system

Abstract

The system according to this embodiment aims to enable agents with specialized knowledge of the business to respond quickly and appropriately to employees' questions and inquiries. [Solution] The system according to this embodiment comprises a learning unit, a reception unit, and a provision unit. The learning unit learns knowledge specific to the business. The reception unit receives questions and consultations based on the knowledge learned by the learning unit. The provision unit provides appropriate advice based on the questions and consultations received by the reception unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for an agent with knowledge specialized in business to respond quickly and appropriately to the questions and consultations of employees.

[0005] The system according to the embodiment aims to enable an agent with knowledge specialized in business to respond quickly and appropriately to the questions and consultations of employees.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a reception unit, and a provision unit. The learning unit learns knowledge specific to the business. The reception unit receives questions and consultations based on the knowledge learned by the learning unit. The provision unit provides appropriate advice based on the questions and consultations received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment allows agents with specialized knowledge of the business to respond quickly and appropriately to employees' questions and inquiries. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The support system according to an embodiment of the present invention is a mechanism that contributes to improving overall company productivity by enabling employees to receive necessary support even while working from home. This support system includes a learning unit for learning work-specific knowledge and providing employees with the information they need. Next, it includes a reception unit for receiving questions and consultations and providing appropriate advice on unclear points and concerns related to work. Furthermore, it includes a support unit for supporting conversations and casual chats, thereby revitalizing communication among employees. Finally, it includes a proposal unit for making suggestions to improve productivity and providing support to enhance work efficiency. For example, when an employee asks a question about their work, the learning unit provides an appropriate answer to that question. Also, when an employee consults the reception unit about a work-related concern, the reception unit provides appropriate advice on that concern. Furthermore, the support system supports conversations and casual chats among employees, revitalizing communication. For example, the support system provides topics that allow employees to relax. Finally, the support system makes suggestions to improve productivity and enhance work efficiency. For example, the support system proposes improvements to work processes or the introduction of new tools. As a result, the support system enables employees to receive the necessary support even while working from home, thereby improving overall company productivity.

[0029] The support system according to this embodiment comprises a learning unit, a reception unit, and a provision unit. The learning unit learns knowledge specific to the business. For example, the learning unit learns knowledge specific to the business, such as sales, marketing, and technical support. The learning unit can also collect business-related data using AI and learn knowledge based on that data. For example, the learning unit collects sales data and learns sales skills based on that data. Furthermore, the learning unit can analyze past business data and learn knowledge based on the results of that analysis. For example, the learning unit analyzes past project data and learns project management skills based on that data. The reception unit receives questions and consultations based on the knowledge learned by the learning unit. For example, the reception unit receives questions and consultations by methods such as chat, email, and telephone. Furthermore, the reception unit can also analyze the content of questions and consultations using AI and take appropriate action based on the analysis results. For example, the reception unit analyzes questions received via chat using AI and provides appropriate answers to those questions. Furthermore, the reception unit can refer to past consultation history and select the optimal response method based on that history. For example, the reception department can refer to past responses to similar inquiries. The service department provides appropriate advice based on the questions and inquiries received by the reception department. The service department provides advice such as procedures for problem solving and recommended actions. The service department can also use AI to analyze the content of questions and inquiries and provide appropriate advice based on the analysis results. For example, the service department can provide optimal advice based on the question content analyzed by AI. Furthermore, the service department can refer to past advice history and select the optimal advice based on that history. For example, the service department can refer to past advice if there have been similar inquiries. As a result, the support system according to this embodiment can learn business-specific knowledge and provide appropriate advice to questions and inquiries.

[0030] The learning unit acquires knowledge specific to its business. For example, it can acquire knowledge specific to tasks such as sales, marketing, and technical support. Furthermore, the learning unit can use AI to collect business-related data and learn knowledge based on that data. Specifically, the AI ​​uses natural language processing technology to analyze the content of business-related documents, reports, and emails, and extract important information. For example, when collecting sales data and learning sales skills based on that data, the AI ​​analyzes past sales performance and customer feedback to identify successful and unsuccessful sales methods. In addition, the learning unit can analyze past business data and learn knowledge based on the analysis results. For example, the learning unit can analyze past project data and learn project management skills based on that data. The AI ​​analyzes project progress, resource allocation, and the frequency of problems to derive the optimal project management method. This allows the learning unit to collect and analyze a wide range of business-related data and efficiently acquire practical knowledge. Furthermore, the learning unit can regularly incorporate new data and constantly update its business knowledge. This allows the learning department to respond quickly to the changing work environment and always provide the latest knowledge.

[0031] The reception department receives questions and consultations based on the knowledge learned by the learning department. The reception department accepts questions and consultations via methods such as chat, email, and telephone. Furthermore, the reception department can use AI to analyze the content of questions and consultations and provide appropriate responses based on the analysis results. Specifically, it analyzes questions received via chat and provides appropriate answers. The AI ​​uses natural language processing technology to understand the intent of the question and searches for the optimal answer from a relevant knowledge base. For example, if a user asks, "Please tell me about the sales strategy for the new product," the AI ​​provides specific advice based on past data and success stories related to sales strategies. In addition, the reception department can refer to past consultation history and select the optimal response method based on that history. For example, if a similar consultation has occurred in the past, the reception department can refer to the response method used then. The AI ​​stores past consultation content and response results in a database, enabling quick and appropriate responses when similar consultations occur. This allows the reception department to respond quickly and accurately to user questions and consultations, improving user satisfaction. The reception department can also collect user feedback and use it to improve its response methods. This allows the reception department to consistently provide the best possible service, improving the overall reliability and efficiency of the system.

[0032] The service department provides appropriate advice based on questions and consultations received by the reception department. For example, the service department provides advice such as procedures for problem-solving and recommended actions. Furthermore, the service department can use AI to analyze the content of questions and consultations and provide appropriate advice based on the analysis results. Specifically, the AI ​​analyzes the question and searches for the most suitable advice from a relevant knowledge base. For example, if a user asks, "Please tell me how to conduct market research for a new product," the AI ​​will provide specific procedures and recommended actions based on past market research data and successful case studies. In addition, the service department can refer to past advice history and select the most suitable advice based on that history. For example, if a similar consultation has occurred in the past, the service department will refer to that advice. The AI ​​stores past advice and its results in a database, enabling it to quickly provide appropriate advice when similar consultations arise. This allows the service department to provide quick and accurate advice to users' questions and consultations, supporting their problem-solving. The service department can also collect user feedback and use it to improve the advice provided. This allows the service department to consistently provide optimal advice and improve the overall reliability and efficiency of the system.

[0033] The company has a support department to assist with conversations and casual chats. The support department assists with conversations and casual chats, such as work-related conversations and everyday small talk. The support department can also use AI to analyze the content of conversations and casual chats and provide appropriate topics based on the analysis results. For example, the support department can provide topics that employees might be interested in based on the conversation content analyzed by the AI. Furthermore, the support department can refer to past communication history and select the most suitable topics based on that history. For example, the support department can provide related topics based on topics that have been discussed in the past. In this way, the support department can revitalize communication among employees by supporting conversations and casual chats.

[0034] The company has a proposal department that makes suggestions to improve productivity. The proposal department makes suggestions to improve productivity, such as improving business processes or introducing new tools. The proposal department can also use AI to analyze business data and make optimal suggestions based on the analysis results. For example, the proposal department can make suggestions to improve business efficiency based on business data analyzed by AI. Furthermore, the proposal department can refer to past proposal history and select the most suitable suggestion based on that history. For example, if a similar suggestion has been made in the past, the proposal department will refer to that suggestion. In this way, the proposal department can improve business efficiency by making suggestions to improve productivity.

[0035] The learning department can acquire knowledge specific to the operations of each division and headquarters. For example, the learning department can acquire knowledge specific to the operations of the sales headquarters, the technology headquarters, etc. The learning department can also use AI to collect operational data from each headquarters and acquire knowledge based on that data. For example, the learning department can collect data from the sales headquarters and acquire sales skills based on that data. Furthermore, the learning department can analyze past operational data and acquire knowledge based on the results of that analysis. For example, the learning department can analyze past project data from the technology headquarters and acquire technical skills based on that data. In this way, by acquiring knowledge specific to the operations of each division and headquarters, more specialized support becomes possible.

[0036] The reception department allows employees to easily ask questions and seek advice whenever needed. The reception department accepts questions and consultations via methods such as chat, email, and telephone. It can also use AI to analyze the content of questions and consultations and provide appropriate responses based on the analysis results. For example, the reception department can use AI to analyze questions received via chat and provide appropriate answers. Furthermore, the reception department can refer to past consultation history and select the optimal response method based on that history. For example, if a similar consultation has occurred in the past, the reception department can refer to the response method used then. This allows employees to easily ask questions and seek advice whenever needed, resolving any unclear points or concerns related to their work.

[0037] The support department can provide appropriate advice in response to questions and consultations. For example, it can provide advice on problem-solving procedures and recommended actions. The support department can also use AI to analyze the content of questions and consultations and provide appropriate advice based on the analysis results. For example, the support department can provide optimal advice based on the question content analyzed by AI. Furthermore, the support department can refer to past advice history and select the best advice based on that history. For example, if there has been a similar consultation in the past, the support department will refer to that advice. In this way, by providing appropriate advice in response to questions and consultations, the work efficiency of employees can be improved.

[0038] The learning unit can optimize its learning algorithm by referring to past business data. For example, it can extract frequently occurring problems from past business data and reflect their solutions in the learning content. It can also analyze past business data and incorporate efficient methods for specific tasks into the learning content. Furthermore, it can reflect business trends and changes in the learning content based on past business data. This allows for the optimization of the learning algorithm and improvement of learning accuracy by referring to past business data.

[0039] The learning department can dynamically change learning priorities according to the progress of work. For example, when work enters a busy period, the learning department will prioritize learning content related to important tasks. Furthermore, during periods of calmer workloads, the learning department can provide learning content that contributes to long-term skill development. In addition, if an urgent task arises, the learning department can immediately provide learning content related to that task. This allows for efficient learning by changing learning priorities according to the progress of work.

[0040] The learning unit can apply different learning algorithms depending on the type of work. For example, it can apply a learning algorithm specialized for sales tasks to improve sales skills. It can also apply a learning algorithm specialized for technical tasks to improve technical skills. Furthermore, it can apply a learning algorithm specialized for administrative tasks to improve administrative skills. This allows for more effective learning by applying different learning algorithms depending on the type of work.

[0041] The learning department can provide optimal learning content by taking into account employees' geographical location. For example, if an employee is overseas, the learning department can provide learning content tailored to that region. Furthermore, if an employee is working remotely, the learning department can provide content that is easy to study at home. Additionally, if an employee is on a business trip, the learning department can provide learning content that is useful at their destination. In this way, by considering employees' geographical location, the learning department can provide more appropriate learning content.

[0042] The reception department can select the most appropriate response by referring to past consultation history. For example, if there have been similar consultations in the past, the reception department can refer to the response methods used then. Furthermore, the reception department can select the most effective response method from past consultation history. In addition, the reception department can analyze past consultation history and improve its response methods. This allows for the selection of the most appropriate response method and improvement of the quality of service by referring to past consultation history.

[0043] The reception desk can dynamically change the priority of responses based on the urgency of the consultation. For example, the reception desk can respond immediately to high-urgency consultations. Conversely, it can also postpone less urgent consultations. Furthermore, the reception desk can assess the urgency of consultations in real time and change the priority of responses accordingly. This allows for efficient responses by adjusting the priority of responses based on the urgency of the consultation.

[0044] The reception desk can apply different response algorithms depending on the category of the inquiry. For example, it can apply a specialized technical response algorithm to technical inquiries. It can also apply a specialized HR response algorithm to HR-related inquiries. Furthermore, it can apply a specialized sales response algorithm to sales-related inquiries. By applying different response algorithms according to the category of the inquiry, more appropriate responses become possible.

[0045] The reception desk can provide the most appropriate response by considering the employee's device information. For example, if the employee is using a smartphone, the reception desk can provide a mobile-optimized response. Similarly, if the employee is using a personal computer, the reception desk can provide a desktop-optimized response. Furthermore, if the employee is using a tablet, the reception desk can provide a tablet-optimized response. This allows for the provision of more appropriate responses by considering the employee's device information.

[0046] The service provider can select the most appropriate advice by referring to past advice history. For example, if there have been similar consultations in the past, the service provider can refer to that advice. Furthermore, the service provider can select the most effective advice from past advice history. In addition, the service provider can analyze past advice history to improve the quality of advice. This allows for the selection of the most appropriate advice and improvement of advice quality by referring to past advice history.

[0047] The service provider can dynamically adjust the level of detail of the advice based on the level of detail of the consultation. For example, if the consultation is detailed, the service provider will provide detailed advice. Conversely, if the consultation is concise, the service provider can also provide concise advice. Furthermore, the service provider can evaluate the level of detail of the consultation in real time and adjust the level of detail of the advice accordingly. This allows for more appropriate advice to be provided by adjusting the level of detail of the advice based on the level of detail of the consultation.

[0048] The service provider can apply different advice algorithms depending on the category of the consultation. For example, it can apply a specialized technical advice algorithm to technical consultations. It can also apply a specialized human resources advice algorithm to human resources-related consultations. Furthermore, it can apply a specialized sales advice algorithm to sales-related consultations. By applying different advice algorithms according to the category of the consultation, more appropriate advice can be provided.

[0049] The service provider can offer optimal advice by taking into account the geographical location of employees. For example, if an employee is overseas, the service provider can provide advice tailored to that region. Furthermore, if an employee is working remotely, the service provider can offer advice that is easy to implement at home. Additionally, if an employee is on a business trip, the service provider can offer advice that is useful at their destination. This allows for the provision of more appropriate advice by considering the geographical location of employees.

[0050] The support department can select the most appropriate topics by referring to past communication history. For example, the support department can provide related topics based on what has been discussed in the past. Furthermore, the support department can select topics that employees are likely to be interested in based on past communication history. In addition, the support department can analyze past communication history to improve the quality of topics. This allows for the selection of optimal topics and improvement of communication quality by referring to past communication history.

[0051] The support department can provide topics based on employees' interests and concerns. For example, the support department can provide topics based on topics that employees are interested in. It can also provide the latest information on industries that employees are interested in. Furthermore, the support department can provide topics related to activities that employees enjoy as hobbies. This allows for more appropriate communication by providing topics based on employees' interests and concerns.

[0052] The proposal department can select the most suitable proposal by referring to past proposal history. For example, if there have been similar proposals in the past, the proposal department can refer to those proposals. Furthermore, the proposal department can select the most effective proposal from past proposal history. In addition, the proposal department can analyze past proposal history to improve the quality of proposals. This allows for the selection of the most suitable proposal and improvement of proposal quality by referring to past proposal history.

[0053] The proposal department can dynamically change the priority of proposals according to the progress of the work. For example, when work enters a busy period, the proposal department will prioritize important proposals. Also, when work is calm, the proposal department can make proposals that will help with long-term skill development. Furthermore, if an urgent task arises, the proposal department can immediately make proposals related to that task. In this way, by changing the priority of proposals according to the progress of the work, efficient proposals can be made.

[0054] The proposal department can apply different proposal algorithms depending on the type of work. For example, it can apply a proposal algorithm specialized for sales work to improve sales skills. It can also apply a proposal algorithm specialized for technical work to improve technical skills. Furthermore, it can apply a proposal algorithm specialized for management work to improve management skills. By applying different proposal algorithms depending on the type of work, it becomes possible to make more appropriate proposals.

[0055] The proposal department can provide optimal suggestions by taking into account employees' geographical locations. For example, if an employee is overseas, the proposal department can provide suggestions tailored to that region. Furthermore, if an employee is working remotely, the proposal department can provide suggestions that are easy to implement at home. Additionally, if an employee is on a business trip, the proposal department can provide suggestions that are useful at their destination. In this way, by considering employees' geographical locations, the proposal department can provide more appropriate suggestions.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The support system can monitor employee work performance in real time and dynamically adjust support based on the results. For example, the learning department can provide learning materials to improve an employee's skills if their work performance is declining. The reception department can handle more advanced questions and consultations for high-performing employees. Furthermore, the service department can provide advice on further efficiency improvements to employees whose work performance is improving. In this way, by adjusting support according to work performance, employee growth can be promoted.

[0058] The support system can analyze employees' work history and customize support based on the results. For example, the learning department can provide employees with the most suitable learning content based on their past work history. The reception department can select the most appropriate response method based on past consultation history. Furthermore, the service provision department can provide optimal advice based on past advice history. By customizing support based on work history, more effective support becomes possible.

[0059] The support system can provide support while taking into account employees' work schedules. For example, the learning department can provide learning materials tailored to employees' work schedules. The reception department can also receive questions and consultations at the optimal time based on employees' work schedules. Furthermore, the service provision department can also provide advice at the optimal time according to employees' work schedules. By providing support based on work schedules, more effective support becomes possible.

[0060] The support system can provide support tailored to each employee's work environment. For example, the learning department can provide learning materials that are easy to study at home if the employee is working remotely. The reception department can provide useful information for employees on business trips. Furthermore, the support department can provide advice best suited to the office environment if the employee is working in the office. By providing support based on the work environment, more effective support becomes possible.

[0061] The support system can provide support based on employees' work objectives. For example, the learning department can provide learning content tailored to employees' work objectives. The reception department can also receive the most appropriate questions and consultations based on employees' work objectives. Furthermore, the service department can provide optimal advice according to employees' work objectives. By providing support based on work objectives, more effective support becomes possible.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The learning unit acquires knowledge specific to its business. For example, it acquires knowledge specific to tasks such as sales, marketing, and technical support. The learning unit can also use AI to collect business-related data and learn knowledge based on that data. For example, it can collect sales data and learn sales skills based on that data. Furthermore, it can analyze past business data and learn knowledge based on the results of that analysis. For example, it can analyze past project data and learn project management skills based on that data. Step 2: The reception department receives questions and consultations based on the knowledge acquired by the learning department. For example, questions and consultations can be received via chat, email, or telephone. It can also use AI to analyze the content of questions and consultations and provide appropriate responses based on the analysis results. For example, it can analyze questions received via chat and provide appropriate answers. Furthermore, it can refer to past consultation history and select the optimal response method based on that history. For example, if a similar consultation has occurred in the past, the response method from that case can be used as a reference. Step 3: The service department provides appropriate advice based on the questions and consultations received by the reception department. For example, it provides advice on steps for solving problems and recommended actions. It can also use AI to analyze the content of questions and consultations and provide appropriate advice based on the analysis results. For example, it can provide optimal advice based on the question content analyzed by AI. Furthermore, it can refer to past advice history and select the best advice based on that history. For example, if there have been similar consultations in the past, that advice can be used as a reference.

[0064] (Example of form 2) The support system according to an embodiment of the present invention is a mechanism that contributes to improving overall company productivity by enabling employees to receive necessary support even while working from home. This support system includes a learning unit for learning work-specific knowledge and providing employees with the information they need. Next, it includes a reception unit for receiving questions and consultations and providing appropriate advice on unclear points and concerns related to work. Furthermore, it includes a support unit for supporting conversations and casual chats, thereby revitalizing communication among employees. Finally, it includes a proposal unit for making suggestions to improve productivity and providing support to enhance work efficiency. For example, when an employee asks a question about their work, the learning unit provides an appropriate answer to that question. Also, when an employee consults the reception unit about a work-related concern, the reception unit provides appropriate advice on that concern. Furthermore, the support system supports conversations and casual chats among employees, revitalizing communication. For example, the support system provides topics that allow employees to relax. Finally, the support system makes suggestions to improve productivity and enhance work efficiency. For example, the support system proposes improvements to work processes or the introduction of new tools. As a result, the support system enables employees to receive the necessary support even while working from home, thereby improving overall company productivity.

[0065] The support system according to this embodiment comprises a learning unit, a reception unit, and a provision unit. The learning unit learns knowledge specific to the business. For example, the learning unit learns knowledge specific to the business, such as sales, marketing, and technical support. The learning unit can also collect business-related data using AI and learn knowledge based on that data. For example, the learning unit collects sales data and learns sales skills based on that data. Furthermore, the learning unit can analyze past business data and learn knowledge based on the results of that analysis. For example, the learning unit analyzes past project data and learns project management skills based on that data. The reception unit receives questions and consultations based on the knowledge learned by the learning unit. For example, the reception unit receives questions and consultations by methods such as chat, email, and telephone. Furthermore, the reception unit can also analyze the content of questions and consultations using AI and take appropriate action based on the analysis results. For example, the reception unit analyzes questions received via chat using AI and provides appropriate answers to those questions. Furthermore, the reception unit can refer to past consultation history and select the optimal response method based on that history. For example, the reception department can refer to past responses to similar inquiries. The service department provides appropriate advice based on the questions and inquiries received by the reception department. The service department provides advice such as procedures for problem solving and recommended actions. The service department can also use AI to analyze the content of questions and inquiries and provide appropriate advice based on the analysis results. For example, the service department can provide optimal advice based on the question content analyzed by AI. Furthermore, the service department can refer to past advice history and select the optimal advice based on that history. For example, the service department can refer to past advice if there have been similar inquiries. As a result, the support system according to this embodiment can learn business-specific knowledge and provide appropriate advice to questions and inquiries.

[0066] The learning unit acquires knowledge specific to its business. For example, it can acquire knowledge specific to tasks such as sales, marketing, and technical support. Furthermore, the learning unit can use AI to collect business-related data and learn knowledge based on that data. Specifically, the AI ​​uses natural language processing technology to analyze the content of business-related documents, reports, and emails, and extract important information. For example, when collecting sales data and learning sales skills based on that data, the AI ​​analyzes past sales performance and customer feedback to identify successful and unsuccessful sales methods. In addition, the learning unit can analyze past business data and learn knowledge based on the analysis results. For example, the learning unit can analyze past project data and learn project management skills based on that data. The AI ​​analyzes project progress, resource allocation, and the frequency of problems to derive the optimal project management method. This allows the learning unit to collect and analyze a wide range of business-related data and efficiently acquire practical knowledge. Furthermore, the learning unit can regularly incorporate new data and constantly update its business knowledge. This allows the learning department to respond quickly to the changing work environment and always provide the latest knowledge.

[0067] The reception department receives questions and consultations based on the knowledge learned by the learning department. The reception department accepts questions and consultations via methods such as chat, email, and telephone. Furthermore, the reception department can use AI to analyze the content of questions and consultations and provide appropriate responses based on the analysis results. Specifically, it analyzes questions received via chat and provides appropriate answers. The AI ​​uses natural language processing technology to understand the intent of the question and searches for the optimal answer from a relevant knowledge base. For example, if a user asks, "Please tell me about the sales strategy for the new product," the AI ​​provides specific advice based on past data and success stories related to sales strategies. In addition, the reception department can refer to past consultation history and select the optimal response method based on that history. For example, if a similar consultation has occurred in the past, the reception department can refer to the response method used then. The AI ​​stores past consultation content and response results in a database, enabling quick and appropriate responses when similar consultations occur. This allows the reception department to respond quickly and accurately to user questions and consultations, improving user satisfaction. The reception department can also collect user feedback and use it to improve its response methods. This allows the reception department to consistently provide the best possible service, improving the overall reliability and efficiency of the system.

[0068] The service department provides appropriate advice based on questions and consultations received by the reception department. For example, the service department provides advice such as procedures for problem-solving and recommended actions. Furthermore, the service department can use AI to analyze the content of questions and consultations and provide appropriate advice based on the analysis results. Specifically, the AI ​​analyzes the question and searches for the most suitable advice from a relevant knowledge base. For example, if a user asks, "Please tell me how to conduct market research for a new product," the AI ​​will provide specific procedures and recommended actions based on past market research data and successful case studies. In addition, the service department can refer to past advice history and select the most suitable advice based on that history. For example, if a similar consultation has occurred in the past, the service department will refer to that advice. The AI ​​stores past advice and its results in a database, enabling it to quickly provide appropriate advice when similar consultations arise. This allows the service department to provide quick and accurate advice to users' questions and consultations, supporting their problem-solving. The service department can also collect user feedback and use it to improve the advice provided. This allows the service department to consistently provide optimal advice and improve the overall reliability and efficiency of the system.

[0069] The company has a support department to assist with conversations and casual chats. The support department assists with conversations and casual chats, such as work-related conversations and everyday small talk. The support department can also use AI to analyze the content of conversations and casual chats and provide appropriate topics based on the analysis results. For example, the support department can provide topics that employees might be interested in based on the conversation content analyzed by the AI. Furthermore, the support department can refer to past communication history and select the most suitable topics based on that history. For example, the support department can provide related topics based on topics that have been discussed in the past. In this way, the support department can revitalize communication among employees by supporting conversations and casual chats.

[0070] The company has a proposal department that makes suggestions to improve productivity. The proposal department makes suggestions to improve productivity, such as improving business processes or introducing new tools. The proposal department can also use AI to analyze business data and make optimal suggestions based on the analysis results. For example, the proposal department can make suggestions to improve business efficiency based on business data analyzed by AI. Furthermore, the proposal department can refer to past proposal history and select the most suitable suggestion based on that history. For example, if a similar suggestion has been made in the past, the proposal department will refer to that suggestion. In this way, the proposal department can improve business efficiency by making suggestions to improve productivity.

[0071] The learning department can acquire knowledge specific to the operations of each division and headquarters. For example, the learning department can acquire knowledge specific to the operations of the sales headquarters, the technology headquarters, etc. The learning department can also use AI to collect operational data from each headquarters and acquire knowledge based on that data. For example, the learning department can collect data from the sales headquarters and acquire sales skills based on that data. Furthermore, the learning department can analyze past operational data and acquire knowledge based on the results of that analysis. For example, the learning department can analyze past project data from the technology headquarters and acquire technical skills based on that data. In this way, by acquiring knowledge specific to the operations of each division and headquarters, more specialized support becomes possible.

[0072] The reception department allows employees to easily ask questions and seek advice whenever needed. The reception department accepts questions and consultations via methods such as chat, email, and telephone. It can also use AI to analyze the content of questions and consultations and provide appropriate responses based on the analysis results. For example, the reception department can use AI to analyze questions received via chat and provide appropriate answers. Furthermore, the reception department can refer to past consultation history and select the optimal response method based on that history. For example, if a similar consultation has occurred in the past, the reception department can refer to the response method used then. This allows employees to easily ask questions and seek advice whenever needed, resolving any unclear points or concerns related to their work.

[0073] The support department can provide appropriate advice in response to questions and consultations. For example, it can provide advice on problem-solving procedures and recommended actions. The support department can also use AI to analyze the content of questions and consultations and provide appropriate advice based on the analysis results. For example, the support department can provide optimal advice based on the question content analyzed by AI. Furthermore, the support department can refer to past advice history and select the best advice based on that history. For example, if there has been a similar consultation in the past, the support department will refer to that advice. In this way, by providing appropriate advice in response to questions and consultations, the work efficiency of employees can be improved.

[0074] The learning unit can estimate employees' emotions and adjust learning content based on those emotions. For example, the learning unit can estimate employees' emotions using facial recognition or voice analysis. For instance, if an employee is stressed, the learning unit can provide relaxing learning content. Conversely, if an employee is highly motivated, the learning unit can provide challenging learning content. Furthermore, if an employee is tired, the learning unit can provide simple and easy-to-understand learning content. This allows for more effective learning by adjusting learning content according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The learning unit can optimize its learning algorithm by referring to past business data. For example, it can extract frequently occurring problems from past business data and reflect their solutions in the learning content. It can also analyze past business data and incorporate efficient methods for specific tasks into the learning content. Furthermore, it can reflect business trends and changes in the learning content based on past business data. This allows for the optimization of the learning algorithm and improvement of learning accuracy by referring to past business data.

[0076] The learning department can dynamically change learning priorities according to the progress of work. For example, when work enters a busy period, the learning department will prioritize learning content related to important tasks. Furthermore, during periods of calmer workloads, the learning department can provide learning content that contributes to long-term skill development. In addition, if an urgent task arises, the learning department can immediately provide learning content related to that task. This allows for efficient learning by changing learning priorities according to the progress of work.

[0077] The learning unit can estimate employees' emotions and adjust the frequency of learning based on those emotions. The learning unit can estimate employees' emotions using, for example, facial recognition or voice analysis. For instance, if an employee is stressed, the learning unit can reduce the frequency of learning and increase relaxation time. Conversely, if an employee is highly motivated, the learning unit can increase the frequency of learning to promote skill development. Furthermore, if an employee is tired, the learning unit can adjust the frequency of learning and prioritize rest. This allows for more effective learning by adjusting the frequency of learning according to employees' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The learning unit can apply different learning algorithms depending on the type of work. For example, it can apply a learning algorithm specialized for sales tasks to improve sales skills. It can also apply a learning algorithm specialized for technical tasks to improve technical skills. Furthermore, it can apply a learning algorithm specialized for administrative tasks to improve administrative skills. This allows for more effective learning by applying different learning algorithms depending on the type of work.

[0079] The learning department can provide optimal learning content by taking into account employees' geographical location. For example, if an employee is overseas, the learning department can provide learning content tailored to that region. Furthermore, if an employee is working remotely, the learning department can provide content that is easy to study at home. Additionally, if an employee is on a business trip, the learning department can provide learning content that is useful at their destination. In this way, by considering employees' geographical location, the learning department can provide more appropriate learning content.

[0080] The reception desk can estimate an employee's emotions and adjust its response based on those emotions. For example, the reception desk might use facial recognition or voice analysis to estimate an employee's emotions. If an employee is stressed, the reception desk might respond with gentle language. If an employee is relaxed, the reception desk might respond in a friendly manner. Furthermore, if an employee is in a hurry, the reception desk might respond quickly. This allows for more appropriate responses by adjusting the reception desk's response according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception department can select the most appropriate response by referring to past consultation history. For example, if there have been similar consultations in the past, the reception department can refer to the response methods used then. Furthermore, the reception department can select the most effective response method from past consultation history. In addition, the reception department can analyze past consultation history and improve its response methods. This allows for the selection of the most appropriate response method and improvement of the quality of service by referring to past consultation history.

[0082] The reception desk can dynamically change the priority of responses based on the urgency of the consultation. For example, the reception desk can respond immediately to high-urgency consultations. Conversely, it can also postpone less urgent consultations. Furthermore, the reception desk can assess the urgency of consultations in real time and change the priority of responses accordingly. This allows for efficient responses by adjusting the priority of responses based on the urgency of the consultation.

[0083] The reception desk can estimate the emotions of employees and adjust the response speed based on the estimated emotions. For example, the reception desk can estimate employees' emotions using facial recognition or voice analysis. For instance, if an employee is stressed, the reception desk can respond quickly. Conversely, if an employee is relaxed, the reception desk can respond slowly. Furthermore, if an employee is in a hurry, the reception desk can respond immediately. This allows for more appropriate responses by adjusting the response speed according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception desk can apply different response algorithms depending on the category of the inquiry. For example, it can apply a specialized technical response algorithm to technical inquiries. It can also apply a specialized HR response algorithm to HR-related inquiries. Furthermore, it can apply a specialized sales response algorithm to sales-related inquiries. By applying different response algorithms according to the category of the inquiry, more appropriate responses become possible.

[0085] The reception desk can provide the most appropriate response by considering the employee's device information. For example, if the employee is using a smartphone, the reception desk can provide a mobile-optimized response. Similarly, if the employee is using a personal computer, the reception desk can provide a desktop-optimized response. Furthermore, if the employee is using a tablet, the reception desk can provide a tablet-optimized response. This allows for the provision of more appropriate responses by considering the employee's device information.

[0086] The service provider can estimate an employee's emotions and adjust the way advice is expressed based on those emotions. For example, the service provider might use facial recognition or voice analysis to estimate an employee's emotions. For instance, if an employee is stressed, the service provider might offer advice in gentle language. Similarly, if an employee is relaxed, the service provider might offer advice in friendly language. Furthermore, if an employee is in a hurry, the service provider might offer concise and quick advice. This allows for more appropriate advice by adjusting the expression of advice according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The service provider can select the most appropriate advice by referring to past advice history. For example, if there have been similar consultations in the past, the service provider can refer to that advice. Furthermore, the service provider can select the most effective advice from past advice history. In addition, the service provider can analyze past advice history to improve the quality of advice. This allows for the selection of the most appropriate advice and improvement of advice quality by referring to past advice history.

[0088] The service provider can dynamically adjust the level of detail of the advice based on the level of detail of the consultation. For example, if the consultation is detailed, the service provider will provide detailed advice. Conversely, if the consultation is concise, the service provider can also provide concise advice. Furthermore, the service provider can evaluate the level of detail of the consultation in real time and adjust the level of detail of the advice accordingly. This allows for more appropriate advice to be provided by adjusting the level of detail of the advice based on the level of detail of the consultation.

[0089] The service provider can estimate an employee's emotions and prioritize advice based on those emotions. For example, the service provider might use facial recognition or voice analysis to estimate an employee's emotions. For instance, if an employee is stressed, the service provider might prioritize important advice. Conversely, if an employee is relaxed, the service provider might offer general advice. Furthermore, if an employee is in a hurry, the service provider might prioritize advice requiring a quick response. This allows for more appropriate advice by prioritizing it according to the employee's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The service provider can apply different advice algorithms depending on the category of the consultation. For example, it can apply a specialized technical advice algorithm to technical consultations. It can also apply a specialized human resources advice algorithm to human resources-related consultations. Furthermore, it can apply a specialized sales advice algorithm to sales-related consultations. By applying different advice algorithms according to the category of the consultation, more appropriate advice can be provided.

[0091] The service provider can offer optimal advice by taking into account the geographical location of employees. For example, if an employee is overseas, the service provider can provide advice tailored to that region. Furthermore, if an employee is working remotely, the service provider can offer advice that is easy to implement at home. Additionally, if an employee is on a business trip, the service provider can offer advice that is useful at their destination. This allows for the provision of more appropriate advice by considering the geographical location of employees.

[0092] The support department can estimate employees' emotions and adjust the content of conversations and small talk based on those estimated emotions. For example, the support department can estimate employees' emotions using facial recognition or voice analysis. For instance, if an employee is stressed, the support department can offer relaxing topics. If an employee is relaxed, the support department can offer enjoyable topics. Furthermore, if an employee is tired, the support department can offer simple and easy-to-understand topics. This allows for more appropriate communication by adjusting the content of conversations and small talk according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The support department can select the most appropriate topics by referring to past communication history. For example, the support department can provide related topics based on what has been discussed in the past. Furthermore, the support department can select topics that employees are likely to be interested in based on past communication history. In addition, the support department can analyze past communication history to improve the quality of topics. This allows for the selection of optimal topics and improvement of communication quality by referring to past communication history.

[0094] The support department can estimate employees' emotions and adjust the frequency of conversations and small talk based on those estimates. For example, the support department might use facial recognition or voice analysis to estimate employees' emotions. If an employee is stressed, the support department might reduce the frequency of conversations and small talk, increasing relaxation time. Conversely, if an employee is relaxed, the support department might increase the frequency of conversations and small talk to stimulate communication. Furthermore, if an employee is tired, the support department might adjust the frequency of conversations and small talk, prioritizing rest. This allows for more appropriate communication by adjusting the frequency of conversations and small talk according to employees' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The support department can provide topics based on employees' interests and concerns. For example, the support department can provide topics based on topics that employees are interested in. It can also provide the latest information on industries that employees are interested in. Furthermore, the support department can provide topics related to activities that employees enjoy as hobbies. This allows for more appropriate communication by providing topics based on employees' interests and concerns.

[0096] The proposal department can estimate employees' emotions and adjust the content of proposals based on those estimated emotions. For example, the proposal department might use facial recognition or voice analysis to estimate employees' emotions. For instance, if an employee is feeling stressed, the proposal department might offer a relaxing proposal. Conversely, if an employee is highly motivated, the proposal department might offer a challenging proposal. Furthermore, if an employee is tired, the proposal department might offer a simple and easy-to-implement proposal. This allows for more appropriate proposals by adjusting the content according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The proposal department can select the most suitable proposal by referring to past proposal history. For example, if there have been similar proposals in the past, the proposal department can refer to those proposals. Furthermore, the proposal department can select the most effective proposal from past proposal history. In addition, the proposal department can analyze past proposal history to improve the quality of proposals. This allows for the selection of the most suitable proposal and improvement of proposal quality by referring to past proposal history.

[0098] The proposal department can dynamically change the priority of proposals according to the progress of the work. For example, when work enters a busy period, the proposal department will prioritize important proposals. Also, when work is calm, the proposal department can make proposals that will help with long-term skill development. Furthermore, if an urgent task arises, the proposal department can immediately make proposals related to that task. In this way, by changing the priority of proposals according to the progress of the work, efficient proposals can be made.

[0099] The suggestion department can estimate employees' emotions and adjust the frequency of suggestions based on those emotions. For example, the suggestion department might use facial recognition or voice analysis to estimate employee emotions. If an employee is stressed, the suggestion department might reduce the frequency of suggestions and increase their relaxation time. Conversely, if an employee is highly motivated, the suggestion department might increase the frequency of suggestions to promote skill development. Furthermore, if an employee is tired, the suggestion department might adjust the frequency of suggestions to prioritize rest. This allows for more appropriate suggestions by adjusting the frequency of suggestions according to employee emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The proposal department can apply different proposal algorithms depending on the type of work. For example, it can apply a proposal algorithm specialized for sales work to improve sales skills. It can also apply a proposal algorithm specialized for technical work to improve technical skills. Furthermore, it can apply a proposal algorithm specialized for management work to improve management skills. By applying different proposal algorithms depending on the type of work, it becomes possible to make more appropriate proposals.

[0101] The proposal department can provide optimal suggestions by taking into account employees' geographical locations. For example, if an employee is overseas, the proposal department can provide suggestions tailored to that region. Furthermore, if an employee is working remotely, the proposal department can provide suggestions that are easy to implement at home. Additionally, if an employee is on a business trip, the proposal department can provide suggestions that are useful at their destination. In this way, by considering employees' geographical locations, the proposal department can provide more appropriate suggestions.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The support system can estimate employees' emotions and adjust the support content based on those estimates. For example, the learning department can provide relaxing learning content if an employee is feeling stressed. The reception department can respond quickly if an employee is in a hurry. Furthermore, the service department can offer challenging advice if an employee is highly motivated. By adjusting support content according to employees' emotions, more effective support becomes possible.

[0104] The support system can monitor employee work performance in real time and dynamically adjust support based on the results. For example, the learning department can provide learning materials to improve an employee's skills if their work performance is declining. The reception department can handle more advanced questions and consultations for high-performing employees. Furthermore, the service department can provide advice on further efficiency improvements to employees whose work performance is improving. In this way, by adjusting support according to work performance, employee growth can be promoted.

[0105] The support system can estimate employees' emotions and adjust communication methods based on those estimates. For example, the support department can offer relaxing topics if an employee is feeling stressed. The suggestions department can offer challenging suggestions if an employee is highly motivated. Furthermore, the reception department can respond quickly if an employee is in a hurry. By adjusting communication methods according to employees' emotions, more effective support becomes possible.

[0106] The support system can analyze employees' work history and customize support based on the results. For example, the learning department can provide employees with the most suitable learning content based on their past work history. The reception department can select the most appropriate response method based on past consultation history. Furthermore, the service provision department can provide optimal advice based on past advice history. By customizing support based on work history, more effective support becomes possible.

[0107] The support system can estimate employees' emotions and adjust the frequency of learning based on those estimates. For example, if an employee is feeling stressed, the learning department can reduce the frequency of learning and increase their relaxation time. Conversely, if an employee is highly motivated, the learning department can increase the frequency of learning to promote skill development. Furthermore, if an employee is tired, the learning department can adjust the frequency of learning and prioritize rest. This allows for more effective learning by adjusting the frequency of learning according to employees' emotions.

[0108] The support system can provide support while taking into account employees' work schedules. For example, the learning department can provide learning materials tailored to employees' work schedules. The reception department can also receive questions and consultations at the optimal time based on employees' work schedules. Furthermore, the service provision department can also provide advice at the optimal time according to employees' work schedules. By providing support based on work schedules, more effective support becomes possible.

[0109] The support system can estimate employees' emotions and adjust the content of suggestions based on those estimates. For example, if an employee is feeling stressed, the suggestion department can offer relaxing suggestions. Conversely, if an employee is highly motivated, the suggestion department can offer challenging suggestions. Furthermore, if an employee is feeling tired, the suggestion department can offer simple and actionable suggestions. By adjusting suggestions according to employees' emotions, more appropriate suggestions can be made.

[0110] The support system can provide support tailored to each employee's work environment. For example, the learning department can provide learning materials that are easy to study at home if the employee is working remotely. The reception department can provide useful information for employees on business trips. Furthermore, the support department can provide advice best suited to the office environment if the employee is working in the office. By providing support based on the work environment, more effective support becomes possible.

[0111] The support system can estimate an employee's emotions and adjust the way advice is delivered based on those emotions. For example, if an employee is feeling stressed, the system can provide advice in gentle language. If an employee is relaxed, the system can provide advice in a friendly tone. Furthermore, if an employee is in a hurry, the system can provide concise and quick advice. This allows for more appropriate advice by adjusting the delivery of advice according to the employee's emotions.

[0112] The support system can provide support based on employees' work objectives. For example, the learning department can provide learning content tailored to employees' work objectives. The reception department can also receive the most appropriate questions and consultations based on employees' work objectives. Furthermore, the service department can provide optimal advice according to employees' work objectives. By providing support based on work objectives, more effective support becomes possible.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The learning unit acquires knowledge specific to its business. For example, it acquires knowledge specific to tasks such as sales, marketing, and technical support. The learning unit can also use AI to collect business-related data and learn knowledge based on that data. For example, it can collect sales data and learn sales skills based on that data. Furthermore, it can analyze past business data and learn knowledge based on the results of that analysis. For example, it can analyze past project data and learn project management skills based on that data. Step 2: The reception department receives questions and consultations based on the knowledge acquired by the learning department. For example, questions and consultations can be received via chat, email, or telephone. It can also use AI to analyze the content of questions and consultations and provide appropriate responses based on the analysis results. For example, it can analyze questions received via chat and provide appropriate answers. Furthermore, it can refer to past consultation history and select the optimal response method based on that history. For example, if a similar consultation has occurred in the past, the response method from that case can be used as a reference. Step 3: The service department provides appropriate advice based on the questions and consultations received by the reception department. For example, it provides advice on steps for solving problems and recommended actions. It can also use AI to analyze the content of questions and consultations and provide appropriate advice based on the analysis results. For example, it can provide optimal advice based on the question content analyzed by AI. Furthermore, it can refer to past advice history and select the best advice based on that history. For example, if there have been similar consultations in the past, that advice can be used as a reference.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the learning unit, reception unit, provision unit, support unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the learning unit, reception unit, provision unit, support unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the learning unit, reception unit, provision unit, support unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the learning unit, reception unit, provision unit, support unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The support unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A learning department for acquiring knowledge specific to the job, The reception desk accepts questions and consultations based on the knowledge acquired by the aforementioned learning department, The system includes a provisioning unit that provides appropriate advice based on questions and consultations received by the aforementioned reception unit. A system characterized by the following features. (Note 2) It includes a support section to assist with conversations and casual chats. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a proposal department that makes suggestions to improve productivity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, Learn specialized knowledge for the operations of each general headquarters and division. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Each employee is free to ask questions or seek advice whenever needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide appropriate advice in response to questions and inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, The system estimates employees' emotions and adjusts learning content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned learning unit, Optimize the learning algorithm by referring to past business data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, Dynamically change learning priorities according to the progress of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, The system estimates employees' emotions and adjusts the frequency of learning based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, Apply different learning algorithms depending on the type of task. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, We provide optimal learning content by taking into account the geographical location of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is The system estimates the emotions of employees and adjusts the receptionist's response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is We will select the most appropriate course of action by referring to past consultation records. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is The priority of responses will be dynamically changed according to the urgency of the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is The system estimates the emotions of employees and adjusts the response speed of reception staff based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is A different response algorithm is applied depending on the category of the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is We provide the optimal solution by taking into account the employee's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates employees' emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Refer to past advice history to select the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The level of detail in the advice is dynamically adjusted according to the level of detail in the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates employees' emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, Apply different advice algorithms depending on the category of the consultation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We provide optimal advice by taking into account the geographical location of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is The system estimates employees' emotions and adjusts the content of conversations and casual chats based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned support unit is Select the most appropriate topic by referring to past communication history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned support unit is The system estimates employees' emotions and adjusts the frequency of conversations and casual chats based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned support unit is We provide topics based on the interests and concerns of our employees. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We estimate the emotions of our employees and adjust the content of proposals based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned proposal section is, We will select the most suitable proposal by referring to past proposal history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned proposal section is, Dynamically change the priority of proposals according to the progress of the work. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned proposal section is, The system estimates employees' emotions and adjusts the frequency of suggestions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned proposal section is, Apply different proposal algorithms depending on the type of task. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned proposal section is, We provide optimal suggestions by taking into account the geographical location of our employees. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning department for acquiring knowledge specific to the job, The reception desk accepts questions and consultations based on the knowledge acquired by the aforementioned learning department, The system includes a provisioning unit that provides appropriate advice based on questions and consultations received by the aforementioned reception unit. A system characterized by the following features.

2. It includes a support section to assist with conversations and casual chats. The system according to feature 1.

3. It has a proposal department that makes suggestions to improve productivity. The system according to feature 1.

4. The aforementioned learning unit, Learn specialized knowledge for the specific operations of each department / headquarters. The system according to feature 1.

5. The aforementioned reception unit is Each employee is free to ask questions or seek advice whenever needed. The system according to feature 1.

6. The aforementioned supply unit is, We provide appropriate advice in response to questions and inquiries. The system according to feature 1.

7. The aforementioned learning unit, The system estimates employees' emotions and adjusts learning content based on those estimated emotions. The system according to feature 1.

8. The aforementioned learning unit, Optimize the learning algorithm by referring to past business data. The system according to feature 1.

9. The aforementioned learning unit, Dynamically change learning priorities according to the progress of tasks. The system according to feature 1.

10. The aforementioned learning unit, The system estimates employees' emotions and adjusts the frequency of learning based on those estimated emotions. The system according to feature 1.

Images