System

Abstract

An object of a system according to an embodiment is to analyze a daily work report and task contents and identify work that can be automated and made efficient.SOLUTION: A system includes an analysis unit, a specification unit, a report unit, and a proposal unit. The analysis unit analyzes the daily report or the task content. The specification unit specifies a task that can be automated or streamlined based on the result analyzed by the analysis unit. The reporting unit reports the service specified by the specifying unit. The proposal unit proposes effective task automation based on the content reported by the reporting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze daily work reports and task content to identify tasks that can be automated or made more efficient, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze daily work reports and task contents, and identify work that can be automated or made more efficient. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an identification unit, a reporting unit, and a proposal unit. The analysis unit analyzes daily business reports or task content. The identification unit identifies business tasks that can be automated or streamlined based on the results of the analysis by the analysis unit. The reporting unit reports the business tasks identified by the identification unit. The proposal unit proposes effective business automation based on the content reported by the reporting unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze daily work reports and task contents to identify work that can be automated or made more efficient. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business process analysis system according to an embodiment of the present invention utilizes a generative AI to analyze employees' daily work reports and task details, and identifies tasks that can be automated or streamlined. This enables the business process analysis system to streamline employees' work and improve productivity.

[0029] A business analysis system according to an embodiment includes an analysis unit, an identification unit, a reporting unit, and a proposal unit. The analysis unit analyzes daily work reports or task content. For example, the analysis unit uses natural language processing technology to extract the content, progress, and problems of work. The analysis unit can also analyze data using machine learning algorithms to identify business patterns. The analysis unit can also detect business trends and anomalies using data mining technology. The identification unit identifies business tasks that can be automated or streamlined based on the results of the analysis by the analysis unit. For example, the identification unit identifies routine data entry tasks and repetitive report creation as targets for automation. The identification unit can also propose optimal automation tools taking into account the complexity and frequency of the tasks. The identification unit can also prioritize tasks and notify employees in real time. The reporting unit reports the tasks identified by the identification unit. For example, when employees report their work, the reporting unit uses a generation AI to automatically complete the input content or provide templates. The reporting unit can also automatically organize the report content and provide it to managers in an easy-to-understand format. The reporting department can also integrate reports from different departments and projects and centrally manage the overall business status. The proposal department makes proposals for effective business automation based on the reports from the reporting department. For example, the proposal department proposes that part of customer support be delegated to a chatbot to reduce employee burden and improve efficiency. The proposal department can also simulate the effects of automation and quantitatively evaluate the predicted results. The proposal department can also collect feedback after automation is implemented and continuously improve the proposals. As a result, the business analysis system according to the embodiment can improve the efficiency of employee work and productivity. For example, employees can focus on important work based on the analysis results from the generation AI. Managers can also optimize work based on the suggestions from the generation AI. Furthermore, the efficiency of business processes across the entire company is improved, strengthening competitiveness.

[0030] The analysis unit can refer to employees' past performance data and propose optimal work allocation for employees. For example, the analysis unit uses a generation AI to analyze employees' past daily work reports and task details, and propose optimal work allocation based on the performance data. For example, it can identify tasks that employees excel at from past data and assign those tasks preferentially. The analysis unit can also optimize work allocation by taking into account employees' skill sets and work priorities. The analysis unit can also adjust work allocation by taking into account team balance. This makes it possible to improve work efficiency by proposing optimal work allocation based on employees' past performance data.

[0031] The analysis unit can automatically set the priority of tasks and notify employees in real time. For example, the generation AI analyzes daily work reports and task content and automatically sets the priority of tasks. For example, it classifies tasks based on urgency and importance and notifies employees in real time. The analysis unit can also set priorities taking into account the dependencies between tasks. The analysis unit can also monitor the progress of tasks in real time and dynamically adjust priorities. This allows for the automatic setting of task priorities and notification to employees in real time, thereby improving work efficiency.

[0032] The analysis unit can perform multimodal data analysis by including audio input and video logs in the data. For example, the analysis unit uses a generative AI to analyze audio input and video logs to complement daily work reports and task details. For example, it can analyze audio recordings of meetings and automatically generate minutes. The analysis unit can also analyze video logs to visualize the progress of work. The analysis unit can also integrate audio input and video logs with text data to perform multimodal data analysis. This enables more detailed work analysis by performing multimodal data analysis that includes audio input and video logs.

[0033] The analysis unit can cross-reference daily work reports from different departments and projects to identify common issues and opportunities for efficiency improvement. For example, the generation AI in the analysis unit cross-references daily reports from different departments to identify common issues. For example, it extracts problems occurring in multiple departments and proposes common solutions. The analysis unit can also cross-reference daily reports from different projects to identify opportunities for efficiency improvement. The analysis unit can also integrate data from different departments and projects to improve overall business efficiency. This makes it possible to identify common issues and opportunities for efficiency improvement by cross-referencing daily work reports from different departments and projects.

[0034] The Identification Department can propose the most suitable automation tool by taking into consideration the complexity and frequency of the work. For example, the Identification Department uses a generative AI to analyze the complexity and frequency of the work and propose the most suitable automation tool. For example, it can propose dedicated automation software for complex data processing work. The Identification Department can also propose RPA tools for routine work that is frequently performed. The Identification Department can also select the appropriate automation tool according to the skill level of the work. This makes it possible to improve the efficiency of work by proposing the most suitable automation tool by taking into consideration the complexity and frequency of the work.

[0035] The identification unit can automatically calculate the resources and costs required to automate the identified tasks and present them to the manager. For example, the identification unit uses a generation AI to automatically calculate the resources and costs required to automate tasks and present them to the manager. For example, it calculates the software implementation costs and labor cost reduction effects. The identification unit can also evaluate the time and manpower required to implement automation. The identification unit can also perform cost-benefit analysis to quantitatively evaluate the effects of automation. This allows the resources and costs required for automation to be automatically calculated and presented to the manager, enabling efficient resource allocation.

[0036] The identification department can refer to best practices from different industries and companies to identify tasks to be automated. For example, the identification department uses generative AI to analyze best practices from different industries to identify tasks to be automated. For example, it introduces automation methods that have been successful in other industries. The identification department can also identify tasks to be automated based on successful cases from other companies. The identification department can also refer to industry standards and guidelines to propose optimal automation methods. This makes it possible to effectively identify tasks to be automated by referring to best practices from different industries and companies.

[0037] When streamlining the reporting process, the reporting department can learn the input patterns of employees and provide optimal input completion. For example, the reporting department uses a generative AI to learn the input patterns of employees and provide optimal input completion. For example, an auto-completion function is provided based on past input data. The reporting department can also provide templates to streamline the input work of employees. The reporting department can also use an auto-complete function to predict what employees will enter and support their input work. In this way, the reporting process can be made more efficient by learning the input patterns of employees and providing optimal input completion.

[0038] The reporting department can utilize voice input and video logs to support various reporting formats when streamlining the reporting process. For example, the reporting department uses a generative AI to analyze voice input and automatically convert the report content into text. For example, the reporting department can analyze audio recordings of meetings and automatically generate minutes. The reporting department can also analyze video logs and visualize the report content. The reporting department can also integrate voice input and video logs with text data to support various reporting formats. This makes it possible to utilize voice input and video logs to support various reporting formats, thereby improving the efficiency of the reporting process.

[0039] The reporting department can integrate report content from different departments and projects and centrally manage the overall business status. For example, the reporting department will build a system in which generation AI integrates report content from different departments and projects and centrally manages the overall business status. For example, it will display report content for each department together. The reporting department can also integrate the progress of each project and visualize the overall business status. The reporting department can also integrate data from different departments and projects and monitor the business status in real time. This allows the integration of report content from different departments and projects and centrally manage the overall business status, thereby improving business efficiency.

[0040] The proposal unit can simulate the effects of automation and quantitatively evaluate the predicted results. For example, the proposal unit uses a generative AI to simulate the effects of automation and quantitatively evaluate the predicted results. For example, it simulates the time reduction effect of automation. The proposal unit can also simulate the cost reduction effect and calculate ROI. The proposal unit can also perform scenario analysis to compare and evaluate the effects of different automation methods. This makes it possible to simulate the effects of automation and quantitatively evaluate the predicted results, thereby making it possible to make effective automation proposals.

[0041] The suggestion unit can collect feedback after automation is implemented and continuously improve the content of the suggestions. For example, the suggestion unit builds a system in which the generative AI collects feedback after automation is implemented and continuously improves the content of the suggestions. For example, the suggestion unit reviews the automation method based on user feedback. The suggestion unit can also analyze the feedback data and identify areas for improvement. The suggestion unit can also optimize the next automation suggestion based on the feedback. In this way, more effective automation can be achieved by collecting feedback after automation is implemented and continuously improving the content of the suggestions.

[0042] The proposal department can compare the target of automation with success stories from other companies and industries to select the optimal method. For example, the proposal department uses a generative AI to analyze success stories from other companies and industries and select the target of automation in the optimal way. For example, the proposal department can propose an automation method based on success stories from other companies. The proposal department can also refer to industry standards and guidelines to select the optimal automation method. The proposal department can also perform risk assessments based on success stories and select the optimal automation method. This makes it possible to select the optimal automation method by comparing with success stories from other companies and industries.

[0043] The proposal department can integrate automation proposals from different departments and projects to improve overall business efficiency. For example, the proposal department builds a system in which the generation AI integrates automation proposals from different departments and projects to improve overall business efficiency. For example, it implements automation proposals for each department together. The proposal department can also integrate automation proposals for each project to improve overall business efficiency. The proposal department can also integrate automation proposals from different departments and projects to optimize resource allocation. In this way, by integrating automation proposals from different departments and projects, overall business efficiency can be improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The analysis unit can analyze employees' health data and adjust their work load. For example, it can analyze employees' heart rate and sleep data to detect signs of overwork. The analysis unit can also dynamically adjust work load based on employees' health status. The analysis unit can also suggest appropriate break times to employees based on their health data. This makes it possible to allocate work taking into account employees' health status and prevent overwork.

[0046] The identification department can analyze employees' skill sets and propose training programs to improve their skills. For example, it can analyze employees' work history and identify skill gaps. The identification department can also propose optimal training programs based on employees' career paths. The identification department can also provide appropriate training to employees according to the skills required for their work. This helps employees improve their skills and improves work efficiency.

[0047] The reporting department can analyze the content of employee reports and identify areas for improvement in work. For example, it can extract frequently occurring problems from the reports and propose improvement measures. The reporting department can also identify areas for improvement in work processes based on employee feedback. The reporting department can also make proposals for improving work efficiency based on the content of the reports. In this way, by analyzing the content of employee reports and identifying areas for improvement in work, work efficiency can be improved.

[0048] The proposal department can analyze employees' work performance and propose incentive programs. For example, it can propose bonuses and promotions based on work results. The proposal department can also design optimal incentive programs based on employee performance data. The proposal department can also simulate the effects of incentive programs and evaluate predicted results. This makes it possible to propose incentive programs that will improve employees' work performance.

[0049] The analysis unit can analyze employee work data and predict the effects of automating work. For example, it can simulate the effects of automation based on past work data. The analysis unit can also analyze work progress data and quantitatively evaluate the effects of automation. The analysis unit can also compare and evaluate the effects of different automation methods and propose the optimal automation method. This makes it possible to propose effective automation by analyzing employee work data and predicting the effects of automation.

[0050] The identification department can analyze employee work data and evaluate work risks. For example, it can analyze work progress data and identify high-risk work. The identification department can also analyze work dependencies and evaluate the impact of risks. The identification department can also make proposals for risk mitigation based on the risk data. In this way, risk management is strengthened by analyzing employee work data and evaluating work risks.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The analysis unit analyzes the daily work reports or task content. For example, the analysis unit uses natural language processing technology to extract the work content, progress, and problems. The analysis unit can also analyze the data using machine learning algorithms to identify work patterns. Furthermore, data mining technology can be used to detect work trends and anomalies. Step 2: The Identification Department identifies tasks that can be automated or streamlined based on the results of the analysis by the Analysis Department. For example, the Identification Department identifies routine data entry tasks and repetitive report creation as targets for automation. The Department can also recommend the most appropriate automation tools, taking into account the complexity and frequency of the tasks. Furthermore, the Department can set priorities for tasks and notify employees in real time. Step 3: The reporting department reports the work identified by the identification department. For example, when an employee reports work, the reporting department can use the generation AI to automatically complete the input or provide a template. The reporting department can also automatically organize the report content and provide it to managers in an easy-to-understand format. Furthermore, the reporting content from different departments and projects can be integrated, and the overall work status can be managed centrally. Step 4: The proposal department makes effective proposals for business automation based on the reports from the reporting department. For example, the proposal department suggests that part of the customer support process be delegated to a chatbot, thereby reducing the burden on employees and improving efficiency. The proposal department can also simulate the effects of automation and quantitatively evaluate the predicted results. Furthermore, it can collect feedback after the automation is implemented and continuously improve its proposals.

[0053] (Example 2) A business process analysis system according to an embodiment of the present invention utilizes a generative AI to analyze employees' daily work reports and task details, and identifies tasks that can be automated or streamlined. This enables the business process analysis system to streamline employees' work and improve productivity.

[0054] A business analysis system according to an embodiment includes an analysis unit, an identification unit, a reporting unit, and a proposal unit. The analysis unit analyzes daily work reports or task content. For example, the analysis unit uses natural language processing technology to extract the content, progress, and problems of work. The analysis unit can also analyze data using machine learning algorithms to identify business patterns. The analysis unit can also detect business trends and anomalies using data mining technology. The identification unit identifies business tasks that can be automated or streamlined based on the results of the analysis by the analysis unit. For example, the identification unit identifies routine data entry tasks and repetitive report creation as targets for automation. The identification unit can also propose optimal automation tools taking into account the complexity and frequency of the tasks. The identification unit can also prioritize tasks and notify employees in real time. The reporting unit reports the tasks identified by the identification unit. For example, when employees report their work, the reporting unit uses a generation AI to automatically complete the input content or provide templates. The reporting unit can also automatically organize the report content and provide it to managers in an easy-to-understand format. The reporting department can also integrate reports from different departments and projects and centrally manage the overall business status. The proposal department makes proposals for effective business automation based on the reports from the reporting department. For example, the proposal department proposes that part of customer support be delegated to a chatbot to reduce employee burden and improve efficiency. The proposal department can also simulate the effects of automation and quantitatively evaluate the predicted results. The proposal department can also collect feedback after automation is implemented and continuously improve the proposals. As a result, the business analysis system according to the embodiment can improve the efficiency of employee work and productivity. For example, employees can focus on important work based on the analysis results from the generation AI. Managers can also optimize work based on the suggestions from the generation AI. Furthermore, the efficiency of business processes across the entire company is improved, strengthening competitiveness.

[0055] The analysis unit can refer to employees' past performance data and propose optimal work allocation for employees. For example, the analysis unit uses a generation AI to analyze employees' past daily work reports and task details, and propose optimal work allocation based on the performance data. For example, it can identify tasks that employees excel at from past data and assign those tasks preferentially. The analysis unit can also optimize work allocation by taking into account employees' skill sets and work priorities. The analysis unit can also adjust work allocation by taking into account team balance. This makes it possible to improve work efficiency by proposing optimal work allocation based on employees' past performance data.

[0056] The analysis unit can automatically set the priority of tasks and notify employees in real time. For example, the generation AI analyzes daily work reports and task content and automatically sets the priority of tasks. For example, it classifies tasks based on urgency and importance and notifies employees in real time. The analysis unit can also set priorities taking into account the dependencies between tasks. The analysis unit can also monitor the progress of tasks in real time and dynamically adjust priorities. This allows for the automatic setting of task priorities and notification to employees in real time, thereby improving work efficiency.

[0057] The analysis unit can use the emotion estimation function to infer emotions from employees' daily reports and analyze fluctuations in stress and motivation. For example, the analysis unit uses the generation AI to analyze employees' daily reports and evaluate stress levels using the emotion estimation function. For example, it can identify daily reports with a lot of negative expressions and analyze the causes of stress. The analysis unit can also extract positive emotions from employees' daily reports and analyze fluctuations in motivation. The analysis unit can also analyze fluctuations in stress and motivation over time based on employees' emotional data. This makes it possible to understand employees' psychological state by inferring emotions from employees' daily reports and analyzing fluctuations in stress and motivation.

[0058] The analysis unit can perform multimodal data analysis by including audio input and video logs in the data. For example, the analysis unit uses a generative AI to analyze audio input and video logs to complement daily work reports and task details. For example, it can analyze audio recordings of meetings and automatically generate minutes. The analysis unit can also analyze video logs to visualize the progress of work. The analysis unit can also integrate audio input and video logs with text data to perform multimodal data analysis. This enables more detailed work analysis by performing multimodal data analysis that includes audio input and video logs.

[0059] The analysis unit can cross-reference daily work reports from different departments and projects to identify common issues and opportunities for efficiency improvement. For example, the generation AI in the analysis unit cross-references daily reports from different departments to identify common issues. For example, it extracts problems occurring in multiple departments and proposes common solutions. The analysis unit can also cross-reference daily reports from different projects to identify opportunities for efficiency improvement. The analysis unit can also integrate data from different departments and projects to improve overall business efficiency. This makes it possible to identify common issues and opportunities for efficiency improvement by cross-referencing daily work reports from different departments and projects.

[0060] The analysis unit uses the emotion estimation function to analyze the emotions of employees when they enter their daily reports in real time and can provide positive feedback. For example, the analysis unit uses a generation AI to analyze the emotions of employees when they enter their daily reports in real time and can provide positive feedback. For example, if positive emotions are detected, an encouraging message is displayed. The analysis unit can also provide advice to reduce stress when negative emotions are detected. The analysis unit can also provide feedback to improve motivation based on the employee's emotion data. In this way, by analyzing the emotions of employees when they enter their daily reports in real time and providing positive feedback, employee motivation is improved.

[0061] The Identification Department can propose the most suitable automation tool by taking into consideration the complexity and frequency of the work. For example, the Identification Department uses a generative AI to analyze the complexity and frequency of the work and propose the most suitable automation tool. For example, it can propose dedicated automation software for complex data processing work. The Identification Department can also propose RPA tools for routine work that is frequently performed. The Identification Department can also select the appropriate automation tool according to the skill level of the work. This makes it possible to improve the efficiency of work by proposing the most suitable automation tool by taking into consideration the complexity and frequency of the work.

[0062] The identification unit can automatically calculate the resources and costs required to automate the identified tasks and present them to the manager. For example, the identification unit uses a generation AI to automatically calculate the resources and costs required to automate tasks and present them to the manager. For example, it calculates the software implementation costs and labor cost reduction effects. The identification unit can also evaluate the time and manpower required to implement automation. The identification unit can also perform cost-benefit analysis to quantitatively evaluate the effects of automation. This allows the resources and costs required for automation to be automatically calculated and presented to the manager, enabling efficient resource allocation.

[0063] The identification unit can use the emotion estimation function to analyze the stress level that employees feel toward specific tasks and prioritize automation of tasks that cause high stress. For example, the identification unit uses a generation AI to analyze employees' emotions toward their tasks and evaluate their stress levels. For example, it identifies tasks that cause high stress and sets priorities for automation. The identification unit can also make automation suggestions to reduce stress based on employee emotion data. The identification unit can also reduce the burden on employees by automating tasks that cause high stress levels. This makes it possible to reduce employee stress by analyzing the stress level that employees feel toward specific tasks and prioritizing automation of tasks that cause high stress.

[0064] The identification department can refer to best practices from different industries and companies to identify tasks to be automated. For example, the identification department uses generative AI to analyze best practices from different industries to identify tasks to be automated. For example, it introduces automation methods that have been successful in other industries. The identification department can also identify tasks to be automated based on successful cases from other companies. The identification department can also refer to industry standards and guidelines to propose optimal automation methods. This makes it possible to effectively identify tasks to be automated by referring to best practices from different industries and companies.

[0065] The identification unit can use the emotion estimation function to analyze the satisfaction employees feel with automated tasks and improve the automation process based on the feedback. For example, the identification unit uses a generative AI to analyze employee emotions and evaluate their satisfaction with automated tasks. For example, it reevaluates tasks with low satisfaction and proposes improvement measures. The identification unit can also continuously improve the automation process based on employee feedback. The identification unit can also make suggestions to improve employee satisfaction based on the emotion data. In this way, employee satisfaction can be improved by analyzing the satisfaction employees feel with automated tasks and improving the automation process based on feedback.

[0066] When streamlining the reporting process, the reporting department can learn the input patterns of employees and provide optimal input completion. For example, the reporting department uses a generative AI to learn the input patterns of employees and provide optimal input completion. For example, an auto-completion function is provided based on past input data. The reporting department can also provide templates to streamline the input work of employees. The reporting department can also use an auto-complete function to predict what employees will enter and support their input work. In this way, the reporting process can be made more efficient by learning the input patterns of employees and providing optimal input completion.

[0067] The reporting department can use the emotion estimation function to analyze the emotions employees feel when reporting and identify areas for improvement in the reporting process. For example, the reporting department can use generative AI to analyze the emotions employees feel when reporting and identify areas for improvement in the reporting process. For example, the reporting department can review a reporting process that has a lot of negative emotions. The reporting department can also improve the efficiency of the reporting process based on employee emotion data. The reporting department can also suggest improvement measures to reduce employee stress based on emotion data. This allows the efficiency of the reporting process to be improved by analyzing the emotions employees feel when reporting and identifying areas for improvement in the reporting process.

[0068] The reporting department can utilize voice input and video logs to support various reporting formats when streamlining the reporting process. For example, the reporting department uses a generative AI to analyze voice input and automatically convert the report content into text. For example, the reporting department can analyze audio recordings of meetings and automatically generate minutes. The reporting department can also analyze video logs and visualize the report content. The reporting department can also integrate voice input and video logs with text data to support various reporting formats. This makes it possible to utilize voice input and video logs to support various reporting formats, thereby improving the efficiency of the reporting process.

[0069] The reporting department can integrate report content from different departments and projects and centrally manage the overall business status. For example, the reporting department will build a system in which generation AI integrates report content from different departments and projects and centrally manages the overall business status. For example, it will display report content for each department together. The reporting department can also integrate the progress of each project and visualize the overall business status. The reporting department can also integrate data from different departments and projects and monitor the business status in real time. This allows the integration of report content from different departments and projects and centrally manage the overall business status, thereby improving business efficiency.

[0070] The reporting department can use the emotion estimation function to analyze the emotions of employees when they report in real time and provide positive feedback. For example, the reporting department can use a generative AI to analyze the emotions of employees when they report in real time and provide positive feedback. For example, an encouraging message can be displayed if positive emotions are detected. The reporting department can also provide advice to reduce stress if negative emotions are detected. The reporting department can also provide feedback to improve motivation based on employee emotion data. In this way, the emotions of employees when they report can be analyzed in real time and positive feedback can be provided, thereby improving employee motivation.

[0071] The proposal unit can simulate the effects of automation and quantitatively evaluate the predicted results. For example, the proposal unit uses a generative AI to simulate the effects of automation and quantitatively evaluate the predicted results. For example, it simulates the time reduction effect of automation. The proposal unit can also simulate the cost reduction effect and calculate ROI. The proposal unit can also perform scenario analysis to compare and evaluate the effects of different automation methods. This makes it possible to simulate the effects of automation and quantitatively evaluate the predicted results, thereby making it possible to make effective automation proposals.

[0072] The suggestion unit can collect feedback after automation is implemented and continuously improve the content of the suggestions. For example, the suggestion unit builds a system in which the generative AI collects feedback after automation is implemented and continuously improves the content of the suggestions. For example, the suggestion unit reviews the automation method based on user feedback. The suggestion unit can also analyze the feedback data and identify areas for improvement. The suggestion unit can also optimize the next automation suggestion based on the feedback. In this way, more effective automation can be achieved by collecting feedback after automation is implemented and continuously improving the content of the suggestions.

[0073] The proposal department can compare the target of automation with success stories from other companies and industries to select the optimal method. For example, the proposal department uses a generative AI to analyze success stories from other companies and industries and select the target of automation in the optimal way. For example, the proposal department can propose an automation method based on success stories from other companies. The proposal department can also refer to industry standards and guidelines to select the optimal automation method. The proposal department can also perform risk assessments based on success stories and select the optimal automation method. This makes it possible to select the optimal automation method by comparing with success stories from other companies and industries.

[0074] The proposal department can integrate automation proposals from different departments and projects to improve overall business efficiency. For example, the proposal department builds a system in which the generation AI integrates automation proposals from different departments and projects to improve overall business efficiency. For example, it implements automation proposals for each department together. The proposal department can also integrate automation proposals for each project to improve overall business efficiency. The proposal department can also integrate automation proposals from different departments and projects to optimize resource allocation. In this way, by integrating automation proposals from different departments and projects, overall business efficiency can be improved.

[0075] The suggestion unit can use the emotion estimation function to analyze the satisfaction employees feel with the proposed automation and improve the proposal content based on the feedback. For example, the suggestion unit uses a generation AI to analyze employees' emotions and evaluate their satisfaction with the automation proposal. For example, the suggestion unit prioritizes implementing automation proposals that result in high satisfaction. The suggestion unit can also continuously improve the automation proposals based on employee feedback. The suggestion unit can also make suggestions to improve employee satisfaction based on the emotion data. In this way, employee satisfaction is improved by analyzing the satisfaction employees feel with the proposed automation and improving the proposal content based on feedback.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The analysis unit can analyze employees' health data and adjust their work load. For example, it can analyze employees' heart rate and sleep data to detect signs of overwork. The analysis unit can also dynamically adjust work load based on employees' health status. The analysis unit can also suggest appropriate break times to employees based on their health data. This makes it possible to allocate work taking into account employees' health status and prevent overwork.

[0078] The identification department can analyze employees' skill sets and propose training programs to improve their skills. For example, it can analyze employees' work history and identify skill gaps. The identification department can also propose optimal training programs based on employees' career paths. The identification department can also provide appropriate training to employees according to the skills required for their work. This helps employees improve their skills and improves work efficiency.

[0079] The reporting department can analyze the content of employee reports and identify areas for improvement in work. For example, it can extract frequently occurring problems from the reports and propose improvement measures. The reporting department can also identify areas for improvement in work processes based on employee feedback. The reporting department can also make proposals for improving work efficiency based on the content of the reports. In this way, by analyzing the content of employee reports and identifying areas for improvement in work, work efficiency can be improved.

[0080] The proposal department can analyze employees' work performance and propose incentive programs. For example, it can propose bonuses and promotions based on work results. The proposal department can also design optimal incentive programs based on employee performance data. The proposal department can also simulate the effects of incentive programs and evaluate predicted results. This makes it possible to propose incentive programs that will improve employees' work performance.

[0081] The analysis unit can use the emotion estimation function to analyze employee emotions and improve team dynamics. For example, it can analyze emotional interactions between team members and identify areas for improvement in communication. The analysis unit can also make suggestions for team building based on the emotion data. The analysis unit can also analyze emotional fluctuations over time and identify factors that affect team performance. This makes it possible to analyze employee emotions and improve team dynamics, thereby improving the performance of the entire team.

[0082] The identification unit can use the emotion estimation function to analyze employees' emotions and evaluate their suitability for work. For example, it can analyze emotional data for specific work and identify work for which they are highly suited. The identification unit can also evaluate employees' suitability for work based on the emotional data and propose optimal work allocation. The identification unit can also analyze emotional fluctuations and dynamically evaluate suitability for work. This allows for the efficiency of work to be improved by analyzing employees' emotions and evaluating their suitability for work.

[0083] The reporting department can use the emotion estimation function to analyze the emotions of employees and evaluate the reliability of the report content. For example, the reliability of the report content can be evaluated based on the emotion data and abnormal reports can be identified. The reporting department can also evaluate the consistency of the report content based on the emotion data. The reporting department can also analyze emotional fluctuations and dynamically evaluate the reliability of the report content. In this way, the accuracy of reports can be improved by analyzing the emotions of employees and evaluating the reliability of the report content.

[0084] The suggestion unit can use the emotion estimation function to analyze employees' emotions and make suggestions for improving work. For example, it can identify stressful work tasks based on the emotion data and propose improvements. The suggestion unit can also make suggestions for improving motivation based on the emotion data. The suggestion unit can also analyze emotional fluctuations and dynamically evaluate areas for improvement in work. This allows for the efficiency of work to be improved by analyzing employees' emotions and proposing improvements to work.

[0085] The analysis unit can analyze employee work data and predict the effects of automating work. For example, it can simulate the effects of automation based on past work data. The analysis unit can also analyze work progress data and quantitatively evaluate the effects of automation. The analysis unit can also compare and evaluate the effects of different automation methods and propose the optimal automation method. This makes it possible to propose effective automation by analyzing employee work data and predicting the effects of automation.

[0086] The identification department can analyze employee work data and evaluate work risks. For example, it can analyze work progress data and identify high-risk work. The identification department can also analyze work dependencies and evaluate the impact of risks. The identification department can also make proposals for risk mitigation based on the risk data. In this way, risk management is strengthened by analyzing employee work data and evaluating work risks.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The analysis unit analyzes the daily work reports or task content. For example, the analysis unit uses natural language processing technology to extract the work content, progress, and problems. The analysis unit can also analyze the data using machine learning algorithms to identify work patterns. Furthermore, data mining technology can be used to detect work trends and anomalies. Step 2: The Identification Department identifies tasks that can be automated or streamlined based on the results of the analysis by the Analysis Department. For example, the Identification Department identifies routine data entry tasks and repetitive report creation as targets for automation. The Department can also recommend the most appropriate automation tools, taking into account the complexity and frequency of the tasks. Furthermore, the Department can set priorities for tasks and notify employees in real time. Step 3: The reporting department reports the work identified by the identification department. For example, when an employee reports work, the reporting department can use the generation AI to automatically complete the input or provide a template. The reporting department can also automatically organize the report content and provide it to managers in an easy-to-understand format. Furthermore, the reporting content from different departments and projects can be integrated, and the overall work status can be managed centrally. Step 4: The proposal department makes effective proposals for business automation based on the reports from the reporting department. For example, the proposal department suggests that part of the customer support process be delegated to a chatbot, thereby reducing the burden on employees and improving efficiency. The proposal department can also simulate the effects of automation and quantitatively evaluate the predicted results. Furthermore, it can collect feedback after the automation is implemented and continuously improve its proposals.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the daily business report or task content; an identification unit that identifies a task that can be automated or made more efficient based on the results of the analysis by the analysis unit; a reporting unit that reports the business identified by the identifying unit; a proposal unit that proposes effective business automation based on the content reported by the reporting unit. A system characterized by:

2. The analysis unit Conduct multimodal data analysis by including audio input or video logs in the data The system of claim 1 .

3. The identification unit Considering the complexity and frequency of the above tasks, we propose the most suitable automation tools The system of claim 1 .

4. The reporting unit Streamline reporting processes by learning employee input patterns and providing optimal completion The system of claim 1 .

5. The proposal unit Simulate the effects of said automation and quantitatively evaluate the predicted outcomes The system of claim 1 .

6. The analysis unit Using the emotion estimation function, we estimate emotions from employees' daily reports and analyze fluctuations in stress and motivation. The system of claim 1 .

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