system

Abstract

The system according to the embodiment aims to visualize the lack of management skills of superiors based on data and provide feedback to management and managers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit generates improvement proposals based on the analysis results obtained by the analysis unit. The provision unit provides the improvement proposals generated by the proposal unit to management and managers.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the lack of management skills of superiors was not visualized based on data, making it difficult for management to see the issues.

[0005] The system according to the embodiment aims to visualize the lack of management skills of superiors based on data and provide feedback to management and managers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit generates improvement proposals based on the analysis results obtained by the analysis unit. The provision unit provides the improvement proposals generated by the proposal unit to management and managers. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the lack of management skills of superiors based on data and provide feedback to management and managers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A management issue visualization system according to an embodiment of the present invention visualizes a company's management issues based on data and provides feedback to management and individual supervisors to promote fundamental improvements. This system collects and analyzes various data, analyzing managers' behavior and employee stress levels to identify management issues and automatically generates individual improvement proposals using a generation AI. Next, the system presents an analysis report and improvement proposals, creates an easy-to-understand report for management, and also outputs specific behavioral improvement proposals for individual managers. For example, the system analyzes managers' behavior from emails and chat logs and analyzes their superiors' attitudes using a behavioral recognition AI. It also estimates employees' stress levels from biometric data and behavior. This allows the system to identify management issues and automatically generate individual improvement proposals using a generation AI. Next, the system presents an analysis report and improvement proposals. It also creates an easy-to-understand report for management and outputs specific behavioral improvement proposals for individual managers. This system utilizes data and AI to visualize data and presents improvement guidelines to both management and managers, promoting fundamental organizational reform. The revenue model is based on pay-as-you-go or subscription, and aims to develop a business that prioritizes planning according to the scale of the company and maximizing effectiveness. As a result, the management issue visualization system visualizes a company's management issues based on data and provides feedback to management and individual superiors, encouraging essential improvements.

[0029] A management issue visualization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects various data within a company. For example, the collection unit can collect emails and chat logs. The collection unit can also collect biometric data and behavioral data. For example, the collection unit collects biometric data such as employees' heart rates and electrodermal activity. The collection unit can also collect behavioral data of employees. For example, the collection unit collects behavioral data such as employees' movement history and working hours. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the words and actions of managers from emails and chat logs. The analysis unit can also analyze the attitude of superiors using behavior recognition AI. For example, the analysis unit analyzes the superior's facial expressions and posture to evaluate the attitude. The analysis unit can also estimate the stress state of employees from the biometric data and behavioral data. For example, the analysis unit analyzes fluctuations in heart rate and electrodermal activity to estimate the stress state. The suggestion unit generates improvement proposals based on the analysis results obtained by the analysis unit. For example, the suggestion unit automatically generates individual improvement proposals using a generation AI. The generation AI can use a text generation AI such as GPT-4 (registered trademark) or Gemini. The suggestion unit uses the generation AI to generate behavioral improvement proposals for managers and business process improvement proposals. The provision unit provides the improvement proposals generated by the suggestion unit to management and managers. For example, the provision unit creates easy-to-understand reports for management. The provision unit can also output specific behavioral improvement proposals for individual managers. For example, the provision unit provides managers with proposals to increase the frequency of meetings and proposals to improve feedback methods. As a result, the management issue visualization system according to the embodiment visualizes a company's management issues based on data and provides feedback to management and individual superiors, thereby promoting essential improvements.

[0030] The collection unit can collect emails, chat logs, or biometric data. The collection unit, for example, collects emails. Emails include text emails and HTML emails. The collection unit acquires email data from a mail server and stores it for analysis. The collection unit can also collect chat logs. Chat logs include text format and JSON format. The collection unit acquires chat logs from a chat application and stores them for analysis. The collection unit can also collect biometric data. The biometric data includes heart rate and electrodermal activity. The collection unit acquires biometric data from wearable devices and sensors and stores it for analysis. This enables more comprehensive analysis by collecting data from various data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input email data into AI, which then analyzes the content of the email.

[0031] The analysis unit can analyze the data collected by the collection unit and identify management issues. The analysis unit, for example, analyzes emails and chat logs collected by the collection unit. For example, the analysis unit can use natural language processing technology to analyze the content of emails and chat logs and evaluate the manager's words and actions. The analysis unit can also analyze the supervisor's attitude using behavioral recognition AI. For example, the analysis unit can analyze the supervisor's facial expressions and posture to evaluate their attitude. Furthermore, the analysis unit can estimate the employee's stress level from biometric data and behavioral data. For example, the analysis unit can analyze heart rate fluctuations and changes in electrodermal activity to estimate the stress level. By analyzing the data and identifying management issues, specific areas for improvement can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into AI, which then analyzes the data and identifies management issues.

[0032] The proposal unit can automatically generate individual improvement proposals using a generation AI. The proposal unit automatically generates individual improvement proposals using, for example, a generation AI. Examples of generation AI include text generation AIs such as GPT-4 and Gemini. The proposal unit inputs data related to management issues to the generation AI, which then generates improvement proposals based on the data. For example, the proposal unit inputs a prompt to the generation AI, such as, "Please suggest ways to improve the manager's lack of communication," and the generation AI generates specific improvement proposals. The proposal unit can also use the generation AI to generate business process improvement proposals and cost reduction measures. For example, the proposal unit inputs a prompt to the generation AI, such as, "Please suggest ways to improve the efficiency of the business process," and the generation AI generates specific improvement proposals. In this way, the generation AI can automatically generate and efficiently provide individual improvement proposals. Some or all of the above-described processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit inputs data to the generation AI, which then generates improvement proposals.

[0033] The provision unit can create easy-to-understand reports for management and output specific behavioral improvement suggestions for individual managers. For example, the provision unit creates easy-to-understand reports for management. The reports include graph displays and summaries. The provision unit creates reports in a visually easy-to-understand format to make it easier for management to understand management issues. The provision unit can also output specific behavioral improvement suggestions for individual managers. For example, the provision unit provides managers with suggestions to increase the frequency of meetings and suggestions to improve feedback methods. This can promote improvement throughout the organization by providing specific improvement suggestions to management and individual managers. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit inputs data into a generation AI, which generates reports and improvement suggestions.

[0034] The analysis unit can analyze the superior's attitude using behavior recognition AI. The analysis unit can analyze the superior's attitude using, for example, behavior recognition AI. Behavior recognition AI includes posture recognition and facial expression recognition. The analysis unit inputs the superior's behavioral data to the behavior recognition AI, which then evaluates the superior's attitude based on the data. For example, the analysis unit inputs a prompt to the behavior recognition AI, such as, "Analyze the superior's facial expressions and evaluate their attitude," and the behavior recognition AI then makes a specific evaluation. The analysis unit can also use behavior recognition AI to evaluate the superior's communication style and leadership. For example, the analysis unit inputs a prompt to the behavior recognition AI, such as, "Analyze the superior's communication style and evaluate their leadership," and the behavior recognition AI then makes a specific evaluation. In this way, the behavior recognition AI can accurately analyze the superior's attitude and identify areas for improvement. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs data to the behavior recognition AI, which then evaluates the superior's attitude.

[0035] The analysis unit can estimate an employee's stress state from biometric data or behavior. The analysis unit estimates an employee's stress state from, for example, biometric data or behavioral data. Estimation of stress state includes heart rate variability and self-report questionnaires. The analysis unit analyzes the biometric data and behavioral data to evaluate the employee's stress state. For example, the analysis unit analyzes heart rate variability and changes in electrodermal activity to estimate the stress state. The analysis unit can also analyze the employee's behavioral data to evaluate the stress state. For example, the analysis unit analyzes data on the employee's working hours and break times to estimate the stress state. This allows for accurate estimation of the employee's stress state, allowing appropriate measures to be taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit inputs biometric data into AI, which then estimates the stress state.

[0036] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history. The collection unit selects the most efficient collection method based on the past data collection history. For example, the collection unit determines the collection frequency and the priority of data to be collected from the past data collection history. The collection unit can also optimize the collection method based on the past data collection history. For example, the collection unit analyzes past data collection history and selects a method that minimizes collection cost and collection time. In this way, by analyzing the past data collection history, the optimal collection method can be selected and efficient data collection becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs past data collection history into AI, which selects the optimal collection method.

[0037] The collection unit can filter data based on the user's current work situation and areas of interest when collecting data. For example, the collection unit filters data based on the user's current work situation and areas of interest when collecting data. Filtering includes keyword filtering, category filtering, and the like. The collection unit analyzes the user's work situation and areas of interest and prioritizes collection of highly relevant data. For example, the collection unit prioritizes collection of data related to a project the user is currently working on. The collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the collection unit filters data based on keywords related to the user's areas of interest. Furthermore, the collection unit can collect only necessary data depending on the user's work situation. For example, if the user is in a hurry, the collection unit prioritizes collection of only important data. This allows for efficient collection of highly relevant data by filtering data based on the user's work situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the user's work situation and areas of interest into AI, which then filters the data.

[0038] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration when collecting data. Geographical location information includes GPS data, IP address, etc. The collection unit analyzes the user's geographical location information and prioritizes collecting highly relevant data. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also prioritize collecting nearby data based on the user's location information. For example, the collection unit collects data related to nearby events and news based on the user's location information. Furthermore, when the user is traveling, the collection unit can collect data related to the user's destination. For example, when the user is on a business trip, the collection unit collects data related to the business trip destination. This allows for efficient collection of highly relevant data by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit inputs geographical location information into the AI, which then prioritizes collecting highly relevant data.

[0039] The collection unit can analyze the user's social media activity during data collection and collect highly relevant data. For example, the collection unit analyzes the user's social media activity during data collection. The social media activity includes the content of posts and the number of likes. The collection unit analyzes the user's social media activity and collects highly relevant data. For example, the collection unit collects relevant data based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activity. For example, the collection unit analyzes the user's social media activity and collects data based on keywords related to topics of interest. Furthermore, the collection unit can analyze the user's social media comments and collect necessary data. For example, the collection unit analyzes the user's social media comments and collects highly relevant data. This makes it possible to efficiently collect highly relevant data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the social media activity into AI, which then collects highly relevant data.

[0040] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the data. The analysis unit adjusts the level of analysis detail based on, for example, the importance of the data. The importance of data includes the impact on business operations and the reliability of the data. The analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. For example, the analysis unit applies a detailed statistical analysis or a machine learning algorithm to data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs simple aggregation or summarization on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and postpones analysis of data with low importance. This enables efficient analysis by adjusting the level of analysis detail based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit inputs the importance of the data into AI, which then adjusts the level of analysis detail.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies different analysis algorithms depending on the data category. Data categories include text data, numerical data, image data, etc. The analysis unit evaluates the data category and selects an appropriate analysis algorithm. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can also apply an image recognition algorithm to image data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the data category into AI, which selects an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit determines the analysis priority based on, for example, the time when the data was collected. The time when the data was collected includes the latest data and past data. The analysis unit evaluates the time when the data was collected and prioritizes analyzing the latest data. For example, the analysis unit prioritizes analyzing the latest data and refers to past data, while emphasizing the latest data. The analysis unit can also adjust the analysis priority based on the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, determining the analysis priority based on the time when the data was collected enables analysis that prioritizes the latest data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the time when the data was collected into AI, and the AI ​​determines the analysis priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. Data relevance includes correlation and causal relationship. The analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. For example, the analysis unit determines the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the relevance of the data into AI, and the AI ​​determines the order of analysis.

[0044] The suggestion unit can adjust the level of detail of the proposal based on the importance of the issue when making a proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the issue. The importance of the issue includes the impact on business operations and the urgency of resolution. The suggestion unit evaluates the importance of the issue and provides detailed proposals for issues of high importance. For example, the suggestion unit provides detailed improvement measures and specific action plans for issues of high importance. The suggestion unit can also provide simplified proposals for issues of low importance. For example, the suggestion unit provides simple advice or summaries for issues of low importance. Furthermore, the suggestion unit can determine the priority of the proposals based on the importance of the issue. For example, the suggestion unit prioritizes proposals for issues of high importance and postpones proposals for issues of low importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the issue. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI or without using a generation AI. For example, the proposal unit inputs the importance of the issue into the generation AI, which then adjusts the level of detail in the proposal.

[0045] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the issue. For example, the proposal unit applies different proposal algorithms depending on the category of the issue. Issue categories include technical issues, organizational issues, etc. The proposal unit evaluates the category of the issue and selects an appropriate proposal algorithm. For example, the proposal unit can provide a proposal to improve communication for an issue of lack of communication. Furthermore, the proposal unit can provide a proposal to review the allocation of work for an issue of excessive work load. Furthermore, the proposal unit can provide a proposal to encourage consideration for an issue of lack of consideration for subordinates. This enables highly accurate proposals by applying an appropriate proposal algorithm depending on the category of the issue. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit inputs the category of the issue into a generation AI, which selects an appropriate proposal algorithm.

[0046] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the issue occurred. The suggestion unit, for example, determines the priority of the proposal based on the time when the issue occurred. The time when the issue occurred includes the most recent issue and past issues. The suggestion unit evaluates the time when the issue occurred and prioritizes the most recent issue. For example, the suggestion unit prioritizes the most recent issue and refers to past issues while proposing the most recent issue. The suggestion unit can also adjust the priority of the proposal based on the time when the issue occurred. For example, the suggestion unit determines the priority of the proposal based on the time when the issue occurred. In this way, by determining the priority of the proposal based on the time when the issue occurred, it is possible to make a proposal that prioritizes the most recent issue. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit inputs the time when the issue occurred into the generation AI, and the generation AI determines the priority of the proposal.

[0047] The suggestion unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. The suggestion unit adjusts the order of proposals based on, for example, the relevance of the tasks. The relevance of the tasks includes correlation and causal relationships. The suggestion unit evaluates the relevance of the tasks and prioritizes proposing highly relevant tasks. For example, the suggestion unit prioritizes proposing highly relevant tasks and postpones less relevant tasks. The suggestion unit can also adjust the order of proposals according to the relevance of the tasks. For example, the suggestion unit determines the order of proposals based on the relevance of the tasks. This enables efficient proposals by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit inputs the relevance of the tasks into a generation AI, which determines the order of proposals.

[0048] The providing unit can select the optimal display method by referring to the user's past operation history when displaying a report. For example, the providing unit selects the optimal display method by referring to the user's past operation history when displaying a report. The operation history includes a click history, a browsing history, etc. The providing unit analyzes the user's past operation history and selects the optimal display method. For example, the providing unit preferentially provides a display method that the user has preferred in the past. The providing unit can also select a display method with high visibility from the user's past operation history. For example, the providing unit analyzes the user's operation history and provides the optimal display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the operation history into AI, and the AI ​​selects the optimal display method.

[0049] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying a report. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when displaying a report. The device information includes the device type, OS version, etc. The providing unit analyzes the user's device information and selects the optimal display method. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. In this way, the optimal display method can be provided by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the device information into AI, which selects the optimal display method.

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

[0051] The collection unit can analyze past data collection history and select the optimal collection method. For example, the collection unit determines the collection frequency and the priority of data to be collected from the past data collection history. The collection unit can also optimize the collection method based on the past data collection history. For example, the collection unit analyzes the past data collection history and selects a method that minimizes collection costs and collection time. In this way, by analyzing the past data collection history, the optimal collection method can be selected, enabling efficient data collection.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0053] The suggestion unit can adjust the level of detail of the proposal based on the importance of the issue. For example, the suggestion unit can provide a detailed proposal for an issue with a high level of importance and a simplified proposal for an issue with a low level of importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the issue.

[0054] When displaying a report, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can provide a display method that the user has used favorably in the past with priority. The providing unit can also select a display method with high visibility from the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0055] When displaying a report, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. In this way, the optimal display method can be provided by taking into account the user's device information.

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

[0057] Step 1: The collection department collects various data within the company, such as emails, chat logs, biometric data (heart rate, electrodermal activity, etc.), and behavioral data (movement history, work hours, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the manager's words and actions from emails and chat logs, and uses behavioral recognition AI to analyze the boss's facial expressions and posture to evaluate their attitude. It also estimates the employee's stress level from biometric and behavioral data. Step 3: The proposal unit generates improvement proposals based on the analysis results obtained by the analysis unit. For example, using a generative AI (such as GPT-4 or Gemini), it automatically generates proposals for improving manager behavior and business processes. Step 4: The Providing Department provides the improvement proposals generated by the Proposing Department to management and managers. For example, they create easy-to-understand reports for management and provide specific behavioral improvement proposals to individual managers.

[0058] (Example 2) A management issue visualization system according to an embodiment of the present invention visualizes a company's management issues based on data and provides feedback to management and individual supervisors to promote fundamental improvements. This system collects and analyzes various data, analyzing managers' behavior and employee stress levels to identify management issues and automatically generates individual improvement proposals using a generation AI. Next, the system presents an analysis report and improvement proposals, creates an easy-to-understand report for management, and also outputs specific behavioral improvement proposals for individual managers. For example, the system analyzes managers' behavior from emails and chat logs and analyzes their superiors' attitudes using a behavioral recognition AI. It also estimates employees' stress levels from biometric data and behavior. This allows the system to identify management issues and automatically generate individual improvement proposals using a generation AI. Next, the system presents an analysis report and improvement proposals. It also creates an easy-to-understand report for management and outputs specific behavioral improvement proposals for individual managers. This system utilizes data and AI to visualize data and presents improvement guidelines to both management and managers, promoting fundamental organizational reform. The revenue model is based on pay-as-you-go or subscription, and aims to develop a business that prioritizes planning according to the scale of the company and maximizing effectiveness. As a result, the management issue visualization system visualizes a company's management issues based on data and provides feedback to management and individual superiors, encouraging essential improvements.

[0059] A management issue visualization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects various data within a company. For example, the collection unit can collect emails and chat logs. The collection unit can also collect biometric data and behavioral data. For example, the collection unit collects biometric data such as employees' heart rates and electrodermal activity. The collection unit can also collect behavioral data of employees. For example, the collection unit collects behavioral data such as employees' movement history and working hours. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the words and actions of managers from emails and chat logs. The analysis unit can also analyze the attitude of superiors using behavior recognition AI. For example, the analysis unit analyzes the superior's facial expressions and posture to evaluate the attitude. The analysis unit can also estimate the stress state of employees from the biometric data and behavioral data. For example, the analysis unit analyzes fluctuations in heart rate and electrodermal activity to estimate the stress state. The proposal unit generates improvement proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit automatically generates individual improvement proposals using a generation AI. The generation AI can use a text generation AI such as GPT-4 or Gemini. The proposal unit uses the generation AI to generate behavioral improvement proposals for managers and business process improvement proposals. The provision unit provides the improvement proposals generated by the proposal unit to management and managers. For example, the provision unit creates easy-to-understand reports for management. The provision unit can also output specific behavioral improvement proposals for individual managers. For example, the provision unit provides managers with proposals to increase the frequency of meetings and proposals to improve feedback methods. As a result, the management issue visualization system according to the embodiment visualizes a company's management issues based on data and provides feedback to management and individual superiors, thereby promoting essential improvements.

[0060] The collection unit can collect emails, chat logs, or biometric data. The collection unit, for example, collects emails. Emails include text emails and HTML emails. The collection unit acquires email data from a mail server and stores it for analysis. The collection unit can also collect chat logs. Chat logs include text format and JSON format. The collection unit acquires chat logs from a chat application and stores them for analysis. The collection unit can also collect biometric data. The biometric data includes heart rate and electrodermal activity. The collection unit acquires biometric data from wearable devices and sensors and stores it for analysis. This enables more comprehensive analysis by collecting data from various data sources. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input email data into AI, which then analyzes the content of the email.

[0061] The analysis unit can analyze the data collected by the collection unit and identify management issues. The analysis unit, for example, analyzes emails and chat logs collected by the collection unit. For example, the analysis unit can use natural language processing technology to analyze the content of emails and chat logs and evaluate the manager's words and actions. The analysis unit can also analyze the supervisor's attitude using behavioral recognition AI. For example, the analysis unit can analyze the supervisor's facial expressions and posture to evaluate their attitude. Furthermore, the analysis unit can estimate the employee's stress level from biometric data and behavioral data. For example, the analysis unit can analyze heart rate fluctuations and changes in electrodermal activity to estimate the stress level. By analyzing the data and identifying management issues, specific areas for improvement can be identified. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into AI, which then analyzes the data and identifies management issues.

[0062] The proposal unit can automatically generate individual improvement proposals using a generation AI. The proposal unit automatically generates individual improvement proposals using, for example, a generation AI. Examples of generation AI include text generation AIs such as GPT-4 and Gemini. The proposal unit inputs data related to management issues to the generation AI, which then generates improvement proposals based on the data. For example, the proposal unit inputs a prompt to the generation AI, such as, "Please suggest ways to improve the manager's lack of communication," and the generation AI generates specific improvement proposals. The proposal unit can also use the generation AI to generate business process improvement proposals and cost reduction measures. For example, the proposal unit inputs a prompt to the generation AI, such as, "Please suggest ways to improve the efficiency of the business process," and the generation AI generates specific improvement proposals. In this way, the generation AI can automatically generate and efficiently provide individual improvement proposals. Some or all of the above-described processing in the proposal unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the proposal unit inputs data to the generation AI, which then generates improvement proposals.

[0063] The provision unit can create easy-to-understand reports for management and output specific behavioral improvement suggestions for individual managers. For example, the provision unit creates easy-to-understand reports for management. The reports include graph displays and summaries. The provision unit creates reports in a visually easy-to-understand format to make it easier for management to understand management issues. The provision unit can also output specific behavioral improvement suggestions for individual managers. For example, the provision unit provides managers with suggestions to increase the frequency of meetings and suggestions to improve feedback methods. This can promote improvement throughout the organization by providing specific improvement suggestions to management and individual managers. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit inputs data into a generation AI, which generates reports and improvement suggestions.

[0064] The analysis unit can analyze the superior's attitude using behavior recognition AI. The analysis unit can analyze the superior's attitude using, for example, behavior recognition AI. Behavior recognition AI includes posture recognition and facial expression recognition. The analysis unit inputs the superior's behavioral data to the behavior recognition AI, which then evaluates the superior's attitude based on the data. For example, the analysis unit inputs a prompt to the behavior recognition AI, such as, "Analyze the superior's facial expressions and evaluate their attitude," and the behavior recognition AI then makes a specific evaluation. The analysis unit can also use behavior recognition AI to evaluate the superior's communication style and leadership. For example, the analysis unit inputs a prompt to the behavior recognition AI, such as, "Analyze the superior's communication style and evaluate their leadership," and the behavior recognition AI then makes a specific evaluation. In this way, the behavior recognition AI can accurately analyze the superior's attitude and identify areas for improvement. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs data to the behavior recognition AI, which then evaluates the superior's attitude.

[0065] The analysis unit can estimate an employee's stress state from biometric data or behavior. The analysis unit estimates an employee's stress state from, for example, biometric data or behavioral data. Estimation of stress state includes heart rate variability and self-report questionnaires. The analysis unit analyzes the biometric data and behavioral data to evaluate the employee's stress state. For example, the analysis unit analyzes heart rate variability and changes in electrodermal activity to estimate the stress state. The analysis unit can also analyze the employee's behavioral data to evaluate the stress state. For example, the analysis unit analyzes data on the employee's working hours and break times to estimate the stress state. This allows for accurate estimation of the employee's stress state, allowing appropriate measures to be taken. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit inputs biometric data into AI, which then estimates the stress state.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The collection unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the collection unit analyzes the user's facial expressions and calculates an emotion score. The collection unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the collection unit adjusts the timing of data collection based on the estimated user emotions. For example, if the user is stressed, the frequency of data collection can be reduced to reduce the burden. Alternatively, if the user is relaxed, the frequency can be increased to collect detailed data. Furthermore, if the user is in a hurry, only important data can be collected preferentially. This reduces the burden on the user by adjusting the timing of data collection according to the user's emotions, enabling efficient data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs emotional data into the AI, which then adjusts the timing of data collection.

[0067] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history. The collection unit selects the most efficient collection method based on the past data collection history. For example, the collection unit determines the collection frequency and the priority of data to be collected from the past data collection history. The collection unit can also optimize the collection method based on the past data collection history. For example, the collection unit analyzes past data collection history and selects a method that minimizes collection cost and collection time. In this way, by analyzing the past data collection history, the optimal collection method can be selected and efficient data collection becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs past data collection history into AI, which selects the optimal collection method.

[0068] The collection unit can filter data based on the user's current work situation and areas of interest when collecting data. For example, the collection unit filters data based on the user's current work situation and areas of interest when collecting data. Filtering includes keyword filtering, category filtering, and the like. The collection unit analyzes the user's work situation and areas of interest and prioritizes collection of highly relevant data. For example, the collection unit prioritizes collection of data related to a project the user is currently working on. The collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the collection unit filters data based on keywords related to the user's areas of interest. Furthermore, the collection unit can collect only necessary data depending on the user's work situation. For example, if the user is in a hurry, the collection unit prioritizes collection of only important data. This allows for efficient collection of highly relevant data by filtering data based on the user's work situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit inputs the user's work situation and areas of interest into AI, which then filters the data.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The collection unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the collection unit analyzes the user's facial expressions and calculates an emotion score. The collection unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the collection unit determines the priority of data to be collected based on the estimated user emotions. For example, if the user is stressed, it can prioritize collecting more important data. Also, if the user is relaxed, it can prioritize collecting detailed data. Furthermore, if the user is in a hurry, it can prioritize collecting the minimum necessary data. In this way, by prioritizing data according to the user's emotions, important data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit inputs emotional data into AI, which then prioritizes the data.

[0070] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration when collecting data. Geographical location information includes GPS data, IP address, etc. The collection unit analyzes the user's geographical location information and prioritizes collecting highly relevant data. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also prioritize collecting nearby data based on the user's location information. For example, the collection unit collects data related to nearby events and news based on the user's location information. Furthermore, when the user is traveling, the collection unit can collect data related to the user's destination. For example, when the user is on a business trip, the collection unit collects data related to the business trip destination. This allows for efficient collection of highly relevant data by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit inputs geographical location information into the AI, which then prioritizes collecting highly relevant data.

[0071] The collection unit can analyze the user's social media activity during data collection and collect highly relevant data. For example, the collection unit analyzes the user's social media activity during data collection. The social media activity includes the content of posts and the number of likes. The collection unit analyzes the user's social media activity and collects highly relevant data. For example, the collection unit collects relevant data based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activity. For example, the collection unit analyzes the user's social media activity and collects data based on keywords related to topics of interest. Furthermore, the collection unit can analyze the user's social media comments and collect necessary data. For example, the collection unit analyzes the user's social media comments and collects highly relevant data. This makes it possible to efficiently collect highly relevant data by analyzing the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit inputs the social media activity into AI, which then collects highly relevant data.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The analysis unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial expressions and calculates an emotion score. The analysis unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. On the other hand, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. By adjusting the presentation method of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis department inputs emotional data into the AI, which then adjusts how the analysis is presented.

[0073] During analysis, the analysis unit can adjust the level of analysis detail based on the importance of the data. The analysis unit adjusts the level of analysis detail based on, for example, the importance of the data. The importance of data includes the impact on business operations and the reliability of the data. The analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. For example, the analysis unit applies a detailed statistical analysis or a machine learning algorithm to data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs simple aggregation or summarization on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance and postpones analysis of data with low importance. This enables efficient analysis by adjusting the level of analysis detail based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit inputs the importance of the data into AI, which then adjusts the level of analysis detail.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies different analysis algorithms depending on the data category. Data categories include text data, numerical data, image data, etc. The analysis unit evaluates the data category and selects an appropriate analysis algorithm. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the analysis unit can also apply an image recognition algorithm to image data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the data category into AI, which selects an appropriate analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The analysis unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial expressions and calculates an emotion score. The analysis unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the analysis unit adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, a short and concise analysis result can be provided. On the other hand, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is excited, an analysis result with visually stimulating effects can be provided. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide the user with an optimal analysis result. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs emotional data into the AI, which then adjusts the length of the analysis.

[0076] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit determines the analysis priority based on, for example, the time when the data was collected. The time when the data was collected includes the latest data and past data. The analysis unit evaluates the time when the data was collected and prioritizes analyzing the latest data. For example, the analysis unit prioritizes analyzing the latest data and refers to past data, while emphasizing the latest data. The analysis unit can also adjust the analysis priority based on the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, determining the analysis priority based on the time when the data was collected enables analysis that prioritizes the latest data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the time when the data was collected into AI, and the AI ​​determines the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit adjusts the order of analysis based on, for example, the relevance of the data. Data relevance includes correlation and causal relationship. The analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. For example, the analysis unit determines the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the relevance of the data into AI, and the AI ​​determines the order of analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The suggestion unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the suggestion unit analyzes the user's facial expressions and calculates an emotion score. The suggestion unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the suggestion unit adjusts the way suggestions are presented based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. If the user is in a hurry, a suggestion that focuses on the main points can be provided. In this way, by adjusting the way suggestions are presented according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI or without using a generation AI. For example, the suggestion unit inputs emotional data into the generation AI, which then adjusts the way the suggestion is expressed.

[0079] The suggestion unit can adjust the level of detail of the proposal based on the importance of the issue when making a proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the issue. The importance of the issue includes the impact on business operations and the urgency of resolution. The suggestion unit evaluates the importance of the issue and provides detailed proposals for issues of high importance. For example, the suggestion unit provides detailed improvement measures and specific action plans for issues of high importance. The suggestion unit can also provide simplified proposals for issues of low importance. For example, the suggestion unit provides simple advice or summaries for issues of low importance. Furthermore, the suggestion unit can determine the priority of the proposals based on the importance of the issue. For example, the suggestion unit prioritizes proposals for issues of high importance and postpones proposals for issues of low importance. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the issue. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI or without using a generation AI. For example, the proposal unit inputs the importance of the issue into the generation AI, which then adjusts the level of detail in the proposal.

[0080] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the issue. For example, the proposal unit applies different proposal algorithms depending on the category of the issue. Issue categories include technical issues, organizational issues, etc. The proposal unit evaluates the category of the issue and selects an appropriate proposal algorithm. For example, the proposal unit can provide a proposal to improve communication for an issue of lack of communication. Furthermore, the proposal unit can provide a proposal to review the allocation of work for an issue of excessive work load. Furthermore, the proposal unit can provide a proposal to encourage consideration for an issue of lack of consideration for subordinates. This enables highly accurate proposals by applying an appropriate proposal algorithm depending on the category of the issue. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit inputs the category of the issue into a generation AI, which selects an appropriate proposal algorithm.

[0081] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. Emotion estimation includes facial expression recognition and voice analysis. The suggestion unit analyzes the user's emotion data and evaluates the user's emotional state. For example, the suggestion unit analyzes the user's facial expression and calculates an emotion score. The suggestion unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the suggestion unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the suggestion unit adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, if the user is excited, the suggestion unit can provide a suggestion with a visually stimulating effect. In this way, by adjusting the length of the suggestion based on the user's emotion, the suggestion unit can provide the user with the optimal suggestion. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit inputs emotion data into the generation AI, which then adjusts the length of the suggestion.

[0082] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the issue occurred. The suggestion unit, for example, determines the priority of the proposal based on the time when the issue occurred. The time when the issue occurred includes the most recent issue and past issues. The suggestion unit evaluates the time when the issue occurred and prioritizes the most recent issue. For example, the suggestion unit prioritizes the most recent issue and refers to past issues while proposing the most recent issue. The suggestion unit can also adjust the priority of the proposal based on the time when the issue occurred. For example, the suggestion unit determines the priority of the proposal based on the time when the issue occurred. In this way, by determining the priority of the proposal based on the time when the issue occurred, it is possible to make a proposal that prioritizes the most recent issue. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit inputs the time when the issue occurred into the generation AI, and the generation AI determines the priority of the proposal.

[0083] The suggestion unit can adjust the order of proposals based on the relevance of the tasks when making a proposal. The suggestion unit adjusts the order of proposals based on, for example, the relevance of the tasks. The relevance of the tasks includes correlation and causal relationships. The suggestion unit evaluates the relevance of the tasks and prioritizes proposing highly relevant tasks. For example, the suggestion unit prioritizes proposing highly relevant tasks and postpones less relevant tasks. The suggestion unit can also adjust the order of proposals according to the relevance of the tasks. For example, the suggestion unit determines the order of proposals based on the relevance of the tasks. This enables efficient proposals by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit inputs the relevance of the tasks into a generation AI, which determines the order of proposals.

[0084] The providing unit can estimate the user's emotions and adjust the display method of the report based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The providing unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the providing unit analyzes the user's facial expressions and calculates an emotion score. The providing unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the providing unit adjusts the display method of the report based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible report can be provided. On the other hand, if the user is relaxed, a detailed report can be provided. Furthermore, if the user is in a hurry, a report that focuses on the main points can be provided. In this way, by adjusting the display method of the report according to the user's emotions, it is possible to provide a report that is easy for the user to understand. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the emotion data into AI, and the AI ​​adjusts the display method of the report.

[0085] The providing unit can select the optimal display method by referring to the user's past operation history when displaying a report. For example, the providing unit selects the optimal display method by referring to the user's past operation history when displaying a report. The operation history includes a click history, a browsing history, etc. The providing unit analyzes the user's past operation history and selects the optimal display method. For example, the providing unit preferentially provides a display method that the user has preferred in the past. The providing unit can also select a display method with high visibility from the user's past operation history. For example, the providing unit analyzes the user's operation history and provides the optimal display method. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the operation history into AI, and the AI ​​selects the optimal display method.

[0086] The providing unit can estimate the user's emotions and adjust the report operation procedures based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. Emotion estimation includes facial expression recognition and voice analysis. The providing unit analyzes the user's emotional data and evaluates the user's emotional state. For example, the providing unit analyzes the user's facial expressions and calculates an emotion score. The providing unit can also analyze the user's voice data and evaluate the user's emotional state. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Next, the providing unit adjusts the report operation procedures based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide operation procedures that focus on the main points. In this way, by adjusting the report operation procedures according to the user's emotions, the optimal operation procedures for the user can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit inputs the emotion data into AI, and the AI ​​adjusts the operation procedures.

[0087] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying a report. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when displaying a report. The device information includes the device type, OS version, etc. The providing unit analyzes the user's device information and selects the optimal display method. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. In this way, the optimal display method can be provided by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the device information into AI, which selects the optimal display method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects various data using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates improvement proposals using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides improvement proposals to management and managers. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects various data using the camera 42 and microphone 238 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates improvement proposals using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides improvement proposals to management and managers. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects various data using the camera 42 and microphone 238 of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates improvement proposals using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides improvement proposals to management and managers. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects various data using the camera 42 and microphone 238 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates improvement proposals using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides improvement proposals to management and managers.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. If the user is relaxed, the frequency can be increased to collect detailed data. Furthermore, if the user is in a hurry, only important data can be collected preferentially. In this way, adjusting the timing of data collection according to the user's emotions reduces the burden on the user and enables efficient data collection.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, simple, highly visible analysis results can be provided. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is in a hurry, analysis results that focus on the main points can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is nervous, a simple and highly visible suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be provided. In this way, by adjusting the way suggestions are expressed according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand.

[0092] The providing unit can estimate the user's emotions and adjust the display method of the report based on the estimated emotions. For example, if the user is nervous, a simple, highly visible report can be provided. If the user is relaxed, a detailed report can be provided. Furthermore, if the user is in a hurry, a report that focuses on the main points can be provided. In this way, by adjusting the display method of the report according to the user's emotions, it is possible to provide a report that is easy for the user to understand.

[0093] The providing unit can estimate the user's emotions and adjust the report operation procedures based on the estimated emotions. For example, if the user is nervous, simple and intuitive operation procedures can be provided. If the user is relaxed, detailed operation procedures can be provided. Furthermore, if the user is in a hurry, operation procedures that focus on the main points can be provided. In this way, by adjusting the report operation procedures according to the user's emotions, it is possible to provide the optimal operation procedures for the user.

[0094] The collection unit can analyze past data collection history and select the optimal collection method. For example, the collection unit determines the collection frequency and the priority of data to be collected from the past data collection history. The collection unit can also optimize the collection method based on the past data collection history. For example, the collection unit analyzes the past data collection history and selects a method that minimizes collection costs and collection time. In this way, by analyzing the past data collection history, the optimal collection method can be selected, enabling efficient data collection.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0096] The suggestion unit can adjust the level of detail of the proposal based on the importance of the issue. For example, the suggestion unit can provide a detailed proposal for an issue with a high level of importance and a simplified proposal for an issue with a low level of importance. This allows for efficient proposals by adjusting the level of detail of the proposal based on the importance of the issue.

[0097] When displaying a report, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can provide a display method that the user has used favorably in the past with priority. The providing unit can also select a display method with high visibility from the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0098] When displaying a report, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the providing unit can provide a display method that is optimized for a large screen. In this way, the optimal display method can be provided by taking into account the user's device information.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection department collects various data within the company, such as emails, chat logs, biometric data (heart rate, electrodermal activity, etc.), and behavioral data (movement history, work hours, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the manager's words and actions from emails and chat logs, and uses behavioral recognition AI to analyze the boss's facial expressions and posture to evaluate their attitude. It also estimates the employee's stress level from biometric and behavioral data. Step 3: The proposal unit generates improvement proposals based on the analysis results obtained by the analysis unit. For example, using a generative AI (such as GPT-4 or Gemini), it automatically generates proposals for improving manager behavior and business processes. Step 4: The Providing Department provides the improvement proposals generated by the Proposing Department to management and managers. For example, they create easy-to-understand reports for management and provide specific behavioral improvement proposals to individual managers.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

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

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that generates improvement proposals based on the analysis results obtained by the analysis unit; a providing unit that provides the improvement proposals generated by the proposing unit to management and managers; Equipped with A system characterized by:

2. The collecting unit Collect emails, chat logs, or biometric data 2. The system of claim 1.

3. The analysis unit Analyzing the data collected by the collection unit and extracting management issues 2. The system of claim 1.

4. The proposal unit Automatically generate individual improvement proposals using generative AI 2. The system of claim 1.

5. The providing unit Create easy-to-understand reports for management and output specific behavioral improvement suggestions for individual managers 2. The system of claim 1.

6. The analysis unit Analyzing your boss's attitude using behavioral recognition AI 2. The system of claim 1.

7. The analysis unit Estimating employee stress levels from biometric data or behavior 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

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