System

Abstract

An object of a system according to an embodiment is to efficiently manage a problem occurring in a store and quickly provide an appropriate solution.SOLUTION: A system according to an embodiment includes a portal and a generation AI. The portal centrally manages problems occurring in the store. The generated AI suggests an appropriate solution to the problem reported to the portal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently manage problems that occur in stores and quickly provide appropriate solutions.

[0005] The system according to the embodiment aims to efficiently manage problems that occur in stores and quickly provide appropriate solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a portal and a generation AI. The portal centrally manages problems that occur in stores. The generation AI proposes appropriate solutions to problems reported to the portal. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage problems that occur in stores and quickly provide appropriate solutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The problem-solving system according to an embodiment of the present invention is a system that uses a dedicated portal and generation AI to solve problems that occur in stores. This system provides support for quickly and efficiently resolving problems such as incorrect campaign registration, cancellations due to incorrect customer service recognition, and terminal malfunctions. As a result, the problem-solving system can quickly and efficiently solve various problems that occur in stores.

[0029] A problem-solving system according to an embodiment includes a dedicated portal and a generation AI. The dedicated portal centrally manages problems that occur in stores. For example, the dedicated portal is designed to be easily accessible by store staff, allowing them to easily report problems and search for solutions. The dedicated portal also manages a problem-solving history and allows them to refer to past solutions. The generation AI proposes appropriate solutions for problems reported to the dedicated portal. For example, if a campaign registration error occurs, the generation AI analyzes the problem and proposes the correct registration method and correction procedure. Furthermore, if a cancellation occurs due to a customer service misunderstanding, the generation AI analyzes the cause and proposes appropriate countermeasures. Furthermore, if a terminal malfunction occurs, the generation AI analyzes the cause of the malfunction and proposes repair methods or alternative solutions. As a result, the problem-solving system according to an embodiment can quickly and efficiently resolve problems that occur in stores. For example, the dedicated portal manages a problem-solving history and allows them to refer to past solutions. The generation AI generates solutions based on prompts containing details of the problem. This reduces the burden on store staff and improves customer satisfaction.

[0030] The dedicated portal is equipped with a voice input function, allowing store staff to report problems by voice. The dedicated portal allows staff to input problems by voice using, for example, a microphone, convert the problem into text, and register it on the portal. For example, voice recognition technology can be used to allow store staff to report problems by voice. This allows store staff to report problems without using their hands.

[0031] The dedicated portal automatically determines the priority of the problem and prioritizes dealing with problems of high urgency. The dedicated portal, for example, analyzes the content and scope of the problem and sets the priority. For example, in order to deal with problems of high urgency quickly, the dedicated portal has a function to automatically determine the priority of the problem. This allows problems of high urgency to be dealt with quickly.

[0032] The dedicated portal will be provided as a mobile app, allowing problems to be reported and resolved outside the store. For example, the dedicated portal will be developed as an app that can be accessed from a smartphone or tablet. For example, the dedicated portal will be provided as a mobile app, allowing problems to be reported and resolved outside the store. This will allow problems to be reported and resolved outside the store.

[0033] The dedicated portal has a function for linking with other stores, allowing solutions from other stores to be shared. The dedicated portal, for example, registers success stories and solutions from other stores in a database and shares them. For example, the dedicated portal with a function for linking with other stores can share solutions from other stores. This allows solutions from other stores to be shared.

[0034] The generative AI provides detailed guidance on the steps to implement a solution to a problem. For example, the generative AI presents step-by-step procedures, making it easy for users to implement. For example, the generative AI has the ability to provide detailed guidance on the steps to implement a solution to a problem. This allows for detailed guidance on the steps to implement a solution.

[0035] Generative AI not only solves problems but also suggests preventative measures. For example, generative AI suggests preventative measures before a problem occurs based on past data. For example, generative AI has the ability to not only solve problems but also suggest preventative measures. This makes it possible to suggest preventative measures for problems.

[0036] Generative AI works with other AI systems to execute solutions. For example, generative AI works with robotic arms and automation systems to automate repair procedures. For example, generative AI has the ability to work with other AI systems to execute solutions. This allows it to execute solutions in collaboration with other AI systems.

[0037] The generation AI identifies the cause of the cancellation and proposes measures to prevent recurrence. For example, the generation AI analyzes the cause of the cancellation and suggests improvements to the system. For example, the generation AI has the function to identify the cause of the cancellation and propose measures to prevent recurrence. This makes it possible to propose measures to prevent cancellations from recurring.

[0038] The generative AI evaluates the effectiveness of countermeasures and improves the accuracy of its suggestions based on the feedback. For example, the generative AI collects feedback from users after implementing countermeasures and uses this as learning data for the AI. For example, the generative AI has the function of evaluating the effectiveness of countermeasures and improving the accuracy of its suggestions based on the feedback. This allows for improved accuracy of suggestions.

[0039] The generation AI not only identifies the cause of the cancellation, but also misunderstandings about other services. For example, the generation AI analyzes the cause when a customer makes an incorrect operation and proposes improvements. For example, the generation AI has the ability to identify not only the cause of the cancellation, but also misunderstandings about other services. This makes it possible to identify misunderstandings about other services as well.

[0040] The generative AI executes countermeasures in cooperation with other AI systems. The generative AI, for example, cooperates with a customer support system to achieve a rapid response. For example, the generative AI has the function of executing countermeasures in cooperation with other AI systems. This allows the generative AI to execute countermeasures in cooperation with other AI systems.

[0041] Generative AI identifies the cause of a failure and proposes preventive measures. For example, generative AI analyzes the cause of a failure and provides regular maintenance and checklists. For example, generative AI has the function of identifying the cause of a failure and proposing preventive measures. This makes it possible to propose preventive measures for failures.

[0042] The generative AI evaluates the effectiveness of repair procedures and improves the accuracy of its suggestions based on the feedback. For example, the generative AI collects feedback from users after performing repair procedures and uses this as learning data for the AI. For example, the generative AI has the function of evaluating the effectiveness of repair procedures and improving the accuracy of its suggestions based on the feedback. This makes it possible to improve the accuracy of repair procedure suggestions.

[0043] Generative AI can identify not only terminal failures but also failures in other equipment. Generative AI can analyze the causes of failures in equipment such as refrigerators and air conditioners, and propose repair procedures. For example, generative AI has the ability to identify not only terminal failures but also failures in other equipment. This makes it possible to identify failures in other equipment as well.

[0044] The generative AI executes repair procedures in cooperation with other AI systems. For example, the generative AI cooperates with robotic arms and automation systems to automate repair procedures. For example, the generative AI has the function of executing repair procedures in cooperation with other AI systems. This allows the generative AI to execute repair procedures in cooperation with other AI systems.

[0045] The history management system evaluates the effectiveness of past solutions to problems and improves the solutions based on the feedback. For example, the history management system collects feedback from users after the solutions are implemented and uses the feedback as learning data for the system. For example, the history management system has a function for evaluating the effectiveness of past solutions to problems and improving the solutions based on the feedback. This makes it possible to evaluate the effectiveness of past solutions to problems and improve the solutions.

[0046] The history management system analyzes the frequency and trends of problems and proposes preventive measures. For example, if a particular problem occurs frequently, the history management system identifies the cause and proposes preventive measures. For example, the history management system has a function to analyze the frequency and trends of problems and propose preventive measures. This makes it possible to analyze the frequency and trends of problems and propose preventive measures.

[0047] The history management system cooperates with other stores and shares solutions to problems from other stores. The history management system, for example, registers success stories and solutions from other stores in a database and shares them. For example, the history management system has a function for cooperating with other stores and sharing solutions to problems from other stores. This allows solutions to problems from other stores to be shared.

[0048] The history management system automatically updates the problem solutions and provides the latest information. The history management system updates the database, for example, every time a new solution is added. For example, the history management system has a function for automatically updating the problem solutions and providing the latest information. This makes it possible to automatically update the problem solutions and provide the latest information.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The problem-solving system can also be equipped with a prediction unit that predicts the occurrence of problems. The prediction unit analyzes past data and identifies situations in which problems are likely to occur. For example, if problems occur frequently during a specific time period or a specific campaign period, it can take measures in advance for that time period or period. In addition, if problems are likely to occur when a specific staff member is in charge, the prediction unit can provide additional training to that staff member. Furthermore, if a specific terminal or piece of equipment is prone to breakdowns, the prediction unit can adjust the maintenance schedule for that terminal or equipment.

[0051] The problem-solving system can also be equipped with a resource management unit that automatically allocates the resources needed to solve a problem. The resource management unit allocates appropriate staff and equipment depending on the type and urgency of the problem. For example, if a terminal malfunctions, the resource management unit can automatically dispatch the nearest technical staff member. The resource management unit can also automatically arrange for the parts and tools needed to solve the problem. Furthermore, the resource management unit can predict the time required to solve the problem and adjust the schedule so as not to affect other work.

[0052] The problem solving system may further include a feedback collection unit that collects feedback regarding the solution of a problem. The feedback collection unit collects feedback from staff and customers after a problem is solved. For example, it may collect evaluations of the effectiveness of the solution and the speed of the response. The feedback collection unit may also analyze the collected feedback to identify areas for improvement in the system. Furthermore, the feedback collection unit may improve staff training programs based on the collected feedback.

[0053] The problem-solving system can also be equipped with a knowledge sharing unit that shares knowledge about solving problems. The knowledge sharing unit registers past problem-solving solutions and success stories in a database so that other staff can refer to them. For example, it can record in detail the solutions and response procedures for a specific problem so that other staff can refer to them when they encounter a similar problem. The knowledge sharing unit can also hold regular training sessions for staff to share new solutions and techniques. Furthermore, the knowledge sharing unit can strengthen cooperation with other stores and share success stories from other stores.

[0054] The problem-solving system may also include a data visualization unit that visualizes data related to problem resolution. The data visualization unit displays the occurrence and resolution status of problems in graphs and charts, allowing staff to understand the situation at a glance. For example, the number and type of problems that occurred during a specific period may be displayed in a bar graph. The data visualization unit may also display the time and resources required to resolve problems in a pie chart. Furthermore, the data visualization unit may display the trend in problem occurrence and resolution in a line graph, which can be used as a reference for planning future countermeasures.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The portal centralizes the management of problems that occur in stores. For example, it is designed to be easily accessible for store staff, allowing them to easily report problems and search for solutions. It also manages the history of problem resolution and allows them to refer to past solutions. Step 2: The generative AI proposes appropriate solutions to problems reported on the portal. For example, if an incorrect campaign registration occurs, the AI ​​analyzes the problem and suggests the correct registration method and correction steps. If a cancellation occurs due to a customer service misunderstanding, the AI ​​analyzes the cause and proposes appropriate countermeasures. Furthermore, if a device malfunctions, the AI ​​analyzes the cause of the malfunction and proposes repair methods or alternative solutions. This reduces the burden on store staff and improves customer satisfaction.

[0057] (Example 2) The problem-solving system according to an embodiment of the present invention is a system that uses a dedicated portal and generation AI to solve problems that occur in stores. This system provides support for quickly and efficiently resolving problems such as incorrect campaign registration, cancellations due to incorrect customer service recognition, and terminal malfunctions. As a result, the problem-solving system can quickly and efficiently solve various problems that occur in stores.

[0058] A problem-solving system according to an embodiment includes a dedicated portal and a generation AI. The dedicated portal centrally manages problems that occur in stores. For example, the dedicated portal is designed to be easily accessible by store staff, allowing them to easily report problems and search for solutions. The dedicated portal also manages a problem-solving history and allows them to refer to past solutions. The generation AI proposes appropriate solutions for problems reported to the dedicated portal. For example, if a campaign registration error occurs, the generation AI analyzes the problem and proposes the correct registration method and correction procedure. Furthermore, if a cancellation occurs due to a customer service misunderstanding, the generation AI analyzes the cause and proposes appropriate countermeasures. Furthermore, if a terminal malfunction occurs, the generation AI analyzes the cause of the malfunction and proposes repair methods or alternative solutions. As a result, the problem-solving system according to an embodiment can quickly and efficiently resolve problems that occur in stores. For example, the dedicated portal manages a problem-solving history and allows them to refer to past solutions. The generation AI generates solutions based on prompts containing details of the problem. This reduces the burden on store staff and improves customer satisfaction.

[0059] The dedicated portal is equipped with a voice input function, allowing store staff to report problems by voice. The dedicated portal allows staff to input problems by voice using, for example, a microphone, convert the problem into text, and register it on the portal. For example, voice recognition technology can be used to allow store staff to report problems by voice. This allows store staff to report problems without using their hands.

[0060] The dedicated portal automatically determines the priority of the problem and prioritizes dealing with problems of high urgency. The dedicated portal, for example, analyzes the content and scope of the problem and sets the priority. For example, in order to deal with problems of high urgency quickly, the dedicated portal has a function to automatically determine the priority of the problem. This allows problems of high urgency to be dealt with quickly.

[0061] The dedicated portal is equipped with an emotion estimation function, analyzes the emotions of staff members regarding the problem, and provides appropriate support. The dedicated portal uses, for example, text analysis to estimate emotions from the content entered by the staff members. For example, the emotion estimation function is used to analyze the emotions of staff members regarding the reported problem, and provides appropriate support. This makes it possible to provide support according to the emotions of the staff members.

[0062] The dedicated portal will be provided as a mobile app, allowing problems to be reported and resolved outside the store. For example, the dedicated portal will be developed as an app that can be accessed from a smartphone or tablet. For example, the dedicated portal will be provided as a mobile app, allowing problems to be reported and resolved outside the store. This will allow problems to be reported and resolved outside the store.

[0063] The dedicated portal has a function for linking with other stores, allowing solutions from other stores to be shared. The dedicated portal, for example, registers success stories and solutions from other stores in a database and shares them. For example, the dedicated portal with a function for linking with other stores can share solutions from other stores. This allows solutions from other stores to be shared.

[0064] The dedicated portal is equipped with an emotion estimation function, and analyzes the emotion of the user when reporting a problem in real time and provides appropriate feedback. The dedicated portal uses, for example, text analysis to estimate the emotion from the user's input. For example, using the emotion estimation function, the emotion of the user when reporting a problem is analyzed in real time and provides appropriate feedback. This makes it possible to provide feedback according to the user's emotion.

[0065] The generative AI provides detailed guidance on the steps to implement a solution to a problem. For example, the generative AI presents step-by-step procedures, making it easy for users to implement. For example, the generative AI has the ability to provide detailed guidance on the steps to implement a solution to a problem. This allows for detailed guidance on the steps to implement a solution.

[0066] The generative AI proposes solutions based on the user's emotions. For example, if the user is feeling stressed, the generative AI proposes a simple and quick solution. For example, the generative AI integrates emotion estimation functions to propose solutions based on the user's emotions. This allows it to propose solutions that correspond to the user's emotions.

[0067] Generative AI not only solves problems but also suggests preventative measures. For example, generative AI suggests preventative measures before a problem occurs based on past data. For example, generative AI has the ability to not only solve problems but also suggest preventative measures. This makes it possible to suggest preventative measures for problems.

[0068] Generative AI works with other AI systems to execute solutions. For example, generative AI works with robotic arms and automation systems to automate repair procedures. For example, generative AI has the ability to work with other AI systems to execute solutions. This allows it to execute solutions in collaboration with other AI systems.

[0069] The generative AI suggests preventive measures based on the user's emotions. For example, if the user is feeling stressed, the generative AI suggests simple and quick preventive measures. For example, the generative AI integrates an emotion estimation function to suggest preventive measures based on the user's emotions. This allows it to suggest preventive measures that correspond to the user's emotions.

[0070] The generation AI identifies the cause of the cancellation and proposes measures to prevent recurrence. For example, the generation AI analyzes the cause of the cancellation and suggests improvements to the system. For example, the generation AI has the function to identify the cause of the cancellation and propose measures to prevent recurrence. This makes it possible to propose measures to prevent cancellations from recurring.

[0071] The generative AI evaluates the effectiveness of countermeasures and improves the accuracy of its suggestions based on the feedback. For example, the generative AI collects feedback from users after implementing countermeasures and uses this as learning data for the AI. For example, the generative AI has the function of evaluating the effectiveness of countermeasures and improving the accuracy of its suggestions based on the feedback. This allows for improved accuracy of suggestions.

[0072] The generative AI proposes countermeasures based on the customer's emotions at the time of cancellation. For example, if the customer is angry, the generative AI will propose a quick response or special services. For example, the generative AI integrates emotion estimation functions to propose countermeasures based on the customer's emotions at the time of cancellation. This makes it possible to propose countermeasures that correspond to the customer's emotions.

[0073] The generation AI not only identifies the cause of the cancellation, but also misunderstandings about other services. For example, the generation AI analyzes the cause when a customer makes an incorrect operation and proposes improvements. For example, the generation AI has the ability to identify not only the cause of the cancellation, but also misunderstandings about other services. This makes it possible to identify misunderstandings about other services as well.

[0074] The generative AI executes countermeasures in cooperation with other AI systems. The generative AI, for example, cooperates with a customer support system to achieve a rapid response. For example, the generative AI has the function of executing countermeasures in cooperation with other AI systems. This allows the generative AI to execute countermeasures in cooperation with other AI systems.

[0075] Generative AI identifies the cause of a failure and proposes preventive measures. For example, generative AI analyzes the cause of a failure and provides regular maintenance and checklists. For example, generative AI has the function of identifying the cause of a failure and proposing preventive measures. This makes it possible to propose preventive measures for failures.

[0076] The generative AI evaluates the effectiveness of repair procedures and improves the accuracy of its suggestions based on the feedback. For example, the generative AI collects feedback from users after performing repair procedures and uses this as learning data for the AI. For example, the generative AI has the function of evaluating the effectiveness of repair procedures and improving the accuracy of its suggestions based on the feedback. This makes it possible to improve the accuracy of repair procedure suggestions.

[0077] The generative AI proposes countermeasures based on the emotions of staff members when a breakdown occurs. For example, if a staff member is feeling stressed, the generative AI proposes simple and quick countermeasures. For example, the generative AI integrates an emotion estimation function to propose countermeasures based on the emotions of staff members when a breakdown occurs. This makes it possible to propose countermeasures that are appropriate for the emotions of staff members.

[0078] Generative AI can identify not only terminal failures but also failures in other equipment. Generative AI can analyze the causes of failures in equipment such as refrigerators and air conditioners, and propose repair procedures. For example, generative AI has the ability to identify not only terminal failures but also failures in other equipment. This makes it possible to identify failures in other equipment as well.

[0079] The generative AI executes repair procedures in cooperation with other AI systems. For example, the generative AI cooperates with robotic arms and automation systems to automate repair procedures. For example, the generative AI has the function of executing repair procedures in cooperation with other AI systems. This allows the generative AI to execute repair procedures in cooperation with other AI systems.

[0080] The generative AI suggests preventive measures based on the emotions of staff members. For example, if a staff member is feeling stressed, the generative AI suggests simple and quick preventive measures. For example, the generative AI integrates an emotion estimation function to suggest preventive measures based on the emotions of staff members. This makes it possible to suggest preventive measures that correspond to the emotions of staff members.

[0081] The history management system evaluates the effectiveness of past solutions to problems and improves the solutions based on the feedback. For example, the history management system collects feedback from users after the solutions are implemented and uses the feedback as learning data for the system. For example, the history management system has a function for evaluating the effectiveness of past solutions to problems and improving the solutions based on the feedback. This makes it possible to evaluate the effectiveness of past solutions to problems and improve the solutions.

[0082] The history management system analyzes the frequency and trends of problems and proposes preventive measures. For example, if a particular problem occurs frequently, the history management system identifies the cause and proposes preventive measures. For example, the history management system has a function to analyze the frequency and trends of problems and propose preventive measures. This makes it possible to analyze the frequency and trends of problems and propose preventive measures.

[0083] The history management system analyzes emotional data from past problem solving and proposes solutions based on the emotions. The history management system, for example, records and analyzes the stress and satisfaction felt by staff when implementing solutions. For example, the history management system integrates an emotion estimation function, analyzes emotional data from past problem solving, and has the function of proposing solutions based on the emotions. This makes it possible to propose solutions based on past emotional data.

[0084] The history management system cooperates with other stores and shares solutions to problems from other stores. The history management system, for example, registers success stories and solutions from other stores in a database and shares them. For example, the history management system has a function for cooperating with other stores and sharing solutions to problems from other stores. This allows solutions to problems from other stores to be shared.

[0085] The history management system automatically updates the problem solutions and provides the latest information. The history management system updates the database, for example, every time a new solution is added. For example, the history management system has a function for automatically updating the problem solutions and providing the latest information. This makes it possible to automatically update the problem solutions and provide the latest information.

[0086] The history management system analyzes the emotion data of other stores and shares solutions based on the emotions. The history management system, for example, records and analyzes the stress and satisfaction felt by staff when implementing solutions. For example, the history management system integrates an emotion estimation function and has a function to analyze the emotion data of other stores and share solutions based on the emotions. This makes it possible to share solutions based on the emotion data of other stores.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The problem-solving system can also be equipped with a prediction unit that predicts the occurrence of problems. The prediction unit analyzes past data and identifies situations in which problems are likely to occur. For example, if problems occur frequently during a specific time period or a specific campaign period, it can take measures in advance for that time period or period. In addition, if problems are likely to occur when a specific staff member is in charge, the prediction unit can provide additional training to that staff member. Furthermore, if a specific terminal or piece of equipment is prone to breakdowns, the prediction unit can adjust the maintenance schedule for that terminal or equipment.

[0089] The problem-solving system can also be equipped with a resource management unit that automatically allocates the resources needed to solve a problem. The resource management unit allocates appropriate staff and equipment depending on the type and urgency of the problem. For example, if a terminal malfunctions, the resource management unit can automatically dispatch the nearest technical staff member. The resource management unit can also automatically arrange for the parts and tools needed to solve the problem. Furthermore, the resource management unit can predict the time required to solve the problem and adjust the schedule so as not to affect other work.

[0090] The problem solving system may further include a feedback collection unit that collects feedback regarding the solution of a problem. The feedback collection unit collects feedback from staff and customers after a problem is solved. For example, it may collect evaluations of the effectiveness of the solution and the speed of the response. The feedback collection unit may also analyze the collected feedback to identify areas for improvement in the system. Furthermore, the feedback collection unit may improve staff training programs based on the collected feedback.

[0091] The problem-solving system can also be equipped with a knowledge sharing unit that shares knowledge about solving problems. The knowledge sharing unit registers past problem-solving solutions and success stories in a database so that other staff can refer to them. For example, it can record in detail the solutions and response procedures for a specific problem so that other staff can refer to them when they encounter a similar problem. The knowledge sharing unit can also hold regular training sessions for staff to share new solutions and techniques. Furthermore, the knowledge sharing unit can strengthen cooperation with other stores and share success stories from other stores.

[0092] The problem-solving system may also include a data visualization unit that visualizes data related to problem resolution. The data visualization unit displays the occurrence and resolution status of problems in graphs and charts, allowing staff to understand the situation at a glance. For example, the number and type of problems that occurred during a specific period may be displayed in a bar graph. The data visualization unit may also display the time and resources required to resolve problems in a pie chart. Furthermore, the data visualization unit may display the trend in problem occurrence and resolution in a line graph, which can be used as a reference for planning future countermeasures.

[0093] The problem-solving system may further include an emotion estimation unit that estimates the user's emotion and proposes a solution based on the estimated emotion. The emotion estimation unit estimates the user's emotion from the user's input content or voice and proposes a solution based on the emotion. For example, if the user is feeling stressed, it can propose a simple and quick solution. Also, if the user is feeling anxious, it can provide a detailed explanation or support. Furthermore, the emotion estimation unit can provide appropriate feedback based on the user's emotion.

[0094] The problem-solving system may further include an emotion estimation unit that estimates the emotion of the staff member and provides support based on the estimated emotion. The emotion estimation unit estimates the emotion from the staff member's input or voice and provides support according to the emotion. For example, if the staff member is tired, it can display a message encouraging them to take a break. Also, if the staff member is feeling stressed, it can provide advice on how to relax. Furthermore, the emotion estimation unit can suggest an appropriate training program based on the staff member's emotion.

[0095] The problem-solving system may further include an emotion estimation unit that estimates the customer's emotion and proposes a countermeasure based on the estimated emotion. The emotion estimation unit estimates the emotion from the customer's input or voice and proposes a countermeasure according to the emotion. For example, if the customer is angry, it may suggest a quick response or special service. Also, if the customer is dissatisfied, it may provide a detailed explanation or compensation. Furthermore, the emotion estimation unit may provide appropriate feedback based on the customer's emotion.

[0096] The problem-solving system may further include an emotion estimation unit that estimates the emotion of the staff member and sets the priority of the problem based on the estimated emotion. The emotion estimation unit estimates the emotion from the staff member's input or voice and sets the priority of the problem based on the emotion. For example, if the staff member is feeling highly stressed, the problem can be dealt with as a priority. Also, if the staff member is feeling anxious, additional support can be provided for the problem. Furthermore, the emotion estimation unit can allocate appropriate resources based on the staff member's emotion.

[0097] The problem-solving system may further include an emotion estimation unit that estimates the customer's emotion and proposes a solution to the problem based on the estimated emotion. The emotion estimation unit estimates the emotion from the customer's input or voice and proposes a solution based on the emotion. For example, if the customer feels anxious, it can provide detailed explanations and support. Also, if the customer is satisfied, it can suggest additional services or benefits. Furthermore, the emotion estimation unit can provide appropriate feedback based on the customer's emotion.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The portal centralizes the management of problems that occur in stores. For example, it is designed to be easily accessible for store staff, allowing them to easily report problems and search for solutions. It also manages the history of problem resolution and allows them to refer to past solutions. Step 2: The generative AI proposes appropriate solutions to problems reported on the portal. For example, if an incorrect campaign registration occurs, the AI ​​analyzes the problem and suggests the correct registration method and correction steps. If a cancellation occurs due to a customer service misunderstanding, the AI ​​analyzes the cause and proposes appropriate countermeasures. Furthermore, if a device malfunctions, the AI ​​analyzes the cause of the malfunction and proposes repair methods or alternative solutions. This reduces the burden on store staff and improves customer satisfaction.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A portal for centrally managing issues that occur in stores, a generation AI that proposes appropriate solutions to the problems reported to the portal; A system characterized by:

2. The portal Equipped with a voice input function, store staff can report the above problem by voice 2. The system of claim 1.

3. The portal It is provided as a mobile app, allowing you to report and resolve the issue outside the store.

2. The system of claim 1.

4. The generating AI is Provide detailed instructions on how to implement the solution to the problem 2. The system of claim 1.

5. The generating AI is Identify the reasons for cancellations and propose measures to prevent recurrence 2. The system of claim 1.

6. The history management system is Evaluate the effectiveness of past solutions to problems and improve those solutions based on feedback 2. The system of claim 1.

7. The portal Equipped with an emotion estimation function, it analyzes staff's emotions regarding the issue and provides appropriate support 2. The system of claim 1.

8. The generating AI is Suggest preventative measures based on user sentiment 2. The system of claim 1.

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