system

Abstract

The system according to this embodiment aims to support employee growth by utilizing the CEO's behavioral data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects CEO behavior data. The analysis unit analyzes the data collected by the collection unit and learns the CEO's behavior patterns. The generation unit generates an AI CEO based on the behavior patterns learned by the analysis unit. The provision unit provides feedback to employees using the AI ​​CEO generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for the CEO to directly participate in all employees, and the growth opportunities for employees are limited.

[0005] The system according to the embodiment aims to support the growth of employees by utilizing the action data of the CEO.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects CEO behavior data. The analysis unit analyzes the data collected by the collection unit and learns the CEO's behavior patterns. The generation unit generates an AI CEO based on the behavior patterns learned by the analysis unit. The provision unit provides feedback to employees using the AI ​​CEO generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can support employee growth by utilizing the CEO's behavioral data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The CEO Complementary Planning System according to an embodiment of the present invention is a system aimed at the growth and innovation of all employees of the SoftBank Group. This system aims to divide the CEO into a "True CEO" and an "AI CEO". The True CEO will be dedicated to vision and strategy creation, while the AI ​​CEO will specialize in talent development. By utilizing the CEO behavioral data held by the SoftBank Group, the most accurate AI CEO will be generated, and all employees will be able to make the AI ​​CEO their boss. This plan is expected to accelerate employee growth and strengthen the competitiveness of the entire organization. Furthermore, this technology will be deployed to society and popularized as a service that supports the growth of highly motivated business people, contributing to the improvement of productivity in Japan. For example, behavioral data of the SoftBank Group's CEO will be collected. This data will include the CEO's past statements, behavioral patterns, and decision-making processes. Next, the collected data will be analyzed by an AI to learn the CEO's behavioral patterns. This will generate an AI CEO. The generated AI CEO can be requested by all employees to have a 1-on-1 meeting anytime, anywhere. For example, when an employee approaches the AI-CEO with work-related challenges or career advice, the AI-CEO provides appropriate advice based on the CEO's past statements and actions. The AI-CEO can also provide firm encouragement and constructive criticism, thereby promoting employee growth. This system allows employees to grow at their own pace, improving the overall competitiveness of the organization. Furthermore, by deploying the AI-CEO technology to other companies and society as a whole, it is expected to support the growth of highly motivated business professionals and contribute to improving productivity in Japan. In this way, the CEO Complementary Planning System can accelerate employee growth and strengthen the overall competitiveness of the organization.

[0029] The CEO complementary planning system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the CEO's behavioral data. For example, the collection unit collects the CEO's past statements and behavioral patterns. The collection unit can collect the CEO's statements in meetings, email content, schedule, etc. For example, the collection unit can record the CEO's statements in meetings and convert them into text data. The collection unit can also analyze the content of the CEO's emails and extract behavioral patterns. Furthermore, the collection unit can analyze the CEO's schedule and grasp the frequency and timing of actions. The analysis unit analyzes the data collected by the collection unit and learns the CEO's behavioral patterns. For example, the analysis unit extracts the CEO's behavioral patterns using data mining techniques. The analysis unit can also learn the CEO's behavioral patterns using machine learning algorithms. For example, the analysis unit learns the CEO's behavioral patterns using supervised learning. The analysis unit can also cluster the CEO's behavioral patterns using unsupervised learning. Furthermore, the analysis unit can optimize the CEO's behavior patterns using reinforcement learning. The generation unit generates an AI-CEO based on the behavior patterns learned by the analysis unit. The generation unit generates an AI-CEO, for example, using a generation algorithm. The generation unit can also generate an AI-CEO, for example, using a generation algorithm. The generation unit can generate an AI-CEO that reproduces the CEO's behavior patterns, for example, using a generation algorithm. The generation unit can also generate an AI-CEO that mimics the CEO's behavior patterns, for example, using a generation model. Furthermore, the generation unit can generate an AI-CEO that predicts the CEO's behavior patterns, for example, using a generation algorithm. The delivery unit provides feedback to employees from the AI-CEO generated by the generation unit. The delivery unit can, for example, have the AI-CEO provide appropriate advice and guidance to employees. The delivery unit can also have the AI-CEO sternly encourage and motivate employees. The delivery unit can, for example, have the AI-CEO advise employees on improving their work. The delivery unit can also have the AI-CEO provide guidance to employees on improving their skills. Furthermore, the service provider can also have the AI ​​CEO provide career path advice to employees.As a result, the CEO complementation planning system according to this embodiment can promote employee growth and improve the overall competitiveness of the organization.

[0030] The data collection unit collects the CEO's behavioral data. For example, it collects the CEO's past statements and behavioral patterns. Specifically, it records the CEO's statements at meetings and converts them into text data using speech recognition technology. This allows for a detailed record of what is said during meetings, which can then be analyzed later. It also analyzes the CEO's emails to understand what topics they are interested in and what instructions they give. The email content is analyzed using natural language processing technology to extract keywords and perform sentiment analysis. Furthermore, it analyzes the CEO's schedule to understand what activities they engage in at what times, as well as their frequency and patterns. Schedule data is automatically retrieved from calendar applications and scheduling systems and stored in a database. This allows the data collection unit to collect the CEO's behavioral data from multiple angles and understand detailed behavioral patterns. The collected data is managed in a secure environment with thorough privacy protection. For example, the data is encrypted, and access rights are strictly controlled. This allows the data collection unit to safely and efficiently collect the CEO's behavioral data and provide it to the analysis and generation units.

[0031] The analysis unit analyzes the data collected by the collection unit and trains the CEO's behavioral patterns. For example, the analysis unit extracts the CEO's behavioral patterns using data mining techniques. Specifically, it treats the CEO's statements and actions as time-series data and detects frequently occurring patterns and abnormal behaviors. Machine learning algorithms can also be used to train the CEO's behavioral patterns. For example, supervised learning is used to build a model that predicts the CEO's behavioral patterns from past data. This model captures the characteristics of the CEO's statements and actions and is used to predict future actions. Unsupervised learning can also be used to cluster the CEO's behavioral patterns and group similar actions. This makes it easier to grasp the overall picture of the CEO's behavioral patterns. Furthermore, reinforcement learning can be used to optimize the CEO's behavioral patterns. In reinforcement learning, the impact of the CEO's actions on the entire organization is evaluated, and the optimal behavioral pattern is found. This allows the analysis unit to analyze the collected data from multiple angles and train the CEO's behavioral patterns in detail. The analysis results are provided to the generation unit and used to generate the AI ​​CEO.

[0032] The generation unit generates AI-CEOs based on behavioral patterns learned by the analysis unit. The generation unit generates AI-CEOs using, for example, a generation algorithm. Specifically, it uses a generation model to generate AI-CEOs that replicate the CEO's behavioral patterns. Examples of such generation models include generative opposite networks (GANs) and variational autoencoders (VAEs). These models generate AI-CEOs that mimic the CEO's statements and actions based on the behavioral patterns provided by the analysis unit. The generation unit can also use a generation algorithm to generate AI-CEOs that predict the CEO's behavioral patterns. For example, it can predict what statements and actions the CEO will take in a specific situation, and the AI-CEO will respond appropriately based on the results. Furthermore, the generation unit can use a generation model to generate AI-CEOs that optimize the CEO's behavioral patterns. This allows the generation unit to generate advanced AI-CEOs based on data provided by the analysis unit, improving the overall efficiency and effectiveness of the organization. The generated AI-CEOs provide feedback to employees through the service provider unit, supporting the organization's growth.

[0033] The providing unit provides feedback to employees through the AI-CEO generated by the generation unit. Specifically, the AI-CEO provides appropriate advice and guidance to employees. For example, the AI-CEO evaluates employees' work content and performance and provides advice for work improvement. The AI-CEO can generate appropriate answers to employees' questions and consultations using natural language processing technology. The AI-CEO can also provide guidance to improve employees' skills. For example, it can propose training programs to improve specific skills and monitor their progress. Furthermore, the AI-CEO can provide advice on employees' career paths. For example, it can propose the optimal career path based on employees' goals and aspirations and show concrete steps toward achieving it. The AI-CEO can also sternly encourage and motivate employees. For example, it can present specific improvement measures to employees whose performance is sluggish and provide feedback to boost their motivation. In this way, the providing unit can provide employees with multifaceted feedback through the AI-CEO and promote employee growth. As a result, the CEO complementary planning system according to this embodiment can promote employee growth and improve the overall competitiveness of the organization.

[0034] The data collection unit can collect the CEO's past statements and behavioral patterns. For example, the data collection unit can record the CEO's statements in meetings and convert them into text data. It can also analyze the content of the CEO's emails and extract behavioral patterns. Furthermore, the data collection unit can analyze the CEO's schedule to understand the frequency and timing of their activities. By collecting the CEO's past statements and behavioral patterns, more accurate behavioral data can be obtained. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the process of recording the CEO's statements in meetings and converting them into text data.

[0035] The analysis unit can analyze the collected data and train the CEO's behavioral patterns. For example, the analysis unit can extract the CEO's behavioral patterns using data mining techniques. The analysis unit can also train the CEO's behavioral patterns using machine learning algorithms. For example, the analysis unit can train the CEO's behavioral patterns using supervised learning. Furthermore, the analysis unit can cluster the CEO's behavioral patterns using unsupervised learning. In addition, the analysis unit can optimize the CEO's behavioral patterns using reinforcement learning. This improves the accuracy of AI-CEO by analyzing the collected data and training the CEO's behavioral patterns. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the training of the CEO's behavioral patterns.

[0036] The generation unit can generate an AI CEO based on learned behavioral patterns. The generation unit can generate an AI CEO using, for example, a generation algorithm. The generation unit can also generate an AI CEO using a generation model. The generation unit can generate an AI CEO that replicates the CEO's behavioral patterns using, for example, a generation algorithm. The generation unit can also generate an AI CEO that mimics the CEO's behavioral patterns using a generation model. Furthermore, the generation unit can generate an AI CEO that predicts the CEO's behavioral patterns using a generation algorithm. This allows for the provision of appropriate feedback to employees by generating an AI CEO based on learned behavioral patterns. Some or all of the above processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input learned behavioral patterns into an AI and have the AI ​​generate the AI ​​CEO.

[0037] The service provider can enable the AI ​​CEO to provide appropriate advice and guidance to employees. For example, the service provider can enable the AI ​​CEO to provide employees with advice on improving their work processes. The service provider can also enable the AI ​​CEO to provide employees with guidance on improving their skills. Furthermore, the service provider can enable the AI ​​CEO to provide employees with advice on career paths. This promotes employee growth by enabling the AI ​​CEO to provide employees with appropriate advice and guidance. Some or all of the above processes in the service provider can be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the process of the AI ​​CEO providing employees with advice on improving their work processes.

[0038] The service department can enable the AI ​​CEO to rigorously reprimand and motivate employees. For example, the service department can enable the AI ​​CEO to provide employees with tough feedback. The service department can also enable the AI ​​CEO to set high goals for employees and encourage their achievement. Furthermore, the service department can enable the AI ​​CEO to provide employees with tough guidance to promote their growth. This further promotes employee growth through the AI ​​CEO's rigorous reprimands and motivation. Some or all of the above processes in the service department may be performed using AI, for example, or not using AI. For example, the service department can have AI perform the process of the AI ​​CEO providing employees with tough feedback.

[0039] The service provider enables the AI ​​CEO to promote employee growth. For example, the service provider can enable the AI ​​CEO to set growth goals for employees and manage their progress. The service provider can also enable the AI ​​CEO to provide employees with feedback for their growth. Furthermore, the service provider can enable the AI ​​CEO to set evaluation criteria for employee growth and conduct evaluations. This allows the AI ​​CEO to promote employee growth and improve the overall competitiveness of the organization. Some or all of the processes described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the process of the AI ​​CEO setting growth goals for employees and managing their progress.

[0040] The provisioning department can deploy AI-CEO's technology to other companies and society as a whole, supporting the growth of highly motivated business professionals. For example, the provisioning department can license AI-CEO's technology. The provisioning department can also deploy AI-CEO's technology in the form of collaborative research. Furthermore, the provisioning department can provide AI-CEO's technology as a public platform. In this way, by deploying AI-CEO's technology to other companies and society as a whole, it will support the growth of highly motivated business professionals and contribute to improving productivity in Japan. Some or all of the above processes in the provisioning department may be performed using AI, for example, or not using AI. For example, the provisioning department can have AI perform the process of licensing AI-CEO's technology.

[0041] The data collection unit can estimate the CEO's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the CEO is stressed, the data collection unit can delay collection and collect data when the CEO is relaxed. The data collection unit can also collect data after an important meeting if the CEO is in one. Furthermore, if the CEO is traveling, the data collection unit can adjust the timing to collect data at the travel destination. This allows for the collection of more accurate behavioral data by adjusting the collection timing based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of estimating the CEO's emotions and adjusting the collection timing.

[0042] The data collection unit can analyze the CEO's past behavioral data and select the optimal data collection method. For example, the unit can select a data collection method specifically tailored to a particular action based on the CEO's past actions. Furthermore, if the data collection unit can identify periods where the CEO's actions are concentrated, it can concentrate data collection during those periods. Additionally, if the data collection unit analyzes the CEO's past behavioral data and identifies locations where actions are frequent, it can strengthen data collection in those locations. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the CEO's past behavioral data into an AI and have the AI ​​select the optimal data collection method.

[0043] The data collection unit can filter behavioral data based on the CEO's current projects and areas of interest. For example, the unit can prioritize collecting behavioral data related to projects the CEO is currently working on. The unit can also filter and collect behavioral data related to the CEO's areas of interest. Furthermore, the unit can collect behavioral data related to areas the CEO has recently become interested in. This allows for the collection of highly relevant data by filtering the data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the process of filtering data based on the CEO's current projects and areas of interest.

[0044] The data collection unit can estimate the CEO's emotions and prioritize the data to collect based on the estimated emotions. For example, if the CEO is stressed, the data collection unit will prioritize collecting behavioral data related to stress reduction. If the CEO is relaxed, the data collection unit can also prioritize collecting everyday behavioral data. Furthermore, if the CEO is agitated, the data collection unit can prioritize collecting behavioral data related to important decision-making. This allows for the priority collection of important data by prioritizing data based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of estimating the CEO's emotions and prioritizing the data to collect.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the CEO's geographical location when collecting behavioral data. For example, if the CEO is on a business trip, the data collection unit will prioritize collecting behavioral data from that location. Furthermore, if the CEO frequently visits a particular location, the data collection unit can prioritize collecting behavioral data from that location. In addition, the data collection unit can consider geographical location information when collecting behavioral data from newly visited locations. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the CEO's geographical location information into an AI and have the AI ​​collect highly relevant data.

[0046] The data collection unit can analyze the CEO's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant behavioral data based on the content the CEO posts on social media. The data collection unit can also identify areas of interest from the CEO's social media activity and collect behavioral data related to those areas. Furthermore, the data collection unit can collect relevant behavioral data based on the accounts the CEO follows on social media. This allows for the collection of highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the CEO's social media activity into AI and have the AI ​​collect relevant data.

[0047] The analysis unit can estimate the CEO's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the CEO is relaxed, the analysis unit can provide detailed analysis results. If the CEO is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the CEO is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis based on the CEO's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI perform the process of estimating the CEO's emotions and adjusting the presentation of the analysis.

[0048] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis of behavioral data related to important decision-making. It can also perform a simplified analysis of everyday behavioral data. Furthermore, the analysis unit can perform a detailed analysis of behavioral data related to the CEO's areas of interest. This allows for efficient analysis by adjusting the level of detail based on the importance of the behavioral data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the behavioral data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0049] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a decision-making analysis algorithm to behavioral data related to decision-making. It can also apply a daily behavior analysis algorithm to everyday behavioral data. Furthermore, it can apply an area of ​​interest analysis algorithm to behavioral data related to the CEO's areas of interest. By applying different analysis algorithms depending on the category of behavioral data, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of behavioral data into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0050] The analysis unit can estimate the CEO's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the CEO is in a hurry, the analysis unit can provide a short, concise analysis. If the CEO is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the CEO is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the CEO's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI perform the process of estimating the CEO's emotions and adjusting the length of the analysis.

[0051] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected behavioral data. It can also prioritize the analysis of behavioral data collected during a specific period. Furthermore, the analysis unit may prioritize the analysis of behavioral data related to important events of the CEO. This allows for efficient analysis by determining the priority of analysis based on the timing of behavioral data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of behavioral data collection into the AI ​​and have the AI ​​perform the process of determining the priority of analysis.

[0052] The analysis unit can adjust the order of analysis based on the relevance of behavioral data during the analysis process. For example, the analysis unit may prioritize the analysis of behavioral data related to the CEO's current projects. It can also prioritize the analysis of behavioral data related to the CEO's areas of interest. Furthermore, the analysis unit can prioritize the analysis of highly relevant data based on the CEO's past behavioral patterns. This allows for efficient analysis by adjusting the order of analysis based on the relevance of behavioral data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of behavioral data into the AI ​​and have the AI ​​perform the process of adjusting the order of analysis.

[0053] The generation unit can estimate the CEO's emotions and adjust the AI-CEO generation method based on the estimated emotions. For example, if the CEO is relaxed, the generation unit will generate an AI-CEO that proceeds at a relaxed pace. If the CEO is in a hurry, the generation unit can also generate an AI-CEO that makes decisions quickly. Furthermore, if the CEO is excited, the generation unit can generate a visually stimulating AI-CEO. This allows for the generation of a more appropriate AI-CEO by adjusting the generation method based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can have an AI perform the process of estimating the CEO's emotions and adjusting the generation method.

[0054] The generation unit can adjust the level of detail in the generation based on the importance of the learned behavioral patterns. For example, the generation unit will generate behavioral patterns related to important decision-making in detail. It can also generate everyday behavioral patterns concisely. Furthermore, it can generate behavioral patterns related to the CEO's areas of interest in detail. This allows for the efficient generation of AI CEOs by adjusting the level of detail based on the importance of the learned behavioral patterns. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of learned behavioral patterns into the AI ​​and have the AI ​​perform the process of adjusting the level of detail in the generation.

[0055] The generation unit can apply different generation algorithms depending on the category of the behavioral pattern during generation. For example, the generation unit can apply a decision-making generation algorithm to behavioral patterns related to decision-making. It can also apply a daily behavior generation algorithm to everyday behavioral patterns. Furthermore, it can apply an area of ​​interest generation algorithm to behavioral patterns related to the CEO's areas of interest. By applying different generation algorithms depending on the category of the behavioral pattern, a more accurate AI CEO can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of behavioral patterns into the AI ​​and have the AI ​​execute the application of different generation algorithms.

[0056] The generation unit can estimate the CEO's emotions and determine the priority of the AI ​​CEO to generate based on the estimated emotions. For example, if the CEO is relaxed, the generation unit will prioritize generating everyday behavioral patterns. If the CEO is in a hurry, the generation unit can also prioritize generating behavioral patterns related to important decision-making. Furthermore, if the CEO is excited, the generation unit can also prioritize generating visually stimulating behavioral patterns. This allows for efficient generation of AI CEOs by determining the priority of the AI ​​CEO to generate based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can have an AI perform the process of estimating the CEO's emotions and determining the priority of the AI ​​CEO to generate.

[0057] The generation unit can determine the generation priority based on the timing of behavioral pattern collection during generation. For example, the generation unit may prioritize the generation of recently collected behavioral patterns. It can also prioritize the generation of behavioral patterns collected during a specific period. Furthermore, the generation unit may prioritize the generation of behavioral patterns related to important events of the CEO. This allows for efficient generation of AI CEOs by determining the generation priority based on the timing of behavioral pattern collection. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of behavioral pattern collection into the AI ​​and have the AI ​​perform the process of determining the generation priority.

[0058] The generation unit can adjust the generation order based on the relevance of behavioral patterns during generation. For example, the generation unit can prioritize generating behavioral patterns related to the CEO's current project. It can also prioritize generating behavioral patterns related to the CEO's areas of interest. Furthermore, the generation unit can prioritize generating highly relevant behavioral patterns based on the CEO's past behavioral patterns. This allows for efficient generation of AI CEOs by adjusting the generation order based on the relevance of behavioral patterns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of behavioral patterns into the AI ​​and have the AI ​​perform the process of adjusting the generation order.

[0059] The service provider can estimate an employee's emotions and adjust the way feedback is expressed based on those emotions. For example, if an employee is nervous, the service provider can provide feedback in a calm tone. If an employee is relaxed, the service provider can also provide detailed feedback. Furthermore, if an employee is in a hurry, the service provider can provide concise feedback that gets straight to the point. This allows for more appropriate feedback to be provided by adjusting the way feedback is expressed based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can have AI perform the process of estimating an employee's emotions and adjusting the way feedback is expressed.

[0060] The feedback department can provide optimal feedback by referring to the employee's past behavioral history when providing feedback. For example, the feedback department can provide feedback that points out specific areas for improvement based on the employee's past behavioral history. The feedback department can also provide motivational feedback by referring to the employee's past achievements. Furthermore, the feedback department can provide feedback on improving specific skills based on the employee's past behavioral history. This allows for the provision of more appropriate feedback by referring to the employee's past behavioral history. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input the employee's past behavioral history into AI and have the AI ​​perform the task of providing optimal feedback.

[0061] The feedback delivery system can customize feedback based on an employee's current projects and areas of interest. For example, the system can provide feedback related to a project the employee is currently working on. It can also provide feedback related to an employee's areas of interest. Furthermore, it can provide feedback on areas the employee has recently become interested in. This allows for more appropriate feedback to be provided by customizing it based on the employee's current projects and areas of interest. Some or all of the above processes in the feedback delivery system may be performed using AI, for example, or not. For example, the feedback delivery system can input the employee's current projects and areas of interest into an AI and have the AI ​​perform the feedback customization.

[0062] The service provider can estimate an employee's emotions and prioritize feedback based on those emotions. For example, if an employee is stressed, the service provider will prioritize providing feedback related to stress reduction. It can also prioritize routine feedback if an employee is relaxed. Furthermore, if an employee is excited, the service provider can prioritize feedback related to important projects. This allows for more appropriate feedback to be provided by prioritizing feedback based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of estimating an employee's emotions and prioritizing feedback.

[0063] The service provider can provide optimal feedback by considering the employee's geographical location when providing feedback. For example, if an employee is on a business trip, the service provider can provide feedback related to their activities at their destination. Furthermore, if an employee frequently visits a particular location, the service provider can provide feedback related to their activities at that location. In addition, the service provider can consider geographical location information to provide feedback related to an employee's activities in a newly visited location. This allows for the provision of more appropriate feedback by considering the employee's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the employee's geographical location information into AI and have the AI ​​provide optimal feedback.

[0064] The service provider can analyze an employee's social media activity and suggest methods for providing feedback when delivering it. For example, the service provider can provide relevant feedback based on what the employee has posted on social media. It can also identify areas of interest from the employee's social media activity and provide feedback related to those areas. Furthermore, the service provider can provide relevant feedback based on the accounts the employee follows on social media. This allows for the suggestion of more appropriate feedback methods by analyzing the employee's social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the employee's social media activity into an AI and have the AI ​​perform the process of suggesting methods for providing feedback.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The CEO Complementary Planning System can also be equipped with a "Behavioral Prediction Unit." This unit predicts future actions based on collected CEO behavioral data. For example, it analyzes past decision-making processes in specific situations and predicts actions to take if similar situations arise again. The unit can also suggest the next course of action based on the CEO's schedule and meeting content. Furthermore, it can learn the CEO's past behavioral patterns and provide valuable information for developing long-term strategies. This allows for more effective decision-making by predicting the CEO's actions.

[0067] The CEO Complementary Planning System can also include a "Behavioral Simulation Unit." This unit simulates various scenarios based on the CEO's behavioral patterns. For example, it can simulate the impact of a specific decision on the organization and provide the results to the CEO. The Behavioral Simulation Unit can also perform simulations to compare the effectiveness of different strategies. Furthermore, it can simulate the impact of the CEO's behavior on employee motivation and performance. By simulating various scenarios, it can support more effective decision-making.

[0068] The CEO Complementary Planning System can also include a "Behavioral Evaluation Department." This department evaluates the CEO's behavior and provides feedback based on the evaluation results. For example, it can evaluate the effectiveness of decisions made by the CEO and provide feedback on the results. The Behavioral Evaluation Department can also evaluate the impact of the CEO's behavior on employee motivation and performance. Furthermore, it can evaluate the impact of the CEO's behavior on the entire organization. This allows for more effective leadership support by evaluating the CEO's behavior.

[0069] The CEO Complementary Planning System can also be equipped with a "Behavioral Prediction Unit." This unit predicts future actions based on collected CEO behavioral data. For example, it analyzes past decision-making processes in specific situations and predicts actions to take if similar situations arise again. The unit can also suggest the next course of action based on the CEO's schedule and meeting content. Furthermore, it can learn the CEO's past behavioral patterns and provide valuable information for developing long-term strategies. This allows for more effective decision-making by predicting the CEO's actions.

[0070] The CEO Complementary Planning System can also include a "Behavioral Simulation Unit." This unit simulates various scenarios based on the CEO's behavioral patterns. For example, it can simulate the impact of a specific decision on the organization and provide the results to the CEO. The Behavioral Simulation Unit can also perform simulations to compare the effectiveness of different strategies. Furthermore, it can simulate the impact of the CEO's behavior on employee motivation and performance. By simulating various scenarios, it can support more effective decision-making.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit collects the CEO's behavioral data. For example, the unit collects the CEO's past statements and behavioral patterns. Specifically, it records the CEO's statements in meetings and converts them into text data. It can also analyze the content of the CEO's emails to extract behavioral patterns. Furthermore, it can analyze the CEO's schedule to understand the frequency and timing of their activities. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the CEO's behavioral patterns. The analysis unit extracts and learns the CEO's behavioral patterns using data mining techniques and machine learning algorithms. Specifically, it can optimize behavioral patterns using supervised learning, unsupervised learning, and reinforcement learning. Step 3: The generation unit generates an AI CEO based on the behavioral patterns learned by the analysis unit. The generation unit uses generation algorithms and models to generate an AI CEO that reproduces or imitates the behavioral patterns of a real CEO. Furthermore, it can also use generation algorithms to generate an AI CEO that predicts the behavioral patterns of a real CEO. Step 4: The delivery department provides feedback to employees through the AI ​​CEO generated by the generation department. The delivery department then uses the AI ​​CEO to provide appropriate advice and guidance to employees. Specifically, this can include advice on improving work processes, guidance on skill development, and career path advice.

[0073] (Example of form 2) The CEO Complementary Planning System according to an embodiment of the present invention is a system aimed at the growth and innovation of all employees of the SoftBank Group. This system aims to divide the CEO into a "True CEO" and an "AI CEO". The True CEO will be dedicated to vision and strategy creation, while the AI ​​CEO will specialize in talent development. By utilizing the CEO behavioral data held by the SoftBank Group, the most accurate AI CEO will be generated, and all employees will be able to make the AI ​​CEO their boss. This plan is expected to accelerate employee growth and strengthen the competitiveness of the entire organization. Furthermore, this technology will be deployed to society and popularized as a service that supports the growth of highly motivated business people, contributing to the improvement of productivity in Japan. For example, behavioral data of the SoftBank Group's CEO will be collected. This data will include the CEO's past statements, behavioral patterns, and decision-making processes. Next, the collected data will be analyzed by an AI to learn the CEO's behavioral patterns. This will generate an AI CEO. The generated AI CEO can be requested by all employees to have a 1-on-1 meeting anytime, anywhere. For example, when an employee approaches the AI-CEO with work-related challenges or career advice, the AI-CEO provides appropriate advice based on the CEO's past statements and actions. The AI-CEO can also provide firm encouragement and constructive criticism, thereby promoting employee growth. This system allows employees to grow at their own pace, improving the overall competitiveness of the organization. Furthermore, by deploying the AI-CEO technology to other companies and society as a whole, it is expected to support the growth of highly motivated business professionals and contribute to improving productivity in Japan. In this way, the CEO Complementary Planning System can accelerate employee growth and strengthen the overall competitiveness of the organization.

[0074] The CEO complementary planning system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the CEO's behavioral data. For example, the collection unit collects the CEO's past statements and behavioral patterns. The collection unit can collect the CEO's statements in meetings, email content, schedule, etc. For example, the collection unit can record the CEO's statements in meetings and convert them into text data. The collection unit can also analyze the content of the CEO's emails and extract behavioral patterns. Furthermore, the collection unit can analyze the CEO's schedule and grasp the frequency and timing of actions. The analysis unit analyzes the data collected by the collection unit and learns the CEO's behavioral patterns. For example, the analysis unit extracts the CEO's behavioral patterns using data mining techniques. The analysis unit can also learn the CEO's behavioral patterns using machine learning algorithms. For example, the analysis unit learns the CEO's behavioral patterns using supervised learning. The analysis unit can also cluster the CEO's behavioral patterns using unsupervised learning. Furthermore, the analysis unit can optimize the CEO's behavior patterns using reinforcement learning. The generation unit generates an AI-CEO based on the behavior patterns learned by the analysis unit. The generation unit generates an AI-CEO, for example, using a generation algorithm. The generation unit can also generate an AI-CEO, for example, using a generation algorithm. The generation unit can generate an AI-CEO that reproduces the CEO's behavior patterns, for example, using a generation algorithm. The generation unit can also generate an AI-CEO that mimics the CEO's behavior patterns, for example, using a generation model. Furthermore, the generation unit can generate an AI-CEO that predicts the CEO's behavior patterns, for example, using a generation algorithm. The delivery unit provides feedback to employees from the AI-CEO generated by the generation unit. The delivery unit can, for example, have the AI-CEO provide appropriate advice and guidance to employees. The delivery unit can also have the AI-CEO sternly encourage and motivate employees. The delivery unit can, for example, have the AI-CEO advise employees on improving their work. The delivery unit can also have the AI-CEO provide guidance to employees on improving their skills. Furthermore, the service provider can also have the AI ​​CEO provide career path advice to employees.As a result, the CEO complementation planning system according to this embodiment can promote employee growth and improve the overall competitiveness of the organization.

[0075] The data collection unit collects the CEO's behavioral data. For example, it collects the CEO's past statements and behavioral patterns. Specifically, it records the CEO's statements at meetings and converts them into text data using speech recognition technology. This allows for a detailed record of what is said during meetings, which can then be analyzed later. It also analyzes the CEO's emails to understand what topics they are interested in and what instructions they give. The email content is analyzed using natural language processing technology to extract keywords and perform sentiment analysis. Furthermore, it analyzes the CEO's schedule to understand what activities they engage in at what times, as well as their frequency and patterns. Schedule data is automatically retrieved from calendar applications and scheduling systems and stored in a database. This allows the data collection unit to collect the CEO's behavioral data from multiple angles and understand detailed behavioral patterns. The collected data is managed in a secure environment with thorough privacy protection. For example, the data is encrypted, and access rights are strictly controlled. This allows the data collection unit to safely and efficiently collect the CEO's behavioral data and provide it to the analysis and generation units.

[0076] The analysis unit analyzes the data collected by the collection unit and trains the CEO's behavioral patterns. For example, the analysis unit extracts the CEO's behavioral patterns using data mining techniques. Specifically, it treats the CEO's statements and actions as time-series data and detects frequently occurring patterns and abnormal behaviors. Machine learning algorithms can also be used to train the CEO's behavioral patterns. For example, supervised learning is used to build a model that predicts the CEO's behavioral patterns from past data. This model captures the characteristics of the CEO's statements and actions and is used to predict future actions. Unsupervised learning can also be used to cluster the CEO's behavioral patterns and group similar actions. This makes it easier to grasp the overall picture of the CEO's behavioral patterns. Furthermore, reinforcement learning can be used to optimize the CEO's behavioral patterns. In reinforcement learning, the impact of the CEO's actions on the entire organization is evaluated, and the optimal behavioral pattern is found. This allows the analysis unit to analyze the collected data from multiple angles and train the CEO's behavioral patterns in detail. The analysis results are provided to the generation unit and used to generate the AI ​​CEO.

[0077] The generation unit generates AI-CEOs based on behavioral patterns learned by the analysis unit. The generation unit generates AI-CEOs using, for example, a generation algorithm. Specifically, it uses a generation model to generate AI-CEOs that replicate the CEO's behavioral patterns. Examples of such generation models include generative opposite networks (GANs) and variational autoencoders (VAEs). These models generate AI-CEOs that mimic the CEO's statements and actions based on the behavioral patterns provided by the analysis unit. The generation unit can also use a generation algorithm to generate AI-CEOs that predict the CEO's behavioral patterns. For example, it can predict what statements and actions the CEO will take in a specific situation, and the AI-CEO will respond appropriately based on the results. Furthermore, the generation unit can use a generation model to generate AI-CEOs that optimize the CEO's behavioral patterns. This allows the generation unit to generate advanced AI-CEOs based on data provided by the analysis unit, improving the overall efficiency and effectiveness of the organization. The generated AI-CEOs provide feedback to employees through the service provider unit, supporting the organization's growth.

[0078] The providing unit provides feedback to employees through the AI-CEO generated by the generation unit. Specifically, the AI-CEO provides appropriate advice and guidance to employees. For example, the AI-CEO evaluates employees' work content and performance and provides advice for work improvement. The AI-CEO can generate appropriate answers to employees' questions and consultations using natural language processing technology. The AI-CEO can also provide guidance to improve employees' skills. For example, it can propose training programs to improve specific skills and monitor their progress. Furthermore, the AI-CEO can provide advice on employees' career paths. For example, it can propose the optimal career path based on employees' goals and aspirations and show concrete steps toward achieving it. The AI-CEO can also sternly encourage and motivate employees. For example, it can present specific improvement measures to employees whose performance is sluggish and provide feedback to boost their motivation. In this way, the providing unit can provide employees with multifaceted feedback through the AI-CEO and promote employee growth. As a result, the CEO complementary planning system according to this embodiment can promote employee growth and improve the overall competitiveness of the organization.

[0079] The data collection unit can collect the CEO's past statements and behavioral patterns. For example, the data collection unit can record the CEO's statements in meetings and convert them into text data. It can also analyze the content of the CEO's emails and extract behavioral patterns. Furthermore, the data collection unit can analyze the CEO's schedule to understand the frequency and timing of their activities. By collecting the CEO's past statements and behavioral patterns, more accurate behavioral data can be obtained. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the process of recording the CEO's statements in meetings and converting them into text data.

[0080] The analysis unit can analyze the collected data and train the CEO's behavioral patterns. For example, the analysis unit can extract the CEO's behavioral patterns using data mining techniques. The analysis unit can also train the CEO's behavioral patterns using machine learning algorithms. For example, the analysis unit can train the CEO's behavioral patterns using supervised learning. Furthermore, the analysis unit can cluster the CEO's behavioral patterns using unsupervised learning. In addition, the analysis unit can optimize the CEO's behavioral patterns using reinforcement learning. This improves the accuracy of AI-CEO by analyzing the collected data and training the CEO's behavioral patterns. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the collected data into the AI ​​and have the AI ​​perform the training of the CEO's behavioral patterns.

[0081] The generation unit can generate an AI CEO based on learned behavioral patterns. The generation unit can generate an AI CEO using, for example, a generation algorithm. The generation unit can also generate an AI CEO using a generation model. The generation unit can generate an AI CEO that replicates the CEO's behavioral patterns using, for example, a generation algorithm. The generation unit can also generate an AI CEO that mimics the CEO's behavioral patterns using a generation model. Furthermore, the generation unit can generate an AI CEO that predicts the CEO's behavioral patterns using a generation algorithm. This allows for the provision of appropriate feedback to employees by generating an AI CEO based on learned behavioral patterns. Some or all of the above processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input learned behavioral patterns into an AI and have the AI ​​generate the AI ​​CEO.

[0082] The service provider can enable the AI ​​CEO to provide appropriate advice and guidance to employees. For example, the service provider can enable the AI ​​CEO to provide employees with advice on improving their work processes. The service provider can also enable the AI ​​CEO to provide employees with guidance on improving their skills. Furthermore, the service provider can enable the AI ​​CEO to provide employees with advice on career paths. This promotes employee growth by enabling the AI ​​CEO to provide employees with appropriate advice and guidance. Some or all of the above processes in the service provider can be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the process of the AI ​​CEO providing employees with advice on improving their work processes.

[0083] The service department can enable the AI ​​CEO to rigorously reprimand and motivate employees. For example, the service department can enable the AI ​​CEO to provide employees with tough feedback. The service department can also enable the AI ​​CEO to set high goals for employees and encourage their achievement. Furthermore, the service department can enable the AI ​​CEO to provide employees with tough guidance to promote their growth. This further promotes employee growth through the AI ​​CEO's rigorous reprimands and motivation. Some or all of the above processes in the service department may be performed using AI, for example, or not using AI. For example, the service department can have AI perform the process of the AI ​​CEO providing employees with tough feedback.

[0084] The service provider enables the AI ​​CEO to promote employee growth. For example, the service provider can enable the AI ​​CEO to set growth goals for employees and manage their progress. The service provider can also enable the AI ​​CEO to provide employees with feedback for their growth. Furthermore, the service provider can enable the AI ​​CEO to set evaluation criteria for employee growth and conduct evaluations. This allows the AI ​​CEO to promote employee growth and improve the overall competitiveness of the organization. Some or all of the processes described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI perform the process of the AI ​​CEO setting growth goals for employees and managing their progress.

[0085] The provisioning department can deploy AI-CEO's technology to other companies and society as a whole, supporting the growth of highly motivated business professionals. For example, the provisioning department can license AI-CEO's technology. The provisioning department can also deploy AI-CEO's technology in the form of collaborative research. Furthermore, the provisioning department can provide AI-CEO's technology as a public platform. In this way, by deploying AI-CEO's technology to other companies and society as a whole, it will support the growth of highly motivated business professionals and contribute to improving productivity in Japan. Some or all of the above processes in the provisioning department may be performed using AI, for example, or not using AI. For example, the provisioning department can have AI perform the process of licensing AI-CEO's technology.

[0086] The data collection unit can estimate the CEO's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the CEO is stressed, the data collection unit can delay collection and collect data when the CEO is relaxed. The data collection unit can also collect data after an important meeting if the CEO is in one. Furthermore, if the CEO is traveling, the data collection unit can adjust the timing to collect data at the travel destination. This allows for the collection of more accurate behavioral data by adjusting the collection timing based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of estimating the CEO's emotions and adjusting the collection timing.

[0087] The data collection unit can analyze the CEO's past behavioral data and select the optimal data collection method. For example, the unit can select a data collection method specifically tailored to a particular action based on the CEO's past actions. Furthermore, if the data collection unit can identify periods where the CEO's actions are concentrated, it can concentrate data collection during those periods. Additionally, if the data collection unit analyzes the CEO's past behavioral data and identifies locations where actions are frequent, it can strengthen data collection in those locations. This allows for the selection of the optimal data collection method and efficient data collection by analyzing past behavioral data. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the CEO's past behavioral data into an AI and have the AI ​​select the optimal data collection method.

[0088] The data collection unit can filter behavioral data based on the CEO's current projects and areas of interest. For example, the unit can prioritize collecting behavioral data related to projects the CEO is currently working on. The unit can also filter and collect behavioral data related to the CEO's areas of interest. Furthermore, the unit can collect behavioral data related to areas the CEO has recently become interested in. This allows for the collection of highly relevant data by filtering the data based on current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can have AI perform the process of filtering data based on the CEO's current projects and areas of interest.

[0089] The data collection unit can estimate the CEO's emotions and prioritize the data to collect based on the estimated emotions. For example, if the CEO is stressed, the data collection unit will prioritize collecting behavioral data related to stress reduction. If the CEO is relaxed, the data collection unit can also prioritize collecting everyday behavioral data. Furthermore, if the CEO is agitated, the data collection unit can prioritize collecting behavioral data related to important decision-making. This allows for the priority collection of important data by prioritizing data based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of estimating the CEO's emotions and prioritizing the data to collect.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the CEO's geographical location when collecting behavioral data. For example, if the CEO is on a business trip, the data collection unit will prioritize collecting behavioral data from that location. Furthermore, if the CEO frequently visits a particular location, the data collection unit can prioritize collecting behavioral data from that location. In addition, the data collection unit can consider geographical location information when collecting behavioral data from newly visited locations. This allows for the efficient collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the CEO's geographical location information into an AI and have the AI ​​collect highly relevant data.

[0091] The data collection unit can analyze the CEO's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant behavioral data based on the content the CEO posts on social media. The data collection unit can also identify areas of interest from the CEO's social media activity and collect behavioral data related to those areas. Furthermore, the data collection unit can collect relevant behavioral data based on the accounts the CEO follows on social media. This allows for the collection of highly relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the CEO's social media activity into AI and have the AI ​​collect relevant data.

[0092] The analysis unit can estimate the CEO's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the CEO is relaxed, the analysis unit can provide detailed analysis results. If the CEO is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the CEO is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis based on the CEO's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI perform the process of estimating the CEO's emotions and adjusting the presentation of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis of behavioral data related to important decision-making. It can also perform a simplified analysis of everyday behavioral data. Furthermore, the analysis unit can perform a detailed analysis of behavioral data related to the CEO's areas of interest. This allows for efficient analysis by adjusting the level of detail based on the importance of the behavioral data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the behavioral data into the AI ​​and have the AI ​​perform the process of adjusting the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a decision-making analysis algorithm to behavioral data related to decision-making. It can also apply a daily behavior analysis algorithm to everyday behavioral data. Furthermore, it can apply an area of ​​interest analysis algorithm to behavioral data related to the CEO's areas of interest. By applying different analysis algorithms depending on the category of behavioral data, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of behavioral data into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0095] The analysis unit can estimate the CEO's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the CEO is in a hurry, the analysis unit can provide a short, concise analysis. If the CEO is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the CEO is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the CEO's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can have AI perform the process of estimating the CEO's emotions and adjusting the length of the analysis.

[0096] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected behavioral data. It can also prioritize the analysis of behavioral data collected during a specific period. Furthermore, the analysis unit may prioritize the analysis of behavioral data related to important events of the CEO. This allows for efficient analysis by determining the priority of analysis based on the timing of behavioral data collection. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of behavioral data collection into the AI ​​and have the AI ​​perform the process of determining the priority of analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of behavioral data during the analysis process. For example, the analysis unit may prioritize the analysis of behavioral data related to the CEO's current projects. It can also prioritize the analysis of behavioral data related to the CEO's areas of interest. Furthermore, the analysis unit can prioritize the analysis of highly relevant data based on the CEO's past behavioral patterns. This allows for efficient analysis by adjusting the order of analysis based on the relevance of behavioral data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of behavioral data into the AI ​​and have the AI ​​perform the process of adjusting the order of analysis.

[0098] The generation unit can estimate the CEO's emotions and adjust the AI-CEO generation method based on the estimated emotions. For example, if the CEO is relaxed, the generation unit will generate an AI-CEO that proceeds at a relaxed pace. If the CEO is in a hurry, the generation unit can also generate an AI-CEO that makes decisions quickly. Furthermore, if the CEO is excited, the generation unit can generate a visually stimulating AI-CEO. This allows for the generation of a more appropriate AI-CEO by adjusting the generation method based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not using AI. For example, the generation unit can have an AI perform the process of estimating the CEO's emotions and adjusting the generation method.

[0099] The generation unit can adjust the level of detail in the generation based on the importance of the learned behavioral patterns. For example, the generation unit will generate behavioral patterns related to important decision-making in detail. It can also generate everyday behavioral patterns concisely. Furthermore, it can generate behavioral patterns related to the CEO's areas of interest in detail. This allows for the efficient generation of AI CEOs by adjusting the level of detail based on the importance of the learned behavioral patterns. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of learned behavioral patterns into the AI ​​and have the AI ​​perform the process of adjusting the level of detail in the generation.

[0100] The generation unit can apply different generation algorithms depending on the category of the behavioral pattern during generation. For example, the generation unit can apply a decision-making generation algorithm to behavioral patterns related to decision-making. It can also apply a daily behavior generation algorithm to everyday behavioral patterns. Furthermore, it can apply an area of ​​interest generation algorithm to behavioral patterns related to the CEO's areas of interest. By applying different generation algorithms depending on the category of the behavioral pattern, a more accurate AI CEO can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the categories of behavioral patterns into the AI ​​and have the AI ​​execute the application of different generation algorithms.

[0101] The generation unit can estimate the CEO's emotions and determine the priority of the AI ​​CEO to generate based on the estimated emotions. For example, if the CEO is relaxed, the generation unit will prioritize generating everyday behavioral patterns. If the CEO is in a hurry, the generation unit can also prioritize generating behavioral patterns related to important decision-making. Furthermore, if the CEO is excited, the generation unit can also prioritize generating visually stimulating behavioral patterns. This allows for efficient generation of AI CEOs by determining the priority of the AI ​​CEO to generate based on the CEO's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can have an AI perform the process of estimating the CEO's emotions and determining the priority of the AI ​​CEO to generate.

[0102] The generation unit can determine the generation priority based on the timing of behavioral pattern collection during generation. For example, the generation unit may prioritize the generation of recently collected behavioral patterns. It can also prioritize the generation of behavioral patterns collected during a specific period. Furthermore, the generation unit may prioritize the generation of behavioral patterns related to important events of the CEO. This allows for efficient generation of AI CEOs by determining the generation priority based on the timing of behavioral pattern collection. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of behavioral pattern collection into the AI ​​and have the AI ​​perform the process of determining the generation priority.

[0103] The generation unit can adjust the generation order based on the relevance of behavioral patterns during generation. For example, the generation unit can prioritize generating behavioral patterns related to the CEO's current project. It can also prioritize generating behavioral patterns related to the CEO's areas of interest. Furthermore, the generation unit can prioritize generating highly relevant behavioral patterns based on the CEO's past behavioral patterns. This allows for efficient generation of AI CEOs by adjusting the generation order based on the relevance of behavioral patterns. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of behavioral patterns into the AI ​​and have the AI ​​perform the process of adjusting the generation order.

[0104] The service provider can estimate an employee's emotions and adjust the way feedback is expressed based on those emotions. For example, if an employee is nervous, the service provider can provide feedback in a calm tone. If an employee is relaxed, the service provider can also provide detailed feedback. Furthermore, if an employee is in a hurry, the service provider can provide concise feedback that gets straight to the point. This allows for more appropriate feedback to be provided by adjusting the way feedback is expressed based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can have AI perform the process of estimating an employee's emotions and adjusting the way feedback is expressed.

[0105] The feedback department can provide optimal feedback by referring to the employee's past behavioral history when providing feedback. For example, the feedback department can provide feedback that points out specific areas for improvement based on the employee's past behavioral history. The feedback department can also provide motivational feedback by referring to the employee's past achievements. Furthermore, the feedback department can provide feedback on improving specific skills based on the employee's past behavioral history. This allows for the provision of more appropriate feedback by referring to the employee's past behavioral history. Some or all of the above processes in the feedback department may be performed using AI, for example, or not. For example, the feedback department can input the employee's past behavioral history into AI and have the AI ​​perform the task of providing optimal feedback.

[0106] The feedback delivery system can customize feedback based on an employee's current projects and areas of interest. For example, the system can provide feedback related to a project the employee is currently working on. It can also provide feedback related to an employee's areas of interest. Furthermore, it can provide feedback on areas the employee has recently become interested in. This allows for more appropriate feedback to be provided by customizing it based on the employee's current projects and areas of interest. Some or all of the above processes in the feedback delivery system may be performed using AI, for example, or not. For example, the feedback delivery system can input the employee's current projects and areas of interest into an AI and have the AI ​​perform the feedback customization.

[0107] The service provider can estimate an employee's emotions and prioritize feedback based on those emotions. For example, if an employee is stressed, the service provider will prioritize providing feedback related to stress reduction. It can also prioritize routine feedback if an employee is relaxed. Furthermore, if an employee is excited, the service provider can prioritize feedback related to important projects. This allows for more appropriate feedback to be provided by prioritizing feedback based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of estimating an employee's emotions and prioritizing feedback.

[0108] The service provider can provide optimal feedback by considering the employee's geographical location when providing feedback. For example, if an employee is on a business trip, the service provider can provide feedback related to their activities at their destination. Furthermore, if an employee frequently visits a particular location, the service provider can provide feedback related to their activities at that location. In addition, the service provider can consider geographical location information to provide feedback related to an employee's activities in a newly visited location. This allows for the provision of more appropriate feedback by considering the employee's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the employee's geographical location information into AI and have the AI ​​provide optimal feedback.

[0109] The service provider can analyze an employee's social media activity and suggest methods for providing feedback when delivering it. For example, the service provider can provide relevant feedback based on what the employee has posted on social media. It can also identify areas of interest from the employee's social media activity and provide feedback related to those areas. Furthermore, the service provider can provide relevant feedback based on the accounts the employee follows on social media. This allows for the suggestion of more appropriate feedback methods by analyzing the employee's social media activity. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the employee's social media activity into an AI and have the AI ​​perform the process of suggesting methods for providing feedback.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The CEO Complementary Planning System can also include an "Emotional Feedback Department." This department monitors employee emotions in real time and provides appropriate feedback. For example, if an employee is stressed, the department offers advice on how to relax. If an employee is unmotivated, the department can send encouraging messages. Furthermore, if an employee is performing well, the department can send messages praising their efforts. This allows for improved employee motivation and performance by providing appropriate feedback based on employee emotions.

[0112] The CEO Complementary Planning System can also be equipped with a "Behavioral Prediction Unit." This unit predicts future actions based on collected CEO behavioral data. For example, it analyzes past decision-making processes in specific situations and predicts actions to take if similar situations arise again. The unit can also suggest the next course of action based on the CEO's schedule and meeting content. Furthermore, it can learn the CEO's past behavioral patterns and provide valuable information for developing long-term strategies. This allows for more effective decision-making by predicting the CEO's actions.

[0113] The CEO Complementary Planning System can also be equipped with an "Emotional Analysis Unit." This unit analyzes the CEO's emotions and adjusts their behavioral patterns based on those emotions. For example, if the CEO is stressed, the unit suggests actions to reduce stress. If the CEO is relaxed, the unit can also suggest actions to generate creative ideas. Furthermore, if the CEO is excited, the unit can suggest actions to harness that energy. This allows for more effective leadership by adjusting behavioral patterns based on the CEO's emotions.

[0114] The CEO Complementary Planning System can also include a "Behavioral Simulation Unit." This unit simulates various scenarios based on the CEO's behavioral patterns. For example, it can simulate the impact of a specific decision on the organization and provide the results to the CEO. The Behavioral Simulation Unit can also perform simulations to compare the effectiveness of different strategies. Furthermore, it can simulate the impact of the CEO's behavior on employee motivation and performance. By simulating various scenarios, it can support more effective decision-making.

[0115] The CEO Complementary Planning System can also be equipped with an "emotion tracking unit." This unit tracks the CEO's emotional changes in real time and adjusts behavioral patterns based on that data. For example, if the CEO is feeling stressed, the emotion tracking unit suggests actions to reduce stress. If the CEO is relaxed, the emotion tracking unit can also suggest actions to generate creative ideas. Furthermore, if the CEO is excited, the emotion tracking unit can suggest actions to harness that energy. By adjusting behavioral patterns based on the CEO's emotional changes, more effective leadership can be achieved.

[0116] The CEO Complementary Planning System can also include a "Behavioral Evaluation Department." This department evaluates the CEO's behavior and provides feedback based on the evaluation results. For example, it can evaluate the effectiveness of decisions made by the CEO and provide feedback on the results. The Behavioral Evaluation Department can also evaluate the impact of the CEO's behavior on employee motivation and performance. Furthermore, it can evaluate the impact of the CEO's behavior on the entire organization. This allows for more effective leadership support by evaluating the CEO's behavior.

[0117] The CEO Complementary Planning System can also include an "Emotional Feedback Department." This department monitors employee emotions in real time and provides appropriate feedback. For example, if an employee is stressed, the department offers advice on how to relax. If an employee is unmotivated, the department can send encouraging messages. Furthermore, if an employee is performing well, the department can send messages praising their efforts. This allows for improved employee motivation and performance by providing appropriate feedback based on employee emotions.

[0118] The CEO Complementary Planning System can also be equipped with a "Behavioral Prediction Unit." This unit predicts future actions based on collected CEO behavioral data. For example, it analyzes past decision-making processes in specific situations and predicts actions to take if similar situations arise again. The unit can also suggest the next course of action based on the CEO's schedule and meeting content. Furthermore, it can learn the CEO's past behavioral patterns and provide valuable information for developing long-term strategies. This allows for more effective decision-making by predicting the CEO's actions.

[0119] The CEO Complementary Planning System can also be equipped with an "Emotional Analysis Unit." This unit analyzes the CEO's emotions and adjusts their behavioral patterns based on those emotions. For example, if the CEO is stressed, the unit suggests actions to reduce stress. If the CEO is relaxed, the unit can also suggest actions to generate creative ideas. Furthermore, if the CEO is excited, the unit can suggest actions to harness that energy. This allows for more effective leadership by adjusting behavioral patterns based on the CEO's emotions.

[0120] The CEO Complementary Planning System can also include a "Behavioral Simulation Unit." This unit simulates various scenarios based on the CEO's behavioral patterns. For example, it can simulate the impact of a specific decision on the organization and provide the results to the CEO. The Behavioral Simulation Unit can also perform simulations to compare the effectiveness of different strategies. Furthermore, it can simulate the impact of the CEO's behavior on employee motivation and performance. By simulating various scenarios, it can support more effective decision-making.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the CEO's behavioral data. For example, the unit collects the CEO's past statements and behavioral patterns. Specifically, it records the CEO's statements in meetings and converts them into text data. It can also analyze the content of the CEO's emails to extract behavioral patterns. Furthermore, it can analyze the CEO's schedule to understand the frequency and timing of their activities. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the CEO's behavioral patterns. The analysis unit extracts and learns the CEO's behavioral patterns using data mining techniques and machine learning algorithms. Specifically, it can optimize behavioral patterns using supervised learning, unsupervised learning, and reinforcement learning. Step 3: The generation unit generates an AI CEO based on the behavioral patterns learned by the analysis unit. The generation unit uses generation algorithms and models to generate an AI CEO that reproduces or imitates the behavioral patterns of a real CEO. Furthermore, it can also use generation algorithms to generate an AI CEO that predicts the behavioral patterns of a real CEO. Step 4: The delivery department provides feedback to employees through the AI ​​CEO generated by the generation department. The delivery department then uses the AI ​​CEO to provide appropriate advice and guidance to employees. Specifically, this can include advice on improving work processes, guidance on skill development, and career path advice.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects CEO behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the CEO's behavior patterns. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, which generates an AI CEO based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14, which provides feedback to employees via the generated AI CEO. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects CEO behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the CEO's behavior patterns. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an AI CEO based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides feedback to employees via the generated AI CEO. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects CEO behavior data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the CEO's behavior patterns. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an AI CEO based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and the generated AI CEO provides feedback to employees. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects CEO behavior data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and learns the CEO's behavior patterns. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an AI CEO based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides feedback to employees via the generated AI CEO. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects CEO behavioral data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that learns the CEO's behavioral patterns, A generation unit that generates an AI CEO based on the behavioral patterns learned by the analysis unit, The system comprises a provisioning unit that provides feedback to employees to the AI ​​CEO generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the CEO's past statements and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to learn the CEO's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate an AI CEO based on learned behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, AI CEO provides appropriate advice and guidance to employees. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, AI CEO gives employees a stern but encouraging pep talk. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, AI CEOs promote employee growth The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, We will deploy AI CEO's technology to other companies and society as a whole, and support the growth of highly motivated business professionals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the CEO's emotions and adjust the timing of behavioral data collection based on the estimated CEO emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the CEO's past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the CEO's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the CEO's emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting behavioral data, the CEO's geographical location information is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting behavioral data, analyze the CEO's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the CEO's emotions and adjust the way the analysis is presented based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the CEO's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the CEO's emotions and adjusts the AI ​​CEO generation method based on the estimated CEO's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, adjust the level of detail based on the importance of learned behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, different generation algorithms are applied depending on the category of the behavioral pattern. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the CEO's emotions and determines the AI ​​CEO's priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the generation priority is determined based on when the behavioral patterns were collected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the generation order is adjusted based on the relevance of behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, We estimate employees' emotions and adjust the way feedback is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing feedback, we refer to the employee's past behavioral history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing feedback, customize the feedback based on the employee's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, The system estimates employees' emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing feedback, we take into account the employee's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing feedback, we analyze employees' social media activity and suggest methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects CEO behavioral data, The data collected by the aforementioned collection unit is analyzed by an analysis unit that learns the CEO's behavioral patterns, A generation unit that generates an AI CEO based on the behavioral patterns learned by the analysis unit, The system comprises a provisioning unit that provides feedback to employees from the AI ​​CEO generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect the CEO's past statements and behavioral patterns. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to learn the CEO's behavioral patterns. The system according to feature 1.

4. The generating unit is Generate an AI CEO based on learned behavioral patterns. The system according to feature 1.

5. The aforementioned supply unit is, AI CEO provides appropriate advice and guidance to employees. The system according to feature 1.

6. The aforementioned supply unit is, AI CEO gives employees a stern but encouraging pep talk. The system according to feature 1.

7. The aforementioned supply unit is, AI CEO promotes employee growth The system according to feature 1.

8. The aforementioned supply unit is, We will deploy AI CEO technology to other companies and society as a whole, and support the growth of highly motivated business professionals. The system according to feature 1.

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