system

Abstract

The system of the embodiment aims to enable small and medium-sized enterprises to take appropriate steps to promote DX, monitor their progress, and receive appropriate advice. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, and an advice unit. The reception unit inputs the company's issues. The proposal unit analyzes the issues input by the reception unit and proposes steps for promoting DX. The monitoring unit monitors progress based on the action plan proposed by the proposal unit. The advice unit provides advice based on the progress monitored by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, small and medium-sized enterprises had the problem of not knowing where to start in promoting DX, making it difficult to monitor progress or receive appropriate advice.

[0005] The system of the embodiment aims to enable small and medium-sized enterprises to take appropriate steps to promote DX, monitor their progress, and receive appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a monitoring unit, and an advice unit. The reception unit inputs the company's issues. The proposal unit analyzes the issues input by the reception unit and proposes steps for promoting DX. The monitoring unit monitors progress based on the action plan proposed by the proposal unit. The advice unit provides advice based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment enables small and medium-sized enterprises to take appropriate steps to promote DX, monitor their progress, and receive appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The DX promotion support system according to an embodiment of the present invention is an online consulting service that uses generative AI to support small and medium-sized enterprises (SMEs) in their DX promotion efforts. This DX promotion support system begins when a company accesses the generative AI online and inputs its challenges. The generative AI analyzes the input challenges and proposes optimal DX promotion steps based on industry benchmark data and past success stories. These proposals include specific action plans and resource allocations. The company then begins DX promotion based on the action plan proposed by the generative AI. The generative AI monitors progress through periodic report generation and a real-time dashboard, providing advice as needed. This allows companies to promote DX efficiently. Furthermore, if a company is interested, the generative AI provides contact information and allows the company to upgrade to a paid consulting service. This paid service provides expert consultants to support the company's DX promotion and provide more advanced advice and support. This system allows SMEs to easily start and efficiently promote DX online. Furthermore, through free consulting services provided by the generative AI, companies can clarify their challenges and obtain specific action plans. Interested companies can upgrade to a paid consulting service for even more advanced support. This enables the DX promotion support system to efficiently analyze a company's challenges, propose optimal steps for promoting DX, monitor progress, and provide advice.

[0029] A DX promotion support system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, and an advice unit. Companies access the reception unit online and input their challenges. The challenges input by companies include, but are not limited to, technical challenges, business challenges, and operational challenges. The reception unit allows companies to input their challenges, for example, through a web portal or a mobile app. The proposal unit uses a generation AI to analyze the challenges input by the reception unit and propose DX promotion steps. The proposal unit proposes optimal DX promotion steps based on, for example, industry benchmark data and past success stories. The generation AI proposes an optimal action plan for the company by, for example, collecting and analyzing industry standards and competitor data. The monitoring unit monitors progress based on the action plan proposed by the proposal unit. The monitoring unit, for example, generates periodic reports and provides a real-time dashboard. The generation AI, for example, analyzes progress in real time and displays it on a dashboard, making it easier for companies to understand their current situation. The advice unit provides advice based on the progress monitored by the monitoring unit. The advice unit provides necessary advice based on the progress, for example. The generation AI, for example, analyzes the progress and proposes the next action to be taken. As a result, the DX promotion support system according to the embodiment can efficiently analyze the issues a company faces, propose optimal steps for promoting DX, monitor the progress, and provide advice.

[0030] The Proposal Department can propose steps for promoting DX based on industry benchmark data and past success stories. For example, the Proposal Department collects and analyzes industry benchmark data. For example, the Proposal Department collects and analyzes industry standards and competitor data to propose the optimal action plan for a company. The Proposal Department can also propose steps for promoting DX based on past success stories. For example, the Proposal Department analyzes the cases of companies that have been successful in the past and extracts the factors that led to their success. This allows the Proposal Department to propose the optimal steps for promoting DX for a company. By proposing the optimal steps for promoting DX based on industry benchmark data and past success stories, more effective DX promotion becomes possible.

[0031] The monitoring unit can generate reports and provide a real-time dashboard. The monitoring unit, for example, generates periodic reports. For example, the monitoring unit generates a report on the progress status every week or every month and provides it to the company. The monitoring unit can also provide a real-time dashboard. For example, the monitoring unit provides a dashboard that displays the progress status in real time, making it easier for the company to grasp the current situation. In this way, by generating periodic reports and providing a real-time dashboard, the progress status can be effectively monitored.

[0032] The advice unit can provide advice based on the progress status. For example, the advice unit analyzes the progress status and suggests the next action to be taken. For example, the advice unit analyzes the progress status in real time and suggests the next action to be taken. The advice unit can also provide necessary advice based on the progress status. For example, the advice unit analyzes the progress status and suggests the next action to be taken. In this way, by providing necessary advice based on the progress status, it is possible to effectively support the promotion of digital transformation in companies.

[0033] The reception unit allows companies to access the system online and enter their company's issues. The reception unit allows companies to enter their issues through, for example, a web portal or a mobile app. For example, companies can access the web portal and enter their company's issues. Companies can also enter their issues using a mobile app. This allows companies to easily start promoting digital transformation by accessing the system online and entering their company's issues.

[0034] The proposal department can propose action plans and resource allocations. For example, the proposal department can propose specific action plans. For example, the proposal department can propose specific tasks that a company should carry out. The proposal department can also propose resource allocations. For example, the proposal department can propose resources such as human resources, budgets, and equipment that a company needs. In this way, by proposing specific action plans and resource allocations, the proposal department can provide specific support for a company's digital transformation promotion.

[0035] The reception unit can analyze the company's past assignment input history and select an input method. For example, the reception unit can prioritize and suggest input methods (text, voice, etc.) that the company has frequently used in the past. For example, the reception unit can analyze the company's past input history and select the most efficient input method. The reception unit can also analyze the company's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception unit can provide the auto-completion function based on the company's past input history. This allows the company's past assignment input history to be analyzed to select the optimal input method and enable efficient assignment input.

[0036] The reception unit can filter tasks based on the company's current business situation and areas of interest when inputting tasks. The reception unit, for example, takes into account the company's current project situation and displays only related tasks as input candidates. For example, the reception unit grasps the company's current project situation in real time and prioritizes displaying related tasks. The reception unit can also prioritize displaying highly relevant tasks as input candidates based on the company's areas of interest. For example, the reception unit analyzes the company's areas of interest and displays highly relevant tasks. The reception unit can also grasp the company's business situation in real time and prompt the user to input tasks at an appropriate time. For example, the reception unit analyzes the company's business situation in real time and prompts the user to input tasks at an optimal time. In this way, highly relevant tasks can be efficiently input by filtering based on the company's current business situation and areas of interest.

[0037] When inputting tasks, the reception unit can prioritize inputting tasks taking into account the geographical location information of the company. The reception unit, for example, prioritizes input of region-specific tasks based on the location of the company. For example, the reception unit analyzes the location of the company and displays region-specific tasks. The reception unit can also prioritize input of related tasks taking into account the geographical market range of the company. For example, the reception unit analyzes the geographical market range of the company and displays related tasks. The reception unit can also prioritize input of tasks that take into account the competitive situation in the region based on the geographical location information of the company. For example, the reception unit analyzes the competitive situation in the region and displays related tasks. This makes it possible to address region-specific tasks by prioritized input of highly relevant tasks taking into account the geographical location information of the company.

[0038] When inputting a task, the reception unit can analyze the company's social media activity and input related tasks. The reception unit, for example, analyzes trends on the company's social media and allows the user to input related tasks. For example, the reception unit analyzes trends on the company's social media and displays related tasks. The reception unit can also analyze the interests of the company's followers on social media and allow the user to input related tasks. For example, the reception unit analyzes the interests of the company's followers and displays related tasks. The reception unit can also analyze past posts on the company's social media and allow the user to input related tasks. For example, the reception unit analyzes past posts on the company and displays related tasks. This allows the company's social media activity to be analyzed and related tasks to be input efficiently.

[0039] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the issue. For example, for issues of high importance, the suggestion unit causes the generation AI to propose a detailed action plan. For example, the generation AI analyzes the importance of the issue and proposes a detailed action plan. In addition, the suggestion unit can cause the generation AI to make a simplified proposal for issues of low importance. For example, the generation AI analyzes the importance of the issue and makes a simplified proposal. In addition, the suggestion unit can cause the generation AI to make a proposal with an appropriate level of detail for issues of medium importance. For example, the generation AI analyzes the importance of the issue and makes a proposal with an appropriate level of detail. In this way, by adjusting the level of detail of the proposal based on the importance of the issue, more effective proposals can be made.

[0040] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the problem. For example, in the proposal unit, for a technical problem, the generation AI applies a specialized technical algorithm. For example, the generation AI analyzes the technical problem and applies a specialized technical algorithm. Furthermore, in the proposal unit, for a management problem, the generation AI can apply a specialized management algorithm. For example, the generation AI analyzes the management problem and applies a specialized management algorithm. Furthermore, in the proposal unit, the generation AI can apply a specialized marketing algorithm to a marketing problem. For example, the generation AI analyzes the marketing problem and applies a specialized marketing algorithm. This makes it possible to make more appropriate proposals by applying different proposal algorithms depending on the category of the problem.

[0041] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date of the assignment. For example, the suggestion unit has the generation AI give priority to proposals for urgent assignments. For example, the generation AI analyzes the submission date of the assignment and gives priority to proposing urgent assignments. The suggestion unit can also have the generation AI give priority to proposals for assignments with an approaching submission deadline. For example, the generation AI analyzes the submission deadline of the assignment and gives priority to proposing assignments with an upcoming submission deadline. The suggestion unit can also have the generation AI postpone proposals for assignments with a distant submission deadline. For example, the generation AI analyzes the submission deadline of the assignment and gives priority to proposing assignments with a distant submission deadline. In this way, more effective suggestions can be made by determining the priority of proposals based on the submission date of the assignment.

[0042] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the tasks. For example, the suggestion unit has the generation AI make a priority proposal for directly related tasks. For example, the generation AI analyzes the relevance of tasks and prioritizes proposing directly related tasks. The suggestion unit can also have the generation AI make a next proposal for indirectly related tasks. For example, the generation AI analyzes the relevance of tasks and proposes indirectly related tasks next. The suggestion unit can also have the generation AI make a last proposal for less related tasks. For example, the generation AI analyzes the relevance of tasks and proposes less related tasks last. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of tasks.

[0043] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between issues during monitoring. For example, the monitoring unit groups related issues and the generation AI monitors them all at once. For example, the generation AI analyzes the interrelationships between issues and groups related issues for monitoring. The monitoring unit can also analyze the interrelationships between issues and have the generation AI prioritize monitoring issues with high impact. For example, the generation AI analyzes the interrelationships between issues and prioritizes monitoring issues with high impact. The monitoring unit can also take into account the interrelationships between issues and have the generation AI simultaneously monitor issues that affect each other. For example, the generation AI analyzes the interrelationships between issues and simultaneously monitors issues that affect each other. In this way, the accuracy of monitoring is improved by taking into account the interrelationships between issues.

[0044] The monitoring unit can conduct monitoring while taking into account the attribute information of the company. In the monitoring unit, for example, the generation AI sets appropriate monitoring standards based on the company's industry. For example, the generation AI analyzes the company's industry and sets appropriate monitoring standards. The monitoring unit can also adjust the frequency of monitoring based on the size of the company. For example, the generation AI analyzes the size of the company and adjusts the frequency of monitoring. The monitoring unit can also adjust the level of detail of monitoring based on the company's growth stage. For example, the generation AI analyzes the company's growth stage and adjusts the level of detail of monitoring. This enables more appropriate monitoring by taking into account the company's attribute information.

[0045] The monitoring unit can conduct monitoring taking into account the geographical distribution of companies. For example, the monitoring unit has the generation AI monitor region-specific issues based on the location of the company. For example, the generation AI analyzes the location of the company and monitors region-specific issues. The monitoring unit can also have the generation AI monitor related issues taking into account the geographical market range of the company. For example, the generation AI analyzes the geographical market range of the company and monitors related issues. The monitoring unit can also have the generation AI set monitoring standards for each region based on the geographical distribution of companies. For example, the generation AI analyzes the geographical distribution of companies and sets monitoring standards for each region. This makes it possible to conduct monitoring that addresses region-specific issues by taking into account the geographical distribution of companies.

[0046] During monitoring, the monitoring unit can improve the accuracy of the monitoring by referring to related literature. For example, the monitoring unit refers to related academic papers, and the generation AI sets monitoring standards. For example, the generation AI analyzes related academic papers and sets monitoring standards. The monitoring unit can also refer to related industry reports, and the generation AI can improve the accuracy of the monitoring. For example, the generation AI analyzes related industry reports and improves the accuracy of the monitoring. The monitoring unit can also refer to related patent documents, and the generation AI can adjust the level of detail of the monitoring. For example, the generation AI analyzes related patent documents and adjust the level of detail of the monitoring. In this way, the accuracy of the monitoring is improved by referring to related literature.

[0047] When giving advice, the advice unit can analyze the company's past progress and provide the advice. In the advice unit, for example, the generation AI proposes the optimal next step based on the company's past progress data. For example, the generation AI analyzes the company's past progress data and proposes the optimal next step. The advice unit can also refer to the company's past success stories and have the generation AI provide similar advice. For example, the generation AI analyzes the company's past success stories and provide similar advice. The advice unit can also analyze the company's past failure stories and have the generation AI propose workarounds. For example, the generation AI analyzes the company's past failure stories and proposes workarounds. This makes it possible to provide optimal advice by analyzing the company's past progress.

[0048] When providing advice, the advice unit can customize the means of advice based on the company's current business situation. The advice unit, for example, takes into account the company's current resource situation and allows the generation AI to provide executable advice. For example, the generation AI analyzes the company's resource situation and provides executable advice. The advice unit can also allow the generation AI to provide appropriate advice based on the company's current project situation. For example, the generation AI analyzes the company's project situation and provides appropriate advice. The advice unit can also take into account the company's current market situation and allow the generation AI to provide optimal advice. For example, the generation AI analyzes the company's market situation and provides optimal advice. This makes it possible to customize the means of advice based on the company's current business situation, thereby enabling more appropriate advice.

[0049] When providing advice, the advice unit can provide advice taking into account the geographical location information of the company. In the advice unit, for example, the generation AI provides region-specific advice based on the location of the company. For example, the generation AI analyzes the location of the company and provides region-specific advice. The advice unit can also provide relevant advice taking into account the geographical market range of the company. For example, the generation AI analyzes the geographical market range of the company and provides relevant advice. The advice unit can also provide region-specific advice based on the geographical location information of the company. For example, the generation AI analyzes the geographical location information of the company and provides region-specific advice. This makes it possible to provide region-specific advice by taking into account the geographical location information of the company.

[0050] When providing advice, the advice unit can analyze the company's social media activities and suggest ways to provide the advice. The advice unit, for example, analyzes trends on the company's social media, and the generation AI provides related advice. For example, the generation AI analyzes trends on the company's social media and provides related advice. The advice unit can also analyze the interests of the company's followers on social media, and the generation AI provides related advice. For example, the generation AI analyzes the interests of the company's followers and provides related advice. The advice unit can also analyze past posts on the company's social media, and the generation AI provides related advice. For example, the generation AI analyzes past posts on the company and provides related advice. This makes it possible to provide more appropriate advice by analyzing the company's social media activities.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception department can analyze a company's past DX promotion history and suggest the optimal timing for entering tasks. For example, the reception department can analyze cases of success and failure when a company has promoted DX in the past and encourage task entry at the optimal timing. The reception department can also adjust the frequency of task entry based on the company's past DX promotion history. For example, if a company has frequently entered tasks in the past, reducing the frequency will support efficient DX promotion. The reception department can also customize the method of task entry based on the company's past DX promotion history. For example, if a company has preferred text input in the past, the reception department will suggest text input as a priority. This makes it possible to enter tasks more effectively by taking into account the company's past DX promotion history.

[0053] The Proposal Department can analyze a company's current market situation in real time and propose optimal steps for promoting DX. For example, the Proposal Department can analyze a company's market share and competitor trends in real time and propose an optimal action plan. The Proposal Department can also propose optimal resource allocation based on the company's current market situation. For example, it can propose the human resources and budget needed for a company to expand its market share. The Proposal Department can also determine priorities for promoting DX based on the company's market situation. For example, it can prioritize and propose the most effective steps for a company to expand its market share. This enables more effective DX promotion by taking into account the company's current market situation.

[0054] The monitoring department can collect performance data from a company's employees and evaluate the progress of DX promotion. For example, the monitoring department can analyze employee work efficiency and productivity in real time to evaluate the progress of DX promotion. The monitoring department can also propose improvements to DX promotion based on employee performance data. For example, if employee work efficiency is declining, the monitoring department can identify the cause and propose improvement measures. The monitoring department can also extract success factors for DX promotion based on employee performance data. For example, they can analyze how high employee performance contributes to DX promotion and apply those success factors to other projects. In this way, utilizing a company's employee performance data can enable more effective DX promotion.

[0055] The Advice Department can collect customer feedback from companies and propose improvements for promoting DX. For example, the Advice Department can analyze feedback from customers and identify areas for improvement in promoting DX. The Advice Department can also determine priorities for promoting DX based on customer feedback. For example, it can prioritize solving issues that receive a lot of feedback from customers. The Advice Department can also propose specific action plans for promoting DX based on customer feedback. For example, it can propose specific improvement measures that reflect customer feedback. In this way, utilizing customer feedback from companies makes it possible to promote DX more effectively.

[0056] The reception department can analyze the skill sets of the company's employees and suggest the optimal method for entering tasks. For example, the reception department can analyze the employee's skill set and suggest the optimal input method (text, voice, video, etc.). The reception department can also customize the procedure for entering tasks based on the employee's skill set. For example, it can simplify the input procedure based on the employee's specialty skills. The reception department can also provide training for entering tasks based on the employee's skill set. For example, it can provide training for employees to learn new input methods. This makes it possible to enter tasks more effectively by taking into account the skill sets of the company's employees.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception department allows companies to enter their issues online. Issues that companies can enter include technical issues, business issues, operational issues, etc. The reception department allows companies to enter their issues through a web portal or mobile app. Step 2: The proposal department uses the generation AI to analyze the issues entered by the reception department and propose steps for promoting DX. The proposal department proposes optimal steps for promoting DX based on industry benchmark data and past success stories. The generation AI collects and analyzes data on industry standards and competitors to propose the optimal action plan for the company. Step 3: The Monitoring Department monitors progress based on the action plan proposed by the Proposal Department. The Monitoring Department generates regular reports and provides a real-time dashboard. The Generation AI analyzes progress in real time and displays it on the dashboard, making it easier for companies to understand the current situation. Step 4: The Advice Unit provides advice based on the progress monitored by the Monitoring Unit. The Advice Unit provides necessary advice based on the progress. The Generative AI analyzes the progress and suggests the next action to be taken.

[0059] (Example 2) The DX promotion support system according to an embodiment of the present invention is an online consulting service that uses generative AI to support small and medium-sized enterprises (SMEs) in their DX promotion efforts. This DX promotion support system begins when a company accesses the generative AI online and inputs its challenges. The generative AI analyzes the input challenges and proposes optimal DX promotion steps based on industry benchmark data and past success stories. These proposals include specific action plans and resource allocations. The company then begins DX promotion based on the action plan proposed by the generative AI. The generative AI monitors progress through periodic report generation and a real-time dashboard, providing advice as needed. This allows companies to promote DX efficiently. Furthermore, if a company is interested, the generative AI provides contact information and allows the company to upgrade to a paid consulting service. This paid service provides expert consultants to support the company's DX promotion and provide more advanced advice and support. This system allows SMEs to easily start and efficiently promote DX online. Furthermore, through free consulting services provided by the generative AI, companies can clarify their challenges and obtain specific action plans. Interested companies can upgrade to a paid consulting service for even more advanced support. This enables the DX promotion support system to efficiently analyze a company's challenges, propose optimal steps for promoting DX, monitor progress, and provide advice.

[0060] A DX promotion support system according to an embodiment includes a reception unit, a proposal unit, a monitoring unit, and an advice unit. Companies access the reception unit online and input their challenges. The challenges input by companies include, but are not limited to, technical challenges, business challenges, and operational challenges. The reception unit allows companies to input their challenges, for example, through a web portal or a mobile app. The proposal unit uses a generation AI to analyze the challenges input by the reception unit and propose DX promotion steps. The proposal unit proposes optimal DX promotion steps based on, for example, industry benchmark data and past success stories. The generation AI proposes an optimal action plan for the company by, for example, collecting and analyzing industry standards and competitor data. The monitoring unit monitors progress based on the action plan proposed by the proposal unit. The monitoring unit, for example, generates periodic reports and provides a real-time dashboard. The generation AI, for example, analyzes progress in real time and displays it on a dashboard, making it easier for companies to understand their current situation. The advice unit provides advice based on the progress monitored by the monitoring unit. The advice unit provides necessary advice based on the progress, for example. The generation AI, for example, analyzes the progress and proposes the next action to be taken. As a result, the DX promotion support system according to the embodiment can efficiently analyze the issues a company faces, propose optimal steps for promoting DX, monitor the progress, and provide advice.

[0061] The Proposal Department can propose steps for promoting DX based on industry benchmark data and past success stories. For example, the Proposal Department collects and analyzes industry benchmark data. For example, the Proposal Department collects and analyzes industry standards and competitor data to propose the optimal action plan for a company. The Proposal Department can also propose steps for promoting DX based on past success stories. For example, the Proposal Department analyzes the cases of companies that have been successful in the past and extracts the factors that led to their success. This allows the Proposal Department to propose the optimal steps for promoting DX for a company. By proposing the optimal steps for promoting DX based on industry benchmark data and past success stories, more effective DX promotion becomes possible.

[0062] The monitoring unit can generate reports and provide a real-time dashboard. The monitoring unit, for example, generates periodic reports. For example, the monitoring unit generates a report on the progress status every week or every month and provides it to the company. The monitoring unit can also provide a real-time dashboard. For example, the monitoring unit provides a dashboard that displays the progress status in real time, making it easier for the company to grasp the current situation. In this way, by generating periodic reports and providing a real-time dashboard, the progress status can be effectively monitored.

[0063] The advice unit can provide advice based on the progress status. For example, the advice unit analyzes the progress status and suggests the next action to be taken. For example, the advice unit analyzes the progress status in real time and suggests the next action to be taken. The advice unit can also provide necessary advice based on the progress status. For example, the advice unit analyzes the progress status and suggests the next action to be taken. In this way, by providing necessary advice based on the progress status, it is possible to effectively support the promotion of digital transformation in companies.

[0064] The reception unit allows companies to access the system online and enter their company's issues. The reception unit allows companies to enter their issues through, for example, a web portal or a mobile app. For example, companies can access the web portal and enter their company's issues. Companies can also enter their issues using a mobile app. This allows companies to easily start promoting digital transformation by accessing the system online and entering their company's issues.

[0065] The proposal department can propose action plans and resource allocations. For example, the proposal department can propose specific action plans. For example, the proposal department can propose specific tasks that a company should carry out. The proposal department can also propose resource allocations. For example, the proposal department can propose resources such as human resources, budgets, and equipment that a company needs. In this way, by proposing specific action plans and resource allocations, the proposal department can provide specific support for a company's digital transformation promotion.

[0066] The reception unit can estimate the user's emotions and adjust the timing of task input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the generation AI to input a task at a time when the user can relax. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. The reception unit can also prompt the generation AI to input a task without missing the opportunity if the user is concentrating. For example, the generation AI analyzes the user's level of concentration and prompts the user to input a task at the optimal timing. The reception unit can also prompt the generation AI to input a task after a break if the user is tired. For example, the generation AI analyzes the user's level of fatigue and prompts the user to input a task at an appropriate timing. In this way, the timing of task input can be adjusted based on the user's emotions, allowing the user to input tasks at a more appropriate timing.

[0067] The reception unit can analyze the company's past assignment input history and select an input method. For example, the reception unit can prioritize and suggest input methods (text, voice, etc.) that the company has frequently used in the past. For example, the reception unit can analyze the company's past input history and select the most efficient input method. The reception unit can also analyze the company's past input history and provide an auto-completion function to reduce the effort required for input. For example, the reception unit can provide the auto-completion function based on the company's past input history. This allows the company's past assignment input history to be analyzed to select the optimal input method and enable efficient assignment input.

[0068] The reception unit can filter tasks based on the company's current business situation and areas of interest when inputting tasks. The reception unit, for example, takes into account the company's current project situation and displays only related tasks as input candidates. For example, the reception unit grasps the company's current project situation in real time and prioritizes displaying related tasks. The reception unit can also prioritize displaying highly relevant tasks as input candidates based on the company's areas of interest. For example, the reception unit analyzes the company's areas of interest and displays highly relevant tasks. The reception unit can also grasp the company's business situation in real time and prompt the user to input tasks at an appropriate time. For example, the reception unit analyzes the company's business situation in real time and prompts the user to input tasks at an optimal time. In this way, highly relevant tasks can be efficiently input by filtering based on the company's current business situation and areas of interest.

[0069] The reception unit can estimate the user's emotions and determine the priority of tasks to be input based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit causes the generation AI to prioritize input of easy tasks. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. The reception unit can also cause the generation AI to prioritize input of important tasks when the user is concentrating. For example, the generation AI analyzes the user's level of concentration and causes the generation AI to input important tasks at the optimal timing. The reception unit can also cause the generation AI to prioritize input of tasks of medium difficulty when the user is relaxed. For example, the generation AI analyzes the user's level of relaxation and causes the generation AI to input tasks of medium difficulty at the appropriate timing. In this way, by determining the priority of tasks to be input based on the user's emotions, more appropriate tasks can be input preferentially.

[0070] When inputting tasks, the reception unit can prioritize inputting tasks taking into account the geographical location information of the company. The reception unit, for example, prioritizes input of region-specific tasks based on the location of the company. For example, the reception unit analyzes the location of the company and displays region-specific tasks. The reception unit can also prioritize input of related tasks taking into account the geographical market range of the company. For example, the reception unit analyzes the geographical market range of the company and displays related tasks. The reception unit can also prioritize input of tasks that take into account the competitive situation in the region based on the geographical location information of the company. For example, the reception unit analyzes the competitive situation in the region and displays related tasks. This makes it possible to address region-specific tasks by prioritized input of highly relevant tasks taking into account the geographical location information of the company.

[0071] When inputting a task, the reception unit can analyze the company's social media activity and input related tasks. The reception unit, for example, analyzes trends on the company's social media and allows the user to input related tasks. For example, the reception unit analyzes trends on the company's social media and displays related tasks. The reception unit can also analyze the interests of the company's followers on social media and allow the user to input related tasks. For example, the reception unit analyzes the interests of the company's followers and displays related tasks. The reception unit can also analyze past posts on the company's social media and allow the user to input related tasks. For example, the reception unit analyzes past posts on the company and displays related tasks. This allows the company's social media activity to be analyzed and related tasks to be input efficiently.

[0072] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit causes the generation AI to make simple and easy-to-understand suggestions. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. Furthermore, if the user is relaxed, the suggestion unit can cause the generation AI to make detailed suggestions. For example, the generation AI analyzes the user's level of relaxation and makes detailed suggestions. Furthermore, if the user is concentrating, the suggestion unit can cause the generation AI to make suggestions that include technical terms. For example, the generation AI analyzes the user's level of concentration and makes suggestions that include technical terms. This enables more appropriate suggestions to be made by adjusting the way suggestions are expressed based on the user's emotions.

[0073] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the issue. For example, for issues of high importance, the suggestion unit causes the generation AI to propose a detailed action plan. For example, the generation AI analyzes the importance of the issue and proposes a detailed action plan. In addition, the suggestion unit can cause the generation AI to make a simplified proposal for issues of low importance. For example, the generation AI analyzes the importance of the issue and makes a simplified proposal. In addition, the suggestion unit can cause the generation AI to make a proposal with an appropriate level of detail for issues of medium importance. For example, the generation AI analyzes the importance of the issue and makes a proposal with an appropriate level of detail. In this way, by adjusting the level of detail of the proposal based on the importance of the issue, more effective proposals can be made.

[0074] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the problem. For example, in the proposal unit, for a technical problem, the generation AI applies a specialized technical algorithm. For example, the generation AI analyzes the technical problem and applies a specialized technical algorithm. Furthermore, in the proposal unit, for a management problem, the generation AI can apply a specialized management algorithm. For example, the generation AI analyzes the management problem and applies a specialized management algorithm. Furthermore, in the proposal unit, the generation AI can apply a specialized marketing algorithm to a marketing problem. For example, the generation AI analyzes the marketing problem and applies a specialized marketing algorithm. This makes it possible to make more appropriate proposals by applying different proposal algorithms depending on the category of the problem.

[0075] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit causes the generation AI to make short, to-the-point suggestions. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is in a hurry. Furthermore, if the user is relaxed, the suggestion unit can cause the generation AI to make detailed suggestions. For example, the generation AI analyzes the user's level of relaxation and makes detailed suggestions. Furthermore, if the user is concentrating, the suggestion unit can cause the generation AI to make suggestions of medium length. For example, the generation AI analyzes the user's level of concentration and makes suggestions of medium length. This enables more appropriate suggestions to be made by adjusting the length of the suggestions based on the user's emotions.

[0076] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date of the assignment. For example, the suggestion unit has the generation AI give priority to proposals for urgent assignments. For example, the generation AI analyzes the submission date of the assignment and gives priority to proposing urgent assignments. The suggestion unit can also have the generation AI give priority to proposals for assignments with an approaching submission deadline. For example, the generation AI analyzes the submission deadline of the assignment and gives priority to proposing assignments with an upcoming submission deadline. The suggestion unit can also have the generation AI postpone proposals for assignments with a distant submission deadline. For example, the generation AI analyzes the submission deadline of the assignment and gives priority to proposing assignments with a distant submission deadline. In this way, more effective suggestions can be made by determining the priority of proposals based on the submission date of the assignment.

[0077] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the tasks. For example, the suggestion unit has the generation AI make a priority proposal for directly related tasks. For example, the generation AI analyzes the relevance of tasks and prioritizes proposing directly related tasks. The suggestion unit can also have the generation AI make a next proposal for indirectly related tasks. For example, the generation AI analyzes the relevance of tasks and proposes indirectly related tasks next. The suggestion unit can also have the generation AI make a last proposal for less related tasks. For example, the generation AI analyzes the relevance of tasks and proposes less related tasks last. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of tasks.

[0078] The monitoring unit can estimate the user's emotions and adjust the monitoring standards based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit causes the generation AI to perform monitoring using simple standards. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. The monitoring unit can also cause the generation AI to perform monitoring using detailed standards if the user is relaxed. For example, the generation AI analyzes the user's level of relaxation and performs monitoring using detailed standards. The monitoring unit can also cause the generation AI to perform monitoring using specialized standards if the user is concentrating. For example, the generation AI analyzes the user's level of concentration and performs monitoring using specialized standards. This allows for more appropriate monitoring by adjusting the monitoring standards based on the user's emotions.

[0079] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between issues during monitoring. For example, the monitoring unit groups related issues and the generation AI monitors them all at once. For example, the generation AI analyzes the interrelationships between issues and groups related issues for monitoring. The monitoring unit can also analyze the interrelationships between issues and have the generation AI prioritize monitoring issues with high impact. For example, the generation AI analyzes the interrelationships between issues and prioritizes monitoring issues with high impact. The monitoring unit can also take into account the interrelationships between issues and have the generation AI simultaneously monitor issues that affect each other. For example, the generation AI analyzes the interrelationships between issues and simultaneously monitors issues that affect each other. In this way, the accuracy of monitoring is improved by taking into account the interrelationships between issues.

[0080] The monitoring unit can conduct monitoring while taking into account the attribute information of the company. In the monitoring unit, for example, the generation AI sets appropriate monitoring standards based on the company's industry. For example, the generation AI analyzes the company's industry and sets appropriate monitoring standards. The monitoring unit can also adjust the frequency of monitoring based on the size of the company. For example, the generation AI analyzes the size of the company and adjusts the frequency of monitoring. The monitoring unit can also adjust the level of detail of monitoring based on the company's growth stage. For example, the generation AI analyzes the company's growth stage and adjusts the level of detail of monitoring. This enables more appropriate monitoring by taking into account the company's attribute information.

[0081] The monitoring unit can estimate the user's emotions and adjust the order in which the monitoring results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit causes the generation AI to prioritize displaying important results. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. The monitoring unit can also cause the generation AI to display detailed results in an orderly manner if the user is relaxed. For example, the generation AI analyzes the user's level of relaxation and displays detailed results in an orderly manner. The monitoring unit can also cause the generation AI to prioritize displaying specialized results if the user is concentrating. For example, the generation AI analyzes the user's level of concentration and displays specialized results in an orderly manner. This allows for more appropriate result display by adjusting the order in which the monitoring results are displayed based on the user's emotions.

[0082] The monitoring unit can conduct monitoring taking into account the geographical distribution of companies. For example, the monitoring unit has the generation AI monitor region-specific issues based on the location of the company. For example, the generation AI analyzes the location of the company and monitors region-specific issues. The monitoring unit can also have the generation AI monitor related issues taking into account the geographical market range of the company. For example, the generation AI analyzes the geographical market range of the company and monitors related issues. The monitoring unit can also have the generation AI set monitoring standards for each region based on the geographical distribution of companies. For example, the generation AI analyzes the geographical distribution of companies and sets monitoring standards for each region. This makes it possible to conduct monitoring that addresses region-specific issues by taking into account the geographical distribution of companies.

[0083] During monitoring, the monitoring unit can improve the accuracy of the monitoring by referring to related literature. For example, the monitoring unit refers to related academic papers, and the generation AI sets monitoring standards. For example, the generation AI analyzes related academic papers and sets monitoring standards. The monitoring unit can also refer to related industry reports, and the generation AI can improve the accuracy of the monitoring. For example, the generation AI analyzes related industry reports and improves the accuracy of the monitoring. The monitoring unit can also refer to related patent documents, and the generation AI can adjust the level of detail of the monitoring. For example, the generation AI analyzes related patent documents and adjust the level of detail of the monitoring. In this way, the accuracy of the monitoring is improved by referring to related literature.

[0084] The advice unit can estimate the user's emotions and adjust the method of advice based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI of the advice unit provides simple and easy-to-understand advice. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. Furthermore, if the user is relaxed, the generation AI can provide detailed advice. For example, the generation AI analyzes the user's level of relaxation and provides detailed advice. Furthermore, if the user is concentrating, the advice unit can provide advice including technical terms. For example, the generation AI analyzes the user's level of concentration and provides advice including technical terms. This enables more appropriate advice to be provided by adjusting the method of advice based on the user's emotions.

[0085] When giving advice, the advice unit can analyze the company's past progress and provide the advice. In the advice unit, for example, the generation AI proposes the optimal next step based on the company's past progress data. For example, the generation AI analyzes the company's past progress data and proposes the optimal next step. The advice unit can also refer to the company's past success stories and have the generation AI provide similar advice. For example, the generation AI analyzes the company's past success stories and provide similar advice. The advice unit can also analyze the company's past failure stories and have the generation AI propose workarounds. For example, the generation AI analyzes the company's past failure stories and proposes workarounds. This makes it possible to provide optimal advice by analyzing the company's past progress.

[0086] When providing advice, the advice unit can customize the means of advice based on the company's current business situation. The advice unit, for example, takes into account the company's current resource situation and allows the generation AI to provide executable advice. For example, the generation AI analyzes the company's resource situation and provides executable advice. The advice unit can also allow the generation AI to provide appropriate advice based on the company's current project situation. For example, the generation AI analyzes the company's project situation and provides appropriate advice. The advice unit can also take into account the company's current market situation and allow the generation AI to provide optimal advice. For example, the generation AI analyzes the company's market situation and provides optimal advice. This makes it possible to customize the means of advice based on the company's current business situation, thereby enabling more appropriate advice.

[0087] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit causes the generation AI to prioritize providing simple advice. For example, the generation AI analyzes the user's facial expressions and voice data to determine whether the user is feeling stressed. Furthermore, if the user is relaxed, the advice unit can also cause the generation AI to prioritize providing detailed advice. For example, the generation AI analyzes the user's level of relaxation and prioritizes providing detailed advice. Furthermore, if the user is concentrating, the advice unit can also cause the generation AI to prioritize providing specialized advice. For example, the generation AI analyzes the user's level of concentration and prioritizes providing specialized advice. This enables more appropriate advice to be provided by determining the priority of advice based on the user's emotions.

[0088] When providing advice, the advice unit can provide advice taking into account the geographical location information of the company. In the advice unit, for example, the generation AI provides region-specific advice based on the location of the company. For example, the generation AI analyzes the location of the company and provides region-specific advice. The advice unit can also provide relevant advice taking into account the geographical market range of the company. For example, the generation AI analyzes the geographical market range of the company and provides relevant advice. The advice unit can also provide region-specific advice based on the geographical location information of the company. For example, the generation AI analyzes the geographical location information of the company and provides region-specific advice. This makes it possible to provide region-specific advice by taking into account the geographical location information of the company.

[0089] When providing advice, the advice unit can analyze the company's social media activities and suggest ways to provide the advice. The advice unit, for example, analyzes trends on the company's social media, and the generation AI provides related advice. For example, the generation AI analyzes trends on the company's social media and provides related advice. The advice unit can also analyze the interests of the company's followers on social media, and the generation AI provides related advice. For example, the generation AI analyzes the interests of the company's followers and provides related advice. The advice unit can also analyze past posts on the company's social media, and the generation AI provides related advice. For example, the generation AI analyzes past posts on the company and provides related advice. This makes it possible to provide more appropriate advice by analyzing the company's social media activities. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, proposal unit, monitoring unit, and advice unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and allows companies to input issues through a web portal or a mobile app. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the issues input by the reception unit using a generative AI and proposes steps for promoting digital transformation. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors progress based on the proposed action plan. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the monitored progress. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, proposal unit, monitoring unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and allows companies to input issues through a web portal or a mobile app. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the issues input by the reception unit using a generative AI and proposes steps for promoting digital transformation. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors progress based on the proposed action plan. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the monitored progress. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, proposal unit, monitoring unit, and advice unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and allows companies to input issues through a web portal or a mobile app. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the issues input by the reception unit using a generative AI and proposes steps for promoting digital transformation. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors progress based on the proposed action plan. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the monitored progress. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, proposal unit, monitoring unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and allows companies to input issues through a web portal or a mobile app. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the issues input by the reception unit using a generative AI and proposes steps for promoting digital transformation. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and monitors progress based on the proposed action plan. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the monitored progress.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception department can analyze a company's past DX promotion history and suggest the optimal timing for entering tasks. For example, the reception department can analyze cases of success and failure when a company has promoted DX in the past and encourage task entry at the optimal timing. The reception department can also adjust the frequency of task entry based on the company's past DX promotion history. For example, if a company has frequently entered tasks in the past, reducing the frequency will support efficient DX promotion. The reception department can also customize the method of task entry based on the company's past DX promotion history. For example, if a company has preferred text input in the past, the reception department will suggest text input as a priority. This makes it possible to enter tasks more effectively by taking into account the company's past DX promotion history.

[0092] The Proposal Department can analyze a company's current market situation in real time and propose optimal steps for promoting DX. For example, the Proposal Department can analyze a company's market share and competitor trends in real time and propose an optimal action plan. The Proposal Department can also propose optimal resource allocation based on the company's current market situation. For example, it can propose the human resources and budget needed for a company to expand its market share. The Proposal Department can also determine priorities for promoting DX based on the company's market situation. For example, it can prioritize and propose the most effective steps for a company to expand its market share. This enables more effective DX promotion by taking into account the company's current market situation.

[0093] The monitoring department can collect performance data from a company's employees and evaluate the progress of DX promotion. For example, the monitoring department can analyze employee work efficiency and productivity in real time to evaluate the progress of DX promotion. The monitoring department can also propose improvements to DX promotion based on employee performance data. For example, if employee work efficiency is declining, the monitoring department can identify the cause and propose improvement measures. The monitoring department can also extract success factors for DX promotion based on employee performance data. For example, they can analyze how high employee performance contributes to DX promotion and apply those success factors to other projects. In this way, utilizing a company's employee performance data can enable more effective DX promotion.

[0094] The Advice Department can collect customer feedback from companies and propose improvements for promoting DX. For example, the Advice Department can analyze feedback from customers and identify areas for improvement in promoting DX. The Advice Department can also determine priorities for promoting DX based on customer feedback. For example, it can prioritize solving issues that receive a lot of feedback from customers. The Advice Department can also propose specific action plans for promoting DX based on customer feedback. For example, it can propose specific improvement measures that reflect customer feedback. In this way, utilizing customer feedback from companies makes it possible to promote DX more effectively.

[0095] The reception department can analyze the skill sets of the company's employees and suggest the optimal method for entering tasks. For example, the reception department can analyze the employee's skill set and suggest the optimal input method (text, voice, video, etc.). The reception department can also customize the procedure for entering tasks based on the employee's skill set. For example, it can simplify the input procedure based on the employee's specialty skills. The reception department can also provide training for entering tasks based on the employee's skill set. For example, it can provide training for employees to learn new input methods. This makes it possible to enter tasks more effectively by taking into account the skill sets of the company's employees.

[0096] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make suggestions at a time when the generation AI can relax. Also, if the user is concentrating, the suggestion unit can make suggestions without missing that timing. Furthermore, if the user is tired, the suggestion unit can make suggestions after the generation AI has taken a break. In this way, by adjusting the timing of suggestions based on the user's emotions, suggestions can be made at more appropriate times.

[0097] The reception unit can estimate the user's emotions and customize the task input interface based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI of the reception unit can provide a simple and intuitive interface. If the user is relaxed, the generation AI of the reception unit can also provide detailed input options. Furthermore, if the user is concentrating, the generation AI of the reception unit can also provide specialized input options. In this way, by customizing the task input interface based on the user's emotions, a more appropriate input environment can be provided.

[0098] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring unit can cause the generation AI to reduce the monitoring frequency. Also, if the user is relaxed, the monitoring unit can cause the generation AI to increase the monitoring frequency. Furthermore, if the user is concentrating, the monitoring unit can cause the generation AI to moderately adjust the monitoring frequency. This allows for more appropriate monitoring by adjusting the monitoring frequency based on the user's emotions.

[0099] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice unit can provide the generation AI with simple, easy-to-follow advice. Also, if the user is relaxed, the advice unit can provide the generation AI with detailed, comprehensive advice. Furthermore, if the user is concentrating, the advice unit can provide the generation AI with specialized, advanced advice. This allows for more appropriate advice to be provided by adjusting the content of the advice based on the user's emotions.

[0100] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can cause the generation AI to prioritize simple suggestions. Also, if the user is relaxed, the suggestion unit can cause the generation AI to prioritize detailed suggestions. Furthermore, if the user is concentrating, the suggestion unit can cause the generation AI to prioritize specialized suggestions. This allows for more appropriate suggestions to be made by prioritizing suggestions based on the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception department allows companies to enter their issues online. Issues that companies can enter include technical issues, business issues, operational issues, etc. The reception department allows companies to enter their issues through a web portal or mobile app. Step 2: The proposal department uses the generation AI to analyze the issues entered by the reception department and propose steps for promoting DX. The proposal department proposes optimal steps for promoting DX based on industry benchmark data and past success stories. The generation AI collects and analyzes data on industry standards and competitors to propose the optimal action plan for the company. Step 3: The Monitoring Department monitors progress based on the action plan proposed by the Proposal Department. The Monitoring Department generates regular reports and provides a real-time dashboard. The Generation AI analyzes progress in real time and displays it on the dashboard, making it easier for companies to understand the current situation. Step 4: The Advice Unit provides advice based on the progress monitored by the Monitoring Unit. The Advice Unit provides necessary advice based on the progress. The Generative AI analyzes the progress and suggests the next action to be taken.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

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

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

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

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where companies can input their issues, a proposal unit that analyzes the issues input by the reception unit and proposes steps for promoting DX; a monitoring unit that monitors progress based on the action plan proposed by the proposal unit; an advice unit that provides advice based on the progress monitored by the monitoring unit. A system characterized by:

2. The proposal unit Proposing steps to promote DX based on industry benchmark data and past success stories 2. The system of claim 1.

3. The monitoring unit Providing reporting and real-time dashboards 2. The system of claim 1.

4. The advice unit Providing advice based on progress 2. The system of claim 1.

5. The reception unit Companies can access the site online and enter their assignments.

2. The system of claim 1.

6. The proposal unit Propose action plans and resource allocations 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of task input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the company's past assignment entry history and select the entry method 2. The system of claim 1.

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