaluation. This improves the accuracy of the idea evaluation.

[0035] The idea collection department uses generative AI to automatically generate evaluation criteria for ideas, enabling consistent evaluation of each idea. The idea collection department, for example, builds a system that uses generative AI to automatically generate evaluation criteria for ideas. For example, it sets criteria such as marketability, technical feasibility, and economic effectiveness. This allows for consistent evaluation of ideas.

[0036] The idea collection unit uses generative AI to automatically translate ideas from different languages ​​and cultural spheres, enabling evaluation from a global perspective. The idea collection unit, for example, builds a system that uses generative AI to automatically translate ideas from different languages ​​and cultural spheres. For example, it can support multiple languages ​​such as English, French, and Chinese. This allows ideas to be evaluated from a global perspective.

[0037] The idea collection department uses generative AI to collect market data and analyze competitors, allowing it to evaluate the marketability of ideas from multiple angles. The idea collection department, for example, uses generative AI to collect market data and analyze competitors, building a system that evaluates the marketability of ideas from multiple angles. For example, it analyzes past market data and information on competing products. This allows it to evaluate the marketability of ideas from multiple angles.

[0038] The generative AI coding unit can analyze the bug occurrence rate and performance of the code using past project data to evaluate the quality of the code generated by the generative AI. The generative AI coding unit, for example, builds a system that analyzes the bug occurrence rate and performance of the code using past project data to evaluate the quality of the code generated by the generative AI. For example, it analyzes past bug reports and performance data. This makes it possible to evaluate the quality of the code generated by the generative AI.

[0039] The generative AI coding unit can monitor the performance of the code in real time and make automatic corrections as necessary to optimize the code generated by the generative AI. The generative AI coding unit, for example, builds a system that monitors the performance of the code in real time to optimize the code generated by the generative AI. For example, it collects and analyzes performance data during execution. This allows the performance of the code generated by the generative AI to be optimized.

[0040] The generative AI coding unit can generate cross-platform compatible code so that the code generated by the generative AI can be compatible with different platforms. For example, the generative AI coding unit builds a system that generates cross-platform compatible code so that the code generated by the generative AI can be compatible with different platforms. For example, it uses frameworks such as React Native and Flutter. This allows the code generated by the generative AI to be compatible with different platforms.

[0041] The generative AI coding unit can perform vulnerability scans in real time and automatically fix any vulnerabilities that are discovered in order to enhance the security of the code generated by the generative AI. The generative AI coding unit, for example, builds a system that performs vulnerability scans in real time in order to enhance the security of the code generated by the generative AI. For example, it uses static analysis tools and dynamic analysis tools. This can enhance the security of the code generated by the generative AI.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Prompt Instruction Human Resources Aggregation Unit actively recruits experts from different industries and fields to promote the creation of prompts from diverse perspectives. For example, by bringing together experts from technical, design, and marketing fields, the quality of prompts for the generation AI can be improved. In addition, by bringing together people with different cultural backgrounds, it becomes possible to create prompts from a global perspective. Furthermore, by bringing together people of different age groups and genders, it becomes possible to create prompts that are suitable for a diverse user population.

[0044] The Prompt Instructor Aggregator uses generation AI to analyze the success rate and efficiency of past prompts of prompt instructors, enabling it to select the most suitable person. For example, it evaluates the execution results and time required for each prompt and selects the most suitable person. This improves the accuracy of selecting prompt instructors. In addition, by analyzing past successful prompt cases and identifying the factors behind their success, it is possible to share that know-how with other personnel. Furthermore, this data can be used when designing training programs to improve the skills of prompt instructors.

[0045] The Prompt Personnel Aggregation Unit uses generative AI to automatically generate training programs for prompt personnel and provide customized training tailored to each individual's skills. For example, it can provide training ranging from basic training for beginners to advanced training for advanced users. This allows prompt personnel to improve their skills. It can also monitor the progress of the training program in real time and adjust the training content as needed. Furthermore, by evaluating the effectiveness of the training program and continuously improving it, it is possible to maximize the skills of prompt personnel.

[0046] The prompting talent aggregation unit can use generation AI to implement virtual projects to test prompting talent's actual prompt creation ability. For example, it can set up a virtual app development project and have the talent create prompts within it. This allows the prompting talent's actual ability to be evaluated. Based on the results of the virtual project, it can also provide feedback to the prompting talent to improve their skills. Furthermore, the virtual project can also be used to evaluate the prompting talent's teamwork and communication skills.

[0047] The idea collection department can use generative AI to analyze past successes and failures of idea providers to improve the accuracy of evaluations. For example, it can analyze success rates and causes of failures to improve the accuracy of evaluations. This improves the accuracy of idea evaluations. In addition, it can identify factors behind success based on past successes and share that know-how with other idea providers. Furthermore, by analyzing failures, it can take measures to prevent the same failures from happening again.

[0048] The idea collection department can use generative AI to conduct market data and competitive analysis and evaluate the marketability of ideas from multiple angles. For example, it analyzes past market data and information on competing products. This allows for a multifaceted evaluation of the marketability of ideas. Generative AI can also be used to predict market trends and evaluate future marketability. Furthermore, through competitive analysis, it is possible to identify points of differentiation for ideas and develop strategies to increase competitive advantage.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The Prompt Instruction Human Resources Aggregation Unit will assemble personnel who can provide appropriate prompts to the Generative AI. For example, they will gather personnel who can provide prompts to the Generative AI, such as "create a chatbot that analyzes the text entered by the user and generates appropriate replies." Furthermore, by incorporating the Prompt Instruction Human Resources Aggregation Unit, they will create a system to support the realization of ideas in an organized manner. Step 2: The idea collection department receives ideas from people who have them. For example, if an idea provider has an idea for "creating a health management app," the department evaluates the marketability and feasibility of the idea. Step 3: The Generative AI Coding Department uses the Generative AI to code the smartphone app based on the ideas received from the Idea Collection Department. For example, if a prompt is input such as "Create an app that allows users to enter food records, analyze the data, and display nutritional balance," the Generative AI will generate the app code based on the instructions.

[0051] (Example 2) A platform according to an embodiment of the present invention is a system that utilizes generative AI to realize ideas as smartphone apps. This system gathers and incorporates human resources who can provide appropriate prompts to the generative AI, accepts ideas from people with ideas, and uses the generative AI to code the smartphone app. This allows the platform to realize ideas as smartphone apps using the generative AI.

[0052] A platform according to an embodiment includes a prompting talent aggregation unit, an idea collection unit, and a generative AI coding unit. The prompting talent aggregation unit gathers talent who can provide appropriate prompts to the generative AI. For example, it may gather talent who can provide prompts to the generative AI, such as "create a chatbot that analyzes text entered by a user and generates appropriate replies." The prompting talent aggregation unit also incorporates itself to provide organizational support for realizing ideas. The idea collection unit receives ideas from people who have them. For example, if an idea provider has an idea for "creating a health management app," the unit evaluates the marketability and feasibility of the idea. The generative AI coding unit uses the generative AI to code a smartphone app based on the idea received from the idea collection unit. For example, if a prompt is input, such as "create an app that allows a user to enter food records, analyze the data, and display nutritional balance," the generative AI generates app code based on the instructions. This allows the platform according to an embodiment to utilize the generative AI to realize ideas as smartphone apps.

[0053] The prompt instruction personnel aggregation unit uses generation AI to analyze the success rate and efficiency of past prompts of prompt instruction personnel, and can select the most suitable personnel. For example, the prompt instruction personnel aggregation unit creates a database of the past prompt performance of prompt instruction personnel, and analyzes the success rate and efficiency using generation AI. For example, it evaluates the execution results and required time of each prompt and selects the most suitable personnel. This improves the accuracy of selecting prompt instruction personnel.

[0054] The prompt instruction personnel aggregation unit can use generation AI to automatically generate training programs for prompt instruction personnel and provide customized training according to individual skills. For example, the prompt instruction personnel aggregation unit uses generation AI to evaluate the skill level of prompt instruction personnel and automatically generate training programs according to individual skills. For example, it can provide training ranging from basic training for beginners to advanced training for advanced personnel. This allows for the improvement of the skills of prompt instruction personnel.

[0055] The prompt instruction personnel aggregation unit uses an emotion estimation function to monitor the motivation and stress levels of prompt instruction personnel in real time, thereby providing an optimal work environment. The prompt instruction personnel aggregation unit, for example, uses the emotion estimation function to build a system that monitors the motivation and stress levels of prompt instruction personnel in real time. For example, it analyzes facial expressions and voice tones to calculate emotion scores. This improves the work efficiency of prompt instruction personnel.

[0056] The prompt instruction human resources aggregation unit actively recruits experts from different industries and fields, and can promote the creation of prompts from diverse perspectives. The prompt instruction human resources aggregation unit, for example, actively recruits experts from different industries and fields, and can promote the creation of prompts from diverse perspectives. For example, it can gather experts from the fields of technology, design, and marketing. This makes it possible to create prompts from diverse perspectives.

[0057] The prompting talent aggregation unit can use the generation AI to implement a virtual project and test the prompting talent's actual prompt creation ability. The prompting talent aggregation unit, for example, uses the generation AI to implement a virtual project and test the prompting talent's actual prompt creation ability. For example, a virtual app development project is set up and the talent is asked to create prompts within it. This allows the prompting talent's actual ability to be evaluated.

[0058] The prompting talent aggregation unit can analyze the emotional state of the prompting talent using the emotion estimation function and provide feedback to optimize communication within the team. For example, the prompting talent aggregation unit can analyze the emotional state of the prompting talent in real time using the emotion estimation function and provide feedback to optimize communication within the team. For example, the prompting talent aggregation unit can suggest areas for improvement in communication based on the emotion score. This optimizes communication within the team.

[0059] The idea collection unit can use the generation AI to analyze the idea provider's past successes and failures, thereby improving the accuracy of the evaluation. For example, the idea collection unit creates a database of the idea provider's past successes and failures, and analyzes the data using the generation AI. For example, it analyzes the success rate and causes of failure, improving the accuracy of the evaluation. This improves the accuracy of the idea evaluation.

[0060] The idea collection department uses generative AI to automatically generate evaluation criteria for ideas, enabling consistent evaluation of each idea. The idea collection department, for example, builds a system that uses generative AI to automatically generate evaluation criteria for ideas. For example, it sets criteria such as marketability, technical feasibility, and economic effectiveness. This allows for consistent evaluation of ideas.

[0061] The idea collection unit can use the emotion estimation function to evaluate the passion and motivation of idea providers and prioritize the adoption of passionate ideas. The idea collection unit, for example, builds a system that uses the emotion estimation function to evaluate the passion and motivation of idea providers in real time. For example, it analyzes facial expressions and voice tones to calculate an emotion score. This allows passionate ideas to be prioritized for adoption.

[0062] The idea collection unit uses generative AI to automatically translate ideas from different languages ​​and cultural spheres, enabling evaluation from a global perspective. The idea collection unit, for example, builds a system that uses generative AI to automatically translate ideas from different languages ​​and cultural spheres. For example, it can support multiple languages ​​such as English, French, and Chinese. This allows ideas to be evaluated from a global perspective.

[0063] The idea collection department uses generative AI to collect market data and analyze competitors, allowing it to evaluate the marketability of ideas from multiple angles. The idea collection department, for example, uses generative AI to collect market data and analyze competitors, building a system that evaluates the marketability of ideas from multiple angles. For example, it analyzes past market data and information on competing products. This allows it to evaluate the marketability of ideas from multiple angles.

[0064] The idea collection unit can use the emotion estimation function to analyze the emotional state of the idea provider and prioritize evaluation of ideas with positive emotions. The idea collection unit, for example, uses the emotion estimation function to analyze the emotional state of the idea provider in real time and builds a system that prioritizes evaluation of ideas with positive emotions. For example, it analyzes facial expressions and voice tone and calculates an emotion score. As a result, ideas with positive emotions are prioritized in evaluation.

[0065] The generative AI coding unit can analyze the bug occurrence rate and performance of the code using past project data to evaluate the quality of the code generated by the generative AI. The generative AI coding unit, for example, builds a system that analyzes the bug occurrence rate and performance of the code using past project data to evaluate the quality of the code generated by the generative AI. For example, it analyzes past bug reports and performance data. This makes it possible to evaluate the quality of the code generated by the generative AI.

[0066] The generative AI coding unit can monitor the performance of the code in real time and make automatic corrections as necessary to optimize the code generated by the generative AI. The generative AI coding unit, for example, builds a system that monitors the performance of the code in real time to optimize the code generated by the generative AI. For example, it collects and analyzes performance data during execution. This allows the performance of the code generated by the generative AI to be optimized.

[0067] The generative AI coding unit can use the emotion estimation function to collect user feedback in real time and improve app functionality based on the user's emotions. The generative AI coding unit, for example, uses the emotion estimation function to collect user feedback in real time and build a system that improves app functionality based on the user's emotions. For example, the generative AI coding unit can analyze the user's facial expressions and voice tone to calculate an emotion score. This makes it possible to improve app functionality based on the user's emotions.

[0068] The generative AI coding unit can generate cross-platform compatible code so that the code generated by the generative AI can be compatible with different platforms. For example, the generative AI coding unit builds a system that generates cross-platform compatible code so that the code generated by the generative AI can be compatible with different platforms. For example, it uses frameworks such as React Native and Flutter. This allows the code generated by the generative AI to be compatible with different platforms.

[0069] The generative AI coding unit can perform vulnerability scans in real time and automatically fix any vulnerabilities that are discovered in order to enhance the security of the code generated by the generative AI. The generative AI coding unit, for example, builds a system that performs vulnerability scans in real time in order to enhance the security of the code generated by the generative AI. For example, it uses static analysis tools and dynamic analysis tools. This can enhance the security of the code generated by the generative AI.

[0070] The generative AI coding unit can use the emotion estimation function to optimize the UI / UX of an app based on the user's emotions, improving the user experience. The generative AI coding unit, for example, uses the emotion estimation function to build a system that optimizes the UI / UX of an app based on the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to calculate an emotion score. This allows the UI / UX of the app to be optimized based on the user's emotions, improving the user experience.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The Prompt Instruction Human Resources Aggregation Unit actively recruits experts from different industries and fields to promote the creation of prompts from diverse perspectives. For example, by bringing together experts from technical, design, and marketing fields, the quality of prompts for the generation AI can be improved. In addition, by bringing together people with different cultural backgrounds, it becomes possible to create prompts from a global perspective. Furthermore, by bringing together people of different age groups and genders, it becomes possible to create prompts that are suitable for a diverse user population.

[0073] The Prompt Instructor Aggregator uses generation AI to analyze the success rate and efficiency of past prompts of prompt instructors, enabling it to select the most suitable person. For example, it evaluates the execution results and time required for each prompt and selects the most suitable person. This improves the accuracy of selecting prompt instructors. In addition, by analyzing past successful prompt cases and identifying the factors behind their success, it is possible to share that know-how with other personnel. Furthermore, this data can be used when designing training programs to improve the skills of prompt instructors.

[0074] The Prompt Personnel Aggregation Unit uses generative AI to automatically generate training programs for prompt personnel and provide customized training tailored to each individual's skills. For example, it can provide training ranging from basic training for beginners to advanced training for advanced users. This allows prompt personnel to improve their skills. It can also monitor the progress of the training program in real time and adjust the training content as needed. Furthermore, by evaluating the effectiveness of the training program and continuously improving it, it is possible to maximize the skills of prompt personnel.

[0075] The prompting personnel aggregation unit uses an emotion estimation function to monitor the motivation and stress levels of prompting personnel in real time, enabling it to provide an optimal working environment. For example, it analyzes facial expressions and voice tones to calculate an emotion score. This improves the work efficiency of prompting personnel. In addition, the emotion data can be used to appropriately adjust break timing and workload. Furthermore, the emotion data can be used to design incentive programs to maintain the motivation of prompting personnel.

[0076] The prompting talent aggregation unit can use generation AI to implement virtual projects to test prompting talent's actual prompt creation ability. For example, it can set up a virtual app development project and have the talent create prompts within it. This allows the prompting talent's actual ability to be evaluated. Based on the results of the virtual project, it can also provide feedback to the prompting talent to improve their skills. Furthermore, the virtual project can also be used to evaluate the prompting talent's teamwork and communication skills.

[0077] The prompting talent aggregation unit can use the emotion estimation function to analyze the emotional state of prompting talent and provide feedback to optimize communication within the team. For example, it can suggest areas for improvement in communication based on the emotion score, thereby optimizing communication within the team. In addition, the emotion data can be used to conduct workshops and training to build trust between team members. Furthermore, the emotion data can be used to develop strategies to improve team performance.

[0078] The idea collection department can use generative AI to analyze past successes and failures of idea providers to improve the accuracy of evaluations. For example, it can analyze success rates and causes of failures to improve the accuracy of evaluations. This improves the accuracy of idea evaluations. In addition, it can identify factors behind success based on past successes and share that know-how with other idea providers. Furthermore, by analyzing failures, it can take measures to prevent the same failures from happening again.

[0079] The idea collection unit can use the emotion estimation function to evaluate the passion and motivation of idea contributors and prioritize the adoption of passionate ideas. For example, it can analyze facial expressions and voice tone to calculate an emotion score. This allows passionate ideas to be prioritized for adoption. In addition, based on the emotion data, it can provide feedback to idea contributors to maintain their motivation. Furthermore, it can utilize the emotion data to design incentive programs to draw out the passion of idea contributors.

[0080] The idea collection department can use generative AI to conduct market data and competitive analysis and evaluate the marketability of ideas from multiple angles. For example, it analyzes past market data and information on competing products. This allows for a multifaceted evaluation of the marketability of ideas. Generative AI can also be used to predict market trends and evaluate future marketability. Furthermore, through competitive analysis, it is possible to identify points of differentiation for ideas and develop strategies to increase competitive advantage.

[0081] The generative AI coding unit uses emotion estimation to collect user feedback in real time and improve app functionality based on the user's emotions. For example, it analyzes the user's facial expressions and tone of voice to calculate an emotion score. This allows the app's functionality to be improved based on the user's emotions. Furthermore, the emotion data can be used to understand the user's needs and desires and customize the app's functionality. Furthermore, the emotion data can be used to suggest new features to improve the user experience.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The Prompt Instruction Human Resources Aggregation Unit will assemble personnel who can provide appropriate prompts to the Generative AI. For example, they will gather personnel who can provide prompts to the Generative AI, such as "create a chatbot that analyzes the text entered by the user and generates appropriate replies." Furthermore, by incorporating the Prompt Instruction Human Resources Aggregation Unit, they will create a system to support the realization of ideas in an organized manner. Step 2: The idea collection department receives ideas from people who have them. For example, if an idea provider has an idea for "creating a health management app," the department evaluates the marketability and feasibility of the idea. Step 3: The Generative AI Coding Department uses the Generative AI to code the smartphone app based on the ideas received from the Idea Collection Department. For example, if a prompt is input such as "Create an app that allows users to enter food records, analyze the data, and display nutritional balance," the Generative AI will generate the app code based on the instructions.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. a prompt instruction talent aggregation unit that gathers talent who can give appropriate prompts to the generation AI; The idea collection department takes in ideas from people who have them, A generation AI coding unit that codes smartphone apps using a generation AI based on the ideas received by the idea collection unit. A system characterized by:

2. The prompt instruction human resource aggregation unit The generation AI is used to analyze the success rate and efficiency of past prompts of prompting personnel, and select the most suitable personnel.

2. The system of claim 1.

3. The prompt instruction human resource aggregation unit Actively recruit experts from different industries and fields to encourage prompt creation from diverse perspectives.

2. The system of claim 1.

4. The idea collection unit The generative AI will be used to analyze the idea provider's past successes and failures, improving the accuracy of the evaluation.

2. The system of claim 1.

5. The generating AI coding unit: To evaluate the quality of the code generated by the AI, the bug rate and performance of the code are analyzed using past project data.

2. The system of claim 1.

6. The prompt instruction human resource aggregation unit Emotion estimation function provides prompt instructions Real-time monitoring of employee motivation and stress levels to provide an optimal working environment 2. The system of claim 1.

7. The idea collection unit Evaluate the passion and motivation of idea contributors using emotion estimation function, and prioritize adoption of passionate ideas.

2. The system of claim 1.

8. The generating AI coding unit: Use emotion estimation to collect user feedback in real time and improve app functionality based on the user's emotions.

2. The system of claim 1.

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