System

Abstract

An object of a system according to an embodiment is to cope with a decrease in labor force population and promote work efficiency and diversity & inclusion.SOLUTION: A system includes a work efficiency improvement part, a disabled person support part, and a diversity promotion part. The business efficiency improvement unit improves the efficiency of business. The disabled person support unit supports the disabled person to participate in the work. The diversity propulsion unit propels diversity and inclusion.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 have not adequately addressed the declining labor force, and there is room for improvement.

[0005] The system according to the embodiment aims to address the decline in the labor force and promote business efficiency and diversity and inclusion. [Means for solving the problem]

[0006] The system according to the embodiment includes a business efficiency improvement department, a disability support department, and a diversity promotion department. The business efficiency improvement department aims to improve business efficiency. The disability support department supports the labor participation of people with disabilities. The diversity promotion department promotes diversity and inclusion. [Effects of the Invention]

[0007] The system according to the embodiment can address the decline in the labor force and promote business efficiency and diversity and inclusion. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 labor utilization system according to an embodiment of the present invention is a system that utilizes AI and automation technology to improve business efficiency and utilize people with disabilities as labor, as a countermeasure against the decline in the labor force due to the declining birthrate and aging population. As a result, the labor utilization system can resolve labor shortages and contribute to the promotion of diversity and inclusion.

[0029] A labor utilization system according to an embodiment includes a business efficiency improvement unit, a disability support unit, and a diversity promotion unit. The business efficiency improvement unit improves business efficiency. For example, the generation AI automates routine tasks such as data entry, analysis, and document preparation. The generation AI also receives prompts containing user instructions as input information and performs tasks based on those instructions. For example, the generation AI receives instructions such as "enter sales data and create a monthly report," enters data, and creates a report. The disability support unit supports the labor participation of people with disabilities. For example, the generation AI uses voice recognition technology and visual assistance technology to support people with disabilities in performing their tasks. The generation AI provides customized support tailored to the needs of people with disabilities. For example, it provides audio guides for visually impaired people and text displays for hearing impaired people. The diversity promotion unit promotes diversity and inclusion. For example, the generation AI analyzes workers' skills and aptitudes and assigns them optimal tasks. The generation AI receives data on workers' skills and aptitudes as input information and suggests optimal tasks based on that data. For example, tasks can be assigned in the following manner: "This worker is good at data analysis, so he or she will be in charge of data analysis work." As a result, the labor force utilization system according to the embodiment can utilize AI and automation technology to improve work efficiency and support the labor participation of people with disabilities, thereby promoting diversity and inclusion, as a measure against the decline in the labor force population due to the declining birthrate and aging population.

[0030] The business efficiency improvement unit can use the generation AI to monitor the progress of work in real time and automatically reallocate tasks as necessary. For example, the business efficiency improvement unit uses the generation AI to monitor the progress of each task in real time and reallocate tasks to other workers if a delay occurs. For example, the generation AI can work with a project management tool to analyze progress data and reallocate tasks. The generation AI can also analyze worker schedules and automatically assign tasks to workers who have free time or leeway. For example, the generation AI can work with a calendar app to detect workers' free time and reallocate tasks. The generation AI can also analyze task priorities and reallocate tasks with higher importance as a priority. For example, tasks can be assigned to the most suitable worker based on the task's importance score. This makes it possible to monitor the progress of work in real time and automatically reallocate tasks, thereby improving business efficiency.

[0031] The business efficiency improvement department can use generative AI to analyze business performance data and propose optimal work procedures. For example, the business efficiency improvement department uses generative AI to analyze past business data and propose the most efficient work procedures. For example, it can optimize data entry procedures and reduce input errors. The generative AI can also monitor workers' work performance in real time and propose efficient work procedures. For example, it can optimize the order of work and reduce unnecessary movements. The generative AI can also identify bottlenecks in business and propose ways to resolve them. For example, if a particular task is delayed, it can propose procedures to make that task more efficient. In this way, business efficiency can be maximized by analyzing business performance data and proposing optimal work procedures.

[0032] The disability support department can use generative AI to analyze the skill sets of people with disabilities and automatically generate optimal training programs. For example, the disability support department uses generative AI to analyze the skill sets of people with disabilities and automatically generate individually optimized training programs. For example, it provides audio-guided training for visually impaired people. The generative AI also customizes training programs based on the skill data of people with disabilities. For example, it provides text-based training for people with hearing impairments. The generative AI also analyzes the skill sets of people with disabilities in real time and dynamically adjusts the training programs. For example, it changes the training content according to their progress. In this way, by analyzing the skill sets of people with disabilities and automatically generating optimal training programs, it is possible to support the workforce participation of people with disabilities.

[0033] The disability support department can use generative AI to monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, the disability support department uses generative AI to monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, it automatically adjusts screen contrast for people with visual impairments. In addition, the generative AI suggests optimal environmental settings based on data on the work environment of people with disabilities. For example, it changes voice notifications to text notifications for people with hearing impairments. In addition, the generative AI analyzes the work environment of people with disabilities in real time and dynamically adjusts environmental settings. For example, it automatically adjusts lighting and volume while working. This allows the work environment of people with disabilities to be monitored in real time and automatically adjusts environmental settings as needed, thereby supporting workforce participation for people with disabilities.

[0034] The Diversity Promotion Department can use generative AI to analyze the diverse backgrounds of workers and propose optimal team formations. For example, the Diversity Promotion Department uses generative AI to analyze workers' background data and propose optimal team formations. For example, it may form a team that combines workers with different skill sets. The generative AI also customizes team formations based on the workers' diverse backgrounds. For example, it may form a team that combines workers with different cultural backgrounds. The generative AI also analyzes workers' background data in real time and dynamically adjusts the optimal team formation. For example, it changes team members according to the progress of a project. In this way, the Diversity Promotion Department can promote diversity and inclusion by analyzing the diverse backgrounds of workers and proposing optimal team formations.

[0035] The Diversity Promotion Department can use generative AI to analyze workers' skills and aptitudes and suggest career paths. For example, the Diversity Promotion Department uses generative AI to analyze workers' skill data and suggest optimal career paths. For example, it could suggest a data scientist career path to a worker who excels in data analysis. The generative AI can also customize career paths based on workers' aptitude data. For example, it could suggest a managerial career path to a worker with leadership skills. The generative AI can also analyze workers' skills and aptitudes in real time and dynamically adjust career paths. For example, it could change career paths as skills improve. In this way, the Diversity Promotion Department can promote diversity and inclusion by analyzing workers' skills and aptitudes and suggesting career paths.

[0036] The Diversity Promotion Department can use generative AI to utilize the diverse skill sets of workers and propose strategies to increase the probability of project success. For example, the Diversity Promotion Department uses generative AI to analyze workers' skill sets and propose strategies to increase the probability of project success. For example, it assigns workers with specific skills to a project. Furthermore, based on the workers' diverse skill sets, the generative AI proposes team formations to increase the probability of project success. For example, it forms a team that combines workers with different skills. Furthermore, the generative AI analyzes workers' skill sets in real time and dynamically adjusts strategies according to the progress of the project. For example, it optimizes skill sets according to the progress of the project. In this way, it is possible to promote diversity and inclusion by utilizing the diverse skill sets of workers and proposing strategies to increase the probability of project success.

[0037] The Diversity Promotion Department can use generative AI to suggest rotations to different tasks based on workers' aptitudes, promoting skill diversification. For example, the Diversity Promotion Department uses generative AI to analyze workers' aptitude data and suggest rotations to different tasks. For example, it could suggest marketing tasks to workers who are good at data analysis. Furthermore, based on workers' aptitude data, generative AI can suggest rotation plans to promote skill diversification. For example, it could suggest rotations to gain work experience in different departments. Furthermore, generative AI can analyze workers' aptitudes in real time and dynamically adjust job rotations. For example, it could change the rotation plan as skills improve. In this way, it is possible to promote diversity and inclusion by suggesting rotations to different tasks based on workers' aptitudes and promoting skill diversification.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The business efficiency improvement department can use the generation AI to monitor the progress of work in real time and automatically reallocate tasks as necessary. For example, the generation AI monitors the progress of each task in real time and reallocates the task to another worker if a delay occurs. For example, it can work with a project management tool to analyze progress data and reallocate tasks. The generation AI can also analyze worker schedules and automatically assign tasks to workers who have free time or leeway. For example, it can work with a calendar app to detect workers' free time and reallocate tasks. The generation AI can also analyze task priorities and reallocate tasks with higher importance as a priority. For example, it can assign tasks to the most suitable worker based on the task's importance score. This makes it possible to monitor the progress of work in real time and automatically reallocate tasks, thereby improving business efficiency.

[0040] The Business Efficiency Department can use generative AI to analyze business performance data and propose optimal work procedures. For example, generative AI can analyze past business data and propose the most efficient work procedures. For example, it can optimize data entry procedures and reduce input errors. Generative AI can also monitor workers' work performance in real time and propose efficient work procedures. For example, it can optimize the order of work and reduce unnecessary movements. Generative AI can also identify bottlenecks in business and propose ways to resolve them. For example, if a particular task is delayed, it can propose procedures to make that task more efficient. In this way, business efficiency can be maximized by analyzing business performance data and proposing optimal work procedures.

[0041] The disability support department can use generative AI to analyze the skill sets of people with disabilities and automatically generate optimal training programs. For example, generative AI can analyze the skill sets of people with disabilities and automatically generate individually optimized training programs. For example, it can provide audio-guided training for people with visual impairments. Generative AI can also customize training programs based on the skill data of people with disabilities. For example, it can provide text-based training for people with hearing impairments. Generative AI can also analyze the skill sets of people with disabilities in real time and dynamically adjust the training programs. For example, it can change the training content according to their progress. In this way, analyzing the skill sets of people with disabilities and automatically generating optimal training programs can support their participation in the workforce.

[0042] The disability support department can use generative AI to monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, generative AI can monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, it can automatically adjust screen contrast for people with visual impairments. In addition, based on data on the work environment of people with disabilities, generative AI can suggest optimal environmental settings. For example, it can change voice notifications to text notifications for people with hearing impairments. In addition, generative AI can analyze the work environment of people with disabilities in real time and dynamically adjust environmental settings. For example, it can automatically adjust lighting and volume while working. This allows for real-time monitoring of the work environment of people with disabilities and automatically adjust environmental settings as needed, thereby supporting workforce participation for people with disabilities.

[0043] The Diversity Promotion Department can use generative AI to analyze the diverse backgrounds of workers and propose optimal team composition. For example, generative AI analyzes workers' background data and proposes optimal team composition. For example, it may form a team that combines workers with different skill sets. Generative AI can also customize team composition based on the workers' diverse backgrounds. For example, it may form a team that combines workers with different cultural backgrounds. Generative AI can also analyze workers' background data in real time and dynamically adjust the optimal team composition. For example, it may change team members according to the progress of a project. In this way, it is possible to promote diversity and inclusion by analyzing the diverse backgrounds of workers and proposing optimal team composition.

[0044] The Diversity Promotion Department can use generative AI to analyze workers' skills and aptitudes and suggest career paths. For example, generative AI analyzes workers' skill data and suggests the most suitable career path. For example, it could suggest a data scientist career path to a worker who excels in data analysis. Generative AI can also customize career paths based on workers' aptitude data. For example, it could suggest a managerial career path to a worker with leadership skills. Generative AI can also analyze workers' skills and aptitudes in real time and dynamically adjust career paths. For example, it could change career paths as skills improve. In this way, analyzing workers' skills and aptitudes and suggesting career paths can promote diversity and inclusion.

[0045] The Diversity Promotion Department can use generative AI to utilize the diverse skill sets of workers and propose strategies to increase the probability of project success. For example, generative AI analyzes workers' skill sets and proposes strategies to increase the probability of project success. For example, it assigns workers with specific skills to a project. Based on the diverse skill sets of workers, generative AI also proposes team formations to increase the probability of project success. For example, it forms a team that combines workers with different skills. Generative AI also analyzes workers' skill sets in real time and dynamically adjusts strategies according to the progress of the project. For example, it optimizes skill sets according to the progress of the project. This makes it possible to promote diversity and inclusion by utilizing the diverse skill sets of workers and proposing strategies to increase the probability of project success.

[0046] The Diversity Promotion Department can use generative AI to suggest rotations to different tasks based on workers' aptitudes, promoting skill diversification. For example, generative AI analyzes workers' aptitude data and suggests rotations to different tasks. For example, it could suggest marketing tasks to workers who are good at data analysis. Furthermore, based on workers' aptitude data, generative AI proposes rotation plans to promote skill diversification. For example, it could suggest rotations to gain work experience in different departments. Generative AI also analyzes workers' aptitudes in real time and dynamically adjusts job rotations. For example, it could change the rotation plan as skills improve. This makes it possible to promote diversity and inclusion by suggesting rotations to different tasks based on workers' aptitudes and promoting skill diversification.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The Business Efficiency Department aims to improve business efficiency. For example, the Generative AI automates routine tasks such as data entry, analysis, and document creation. The Generative AI also receives prompts containing instructions from the user as input information and carries out tasks based on those instructions. For example, the Generative AI receives instructions such as "enter sales data and create a monthly report," enters the data, and creates the report. Step 2: The Disability Support Department supports the workforce participation of people with disabilities. For example, the generative AI uses voice recognition technology and visual assistance technology to support people with disabilities in carrying out their work. The generative AI provides customized assistance according to the needs of people with disabilities. For example, it provides audio guides for the visually impaired and text displays for the hearing impaired. Step 3: The Diversity Promotion Department promotes diversity and inclusion. For example, the Generative AI analyzes the skills and aptitudes of workers and assigns them the most suitable tasks. The Generative AI receives data on the skills and aptitudes of workers as input and suggests the most suitable tasks based on that data. For example, it assigns tasks in the following way: "This worker is good at data analysis, so he or she will be in charge of data analysis tasks."

[0049] (Example 2) The labor utilization system according to an embodiment of the present invention is a system that utilizes AI and automation technology to improve business efficiency and utilize people with disabilities as labor, as a countermeasure against the decline in the labor force due to the declining birthrate and aging population. As a result, the labor utilization system can resolve labor shortages and contribute to the promotion of diversity and inclusion.

[0050] A labor utilization system according to an embodiment includes a business efficiency improvement unit, a disability support unit, and a diversity promotion unit. The business efficiency improvement unit improves business efficiency. For example, the generation AI automates routine tasks such as data entry, analysis, and document preparation. The generation AI also receives prompts containing user instructions as input information and performs tasks based on those instructions. For example, the generation AI receives instructions such as "enter sales data and create a monthly report," enters data, and creates a report. The disability support unit supports the labor participation of people with disabilities. For example, the generation AI uses voice recognition technology and visual assistance technology to support people with disabilities in performing their tasks. The generation AI provides customized support tailored to the needs of people with disabilities. For example, it provides audio guides for visually impaired people and text displays for hearing impaired people. The diversity promotion unit promotes diversity and inclusion. For example, the generation AI analyzes workers' skills and aptitudes and assigns them optimal tasks. The generation AI receives data on workers' skills and aptitudes as input information and suggests optimal tasks based on that data. For example, tasks can be assigned in the following manner: "This worker is good at data analysis, so he or she will be in charge of data analysis work." As a result, the labor force utilization system according to the embodiment can utilize AI and automation technology to improve work efficiency and support the labor participation of people with disabilities, thereby promoting diversity and inclusion, as a measure against the decline in the labor force population due to the declining birthrate and aging population.

[0051] The business efficiency improvement unit can use the generation AI to monitor the progress of work in real time and automatically reallocate tasks as necessary. For example, the business efficiency improvement unit uses the generation AI to monitor the progress of each task in real time and reallocate tasks to other workers if a delay occurs. For example, the generation AI can work with a project management tool to analyze progress data and reallocate tasks. The generation AI can also analyze worker schedules and automatically assign tasks to workers who have free time or leeway. For example, the generation AI can work with a calendar app to detect workers' free time and reallocate tasks. The generation AI can also analyze task priorities and reallocate tasks with higher importance as a priority. For example, tasks can be assigned to the most suitable worker based on the task's importance score. This makes it possible to monitor the progress of work in real time and automatically reallocate tasks, thereby improving business efficiency.

[0052] The business efficiency improvement department can use generative AI to analyze business performance data and propose optimal work procedures. For example, the business efficiency improvement department uses generative AI to analyze past business data and propose the most efficient work procedures. For example, it can optimize data entry procedures and reduce input errors. The generative AI can also monitor workers' work performance in real time and propose efficient work procedures. For example, it can optimize the order of work and reduce unnecessary movements. The generative AI can also identify bottlenecks in business and propose ways to resolve them. For example, if a particular task is delayed, it can propose procedures to make that task more efficient. In this way, business efficiency can be maximized by analyzing business performance data and proposing optimal work procedures.

[0053] The work efficiency improvement unit can use the emotion estimation function to analyze the emotional state of the worker and suggest tasks for relaxation if stress increases. For example, the work efficiency improvement unit uses the emotion estimation function to monitor the worker's stress level in real time and suggest tasks for relaxation if stress increases. For example, it suggests a short break or light exercise. It also analyzes the worker's emotional state and automatically assigns tasks for relaxation if stress increases. For example, it suggests time to listen to relaxing music. It also suggests tasks to reduce the worker's stress level based on the emotion estimation data. For example, it suggests relaxation exercises to relieve stress. In this way, the working environment can be improved by analyzing the worker's emotional state and suggesting tasks for relaxation if stress increases.

[0054] The disability support department can use generative AI to analyze the skill sets of people with disabilities and automatically generate optimal training programs. For example, the disability support department uses generative AI to analyze the skill sets of people with disabilities and automatically generate individually optimized training programs. For example, it provides audio-guided training for visually impaired people. The generative AI also customizes training programs based on the skill data of people with disabilities. For example, it provides text-based training for people with hearing impairments. The generative AI also analyzes the skill sets of people with disabilities in real time and dynamically adjusts the training programs. For example, it changes the training content according to their progress. In this way, by analyzing the skill sets of people with disabilities and automatically generating optimal training programs, it is possible to support the workforce participation of people with disabilities.

[0055] The disability support department can use generative AI to monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, the disability support department uses generative AI to monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, it automatically adjusts screen contrast for people with visual impairments. In addition, the generative AI suggests optimal environmental settings based on data on the work environment of people with disabilities. For example, it changes voice notifications to text notifications for people with hearing impairments. In addition, the generative AI analyzes the work environment of people with disabilities in real time and dynamically adjusts environmental settings. For example, it automatically adjusts lighting and volume while working. This allows the work environment of people with disabilities to be monitored in real time and automatically adjusts environmental settings as needed, thereby supporting workforce participation for people with disabilities.

[0056] The disability support unit can use the emotion estimation function to analyze the emotional state of a person with a disability and provide appropriate support if stress increases. For example, the disability support unit can use the emotion estimation function to monitor the emotional state of a person with a disability in real time and provide appropriate support if stress increases. For example, it can suggest a break to relax. Furthermore, based on the emotional data of a person with a disability, it can automatically provide support if stress increases. For example, it can suggest relaxation exercises to relieve stress. Furthermore, based on the emotion estimation data, it can provide support to reduce the stress level of a person with a disability. For example, it can suggest counseling if stress increases. In this way, by analyzing the emotional state of a person with a disability and providing appropriate support if stress increases, it is possible to support the workforce participation of people with disabilities.

[0057] The Diversity Promotion Department can use generative AI to analyze the diverse backgrounds of workers and propose optimal team formations. For example, the Diversity Promotion Department uses generative AI to analyze workers' background data and propose optimal team formations. For example, it may form a team that combines workers with different skill sets. The generative AI also customizes team formations based on the workers' diverse backgrounds. For example, it may form a team that combines workers with different cultural backgrounds. The generative AI also analyzes workers' background data in real time and dynamically adjusts the optimal team formation. For example, it changes team members according to the progress of a project. In this way, the Diversity Promotion Department can promote diversity and inclusion by analyzing the diverse backgrounds of workers and proposing optimal team formations.

[0058] The Diversity Promotion Department can use generative AI to analyze workers' skills and aptitudes and suggest career paths. For example, the Diversity Promotion Department uses generative AI to analyze workers' skill data and suggest optimal career paths. For example, it could suggest a data scientist career path to a worker who excels in data analysis. The generative AI can also customize career paths based on workers' aptitude data. For example, it could suggest a managerial career path to a worker with leadership skills. The generative AI can also analyze workers' skills and aptitudes in real time and dynamically adjust career paths. For example, it could change career paths as skills improve. In this way, the Diversity Promotion Department can promote diversity and inclusion by analyzing workers' skills and aptitudes and suggesting career paths.

[0059] The diversity promotion department can use the emotion estimation function to analyze the emotional state of workers and make suggestions to smooth communication within the team. For example, the diversity promotion department can use the emotion estimation function to monitor the emotional state of workers in real time and make suggestions to smooth communication. For example, if stress levels increase, the department can suggest relaxation activities. The department can also make suggestions to improve communication within the team based on the emotional data of workers. For example, the department can suggest team building activities according to their emotional state. The department can also analyze the emotional state of workers based on the emotion estimation data and provide feedback to smooth communication. For example, the department can adjust the communication style according to their emotional state. This makes it possible to promote diversity and inclusion by analyzing the emotional state of workers and making suggestions to smooth communication within the team.

[0060] The Diversity Promotion Department can use generative AI to utilize the diverse skill sets of workers and propose strategies to increase the probability of project success. For example, the Diversity Promotion Department uses generative AI to analyze workers' skill sets and propose strategies to increase the probability of project success. For example, it assigns workers with specific skills to a project. Furthermore, based on the workers' diverse skill sets, the generative AI proposes team formations to increase the probability of project success. For example, it forms a team that combines workers with different skills. Furthermore, the generative AI analyzes workers' skill sets in real time and dynamically adjusts strategies according to the progress of the project. For example, it optimizes skill sets according to the progress of the project. In this way, it is possible to promote diversity and inclusion by utilizing the diverse skill sets of workers and proposing strategies to increase the probability of project success.

[0061] The Diversity Promotion Department can use generative AI to suggest rotations to different tasks based on workers' aptitudes, promoting skill diversification. For example, the Diversity Promotion Department uses generative AI to analyze workers' aptitude data and suggest rotations to different tasks. For example, it could suggest marketing tasks to workers who are good at data analysis. Furthermore, based on workers' aptitude data, generative AI can suggest rotation plans to promote skill diversification. For example, it could suggest rotations to gain work experience in different departments. Furthermore, generative AI can analyze workers' aptitudes in real time and dynamically adjust job rotations. For example, it could change the rotation plan as skills improve. In this way, it is possible to promote diversity and inclusion by suggesting rotations to different tasks based on workers' aptitudes and promoting skill diversification.

[0062] The diversity promotion department can use the emotion estimation function to suggest team building activities based on workers' emotions and increase team cohesion. The diversity promotion department can, for example, use the emotion estimation function to monitor workers' emotional states in real time and suggest team building activities. For example, if stress levels increase, the department can suggest relaxation activities. The department can also suggest team building activities to increase team cohesion based on workers' emotional data. For example, the department can suggest team building events according to their emotional states. The department can also analyze workers' emotional states based on the emotion estimation data and provide feedback to increase team cohesion. For example, the department can adjust communication styles according to their emotional states. This makes it possible to suggest team building activities based on workers' emotions and increase team cohesion, thereby promoting diversity and inclusion.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The business efficiency improvement department can use the generation AI to monitor the progress of work in real time and automatically reallocate tasks as necessary. For example, the generation AI monitors the progress of each task in real time and reallocates the task to another worker if a delay occurs. For example, it can work with a project management tool to analyze progress data and reallocate tasks. The generation AI can also analyze worker schedules and automatically assign tasks to workers who have free time or leeway. For example, it can work with a calendar app to detect workers' free time and reallocate tasks. The generation AI can also analyze task priorities and reallocate tasks with higher importance as a priority. For example, it can assign tasks to the most suitable worker based on the task's importance score. This makes it possible to monitor the progress of work in real time and automatically reallocate tasks, thereby improving business efficiency.

[0065] The Business Efficiency Department can use generative AI to analyze business performance data and propose optimal work procedures. For example, generative AI can analyze past business data and propose the most efficient work procedures. For example, it can optimize data entry procedures and reduce input errors. Generative AI can also monitor workers' work performance in real time and propose efficient work procedures. For example, it can optimize the order of work and reduce unnecessary movements. Generative AI can also identify bottlenecks in business and propose ways to resolve them. For example, if a particular task is delayed, it can propose procedures to make that task more efficient. In this way, business efficiency can be maximized by analyzing business performance data and proposing optimal work procedures.

[0066] The business efficiency improvement department can use the emotion estimation function to analyze the emotional state of workers and suggest tasks for relaxation if stress increases. For example, the emotion estimation function can be used to monitor the stress level of workers in real time and suggest tasks for relaxation if stress increases. For example, it can suggest a short break or light exercise. It can also analyze the emotional state of workers and automatically assign tasks for relaxation if stress increases. For example, it can suggest time to listen to relaxing music. It can also suggest tasks to reduce the worker's stress level based on the emotion estimation data. For example, it can suggest relaxation exercises to relieve stress. In this way, the working environment can be improved by analyzing the emotional state of workers and suggesting tasks for relaxation if stress increases.

[0067] The disability support department can use generative AI to analyze the skill sets of people with disabilities and automatically generate optimal training programs. For example, generative AI can analyze the skill sets of people with disabilities and automatically generate individually optimized training programs. For example, it can provide audio-guided training for people with visual impairments. Generative AI can also customize training programs based on the skill data of people with disabilities. For example, it can provide text-based training for people with hearing impairments. Generative AI can also analyze the skill sets of people with disabilities in real time and dynamically adjust the training programs. For example, it can change the training content according to their progress. In this way, analyzing the skill sets of people with disabilities and automatically generating optimal training programs can support their participation in the workforce.

[0068] The disability support department can use generative AI to monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, generative AI can monitor the work environment of people with disabilities in real time and automatically adjust environmental settings as needed. For example, it can automatically adjust screen contrast for people with visual impairments. In addition, based on data on the work environment of people with disabilities, generative AI can suggest optimal environmental settings. For example, it can change voice notifications to text notifications for people with hearing impairments. In addition, generative AI can analyze the work environment of people with disabilities in real time and dynamically adjust environmental settings. For example, it can automatically adjust lighting and volume while working. This allows for real-time monitoring of the work environment of people with disabilities and automatically adjust environmental settings as needed, thereby supporting workforce participation for people with disabilities.

[0069] The disability support department can use the emotion estimation function to analyze the emotional state of persons with disabilities and provide appropriate support if stress levels rise. For example, the emotion estimation function can be used to monitor the emotional state of persons with disabilities in real time and provide appropriate support if stress levels rise, for example, by suggesting a break to relax. In addition, support can be automatically provided if stress levels rise based on the emotional data of persons with disabilities, for example, by suggesting relaxation exercises to relieve stress. In addition, support can be provided to reduce the stress levels of persons with disabilities based on the emotion estimation data, for example, by suggesting counseling if stress levels rise. In this way, by analyzing the emotional state of persons with disabilities and providing appropriate support if stress levels rise, it is possible to support the participation of persons with disabilities in the workforce.

[0070] The Diversity Promotion Department can use generative AI to analyze the diverse backgrounds of workers and propose optimal team composition. For example, generative AI analyzes workers' background data and proposes optimal team composition. For example, it may form a team that combines workers with different skill sets. Generative AI can also customize team composition based on the workers' diverse backgrounds. For example, it may form a team that combines workers with different cultural backgrounds. Generative AI can also analyze workers' background data in real time and dynamically adjust the optimal team composition. For example, it may change team members according to the progress of a project. In this way, it is possible to promote diversity and inclusion by analyzing the diverse backgrounds of workers and proposing optimal team composition.

[0071] The Diversity Promotion Department can use generative AI to analyze workers' skills and aptitudes and suggest career paths. For example, generative AI analyzes workers' skill data and suggests the most suitable career path. For example, it could suggest a data scientist career path to a worker who excels in data analysis. Generative AI can also customize career paths based on workers' aptitude data. For example, it could suggest a managerial career path to a worker with leadership skills. Generative AI can also analyze workers' skills and aptitudes in real time and dynamically adjust career paths. For example, it could change career paths as skills improve. In this way, analyzing workers' skills and aptitudes and suggesting career paths can promote diversity and inclusion.

[0072] The Diversity Promotion Department can use the emotion estimation function to analyze the emotional state of workers and make suggestions to smooth communication within the team. For example, the emotion estimation function can be used to monitor the emotional state of workers in real time and make suggestions to smooth communication. For example, if stress levels rise, the function can suggest relaxation activities. The department can also make suggestions to improve communication within the team based on the workers' emotional data. For example, the function can suggest team building activities based on the workers' emotional state. The department can also analyze the workers' emotional state based on the emotion estimation data and provide feedback to smooth communication. For example, the department can adjust the communication style according to the workers' emotional state. This makes it possible to promote diversity and inclusion by analyzing the workers' emotional state and making suggestions to smooth communication within the team.

[0073] The Diversity Promotion Department can use generative AI to utilize the diverse skill sets of workers and propose strategies to increase the probability of project success. For example, generative AI analyzes workers' skill sets and proposes strategies to increase the probability of project success. For example, it assigns workers with specific skills to a project. Based on the diverse skill sets of workers, generative AI also proposes team formations to increase the probability of project success. For example, it forms a team that combines workers with different skills. Generative AI also analyzes workers' skill sets in real time and dynamically adjusts strategies according to the progress of the project. For example, it optimizes skill sets according to the progress of the project. This makes it possible to promote diversity and inclusion by utilizing the diverse skill sets of workers and proposing strategies to increase the probability of project success.

[0074] The Diversity Promotion Department can use generative AI to suggest rotations to different tasks based on workers' aptitudes, promoting skill diversification. For example, generative AI analyzes workers' aptitude data and suggests rotations to different tasks. For example, it could suggest marketing tasks to workers who are good at data analysis. Furthermore, based on workers' aptitude data, generative AI proposes rotation plans to promote skill diversification. For example, it could suggest rotations to gain work experience in different departments. Generative AI also analyzes workers' aptitudes in real time and dynamically adjusts job rotations. For example, it could change the rotation plan as skills improve. This makes it possible to promote diversity and inclusion by suggesting rotations to different tasks based on workers' aptitudes and promoting skill diversification.

[0075] The Diversity Promotion Department can use the emotion estimation function to suggest team building activities based on workers' emotions and increase team cohesion. For example, the emotion estimation function can be used to monitor workers' emotional states in real time and suggest team building activities. For example, if stress levels increase, the function can suggest relaxation activities. Furthermore, the function can suggest team building activities to increase team cohesion based on workers' emotional data. For example, team building events can be suggested according to their emotional states. Furthermore, the emotion estimation data can be used to analyze workers' emotional states and provide feedback to increase team cohesion. For example, the communication style can be adjusted according to their emotional states. In this way, the function can suggest team building activities based on workers' emotions and increase team cohesion, thereby promoting diversity and inclusion.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The Business Efficiency Department aims to improve business efficiency. For example, the Generative AI automates routine tasks such as data entry, analysis, and document creation. The Generative AI also receives prompts containing instructions from the user as input information and carries out tasks based on those instructions. For example, the Generative AI receives instructions such as "enter sales data and create a monthly report," enters the data, and creates the report. Step 2: The Disability Support Department supports the workforce participation of people with disabilities. For example, the generative AI uses voice recognition technology and visual assistance technology to support people with disabilities in carrying out their work. The generative AI provides customized assistance according to the needs of people with disabilities. For example, it provides audio guides for the visually impaired and text displays for the hearing impaired. Step 3: The Diversity Promotion Department promotes diversity and inclusion. For example, the Generative AI analyzes the skills and aptitudes of workers and assigns them the most suitable tasks. The Generative AI receives data on the skills and aptitudes of workers as input and suggests the most suitable tasks based on that data. For example, it assigns tasks in the following way: "This worker is good at data analysis, so he or she will be in charge of data analysis tasks."

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0091] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 7, the 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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. The Business Efficiency Department aims to improve business efficiency, The Disability Support Department, which supports the workforce participation of people with disabilities, The company will have a Diversity Promotion Department that promotes diversity and inclusion. A system characterized by:

2. The business efficiency department Uses generative AI to monitor work progress in real time and automatically reassign tasks as needed 2. The system of claim 1.

3. The business efficiency department Analyzes business performance data using generative AI and proposes optimal work procedures 2. The system of claim 1.

4. The business efficiency department Analyzes the emotional state of workers and suggests tasks to relax them if they are under stress 2. The system of claim 1.

5. The disability support department Using generative AI to analyze the skill sets of people with disabilities and automatically generate optimal training programs 2. The system of claim 1.

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