System

Abstract

An object of a system according to an embodiment is to efficiently provide a method of using a model and an operation explanation and reduce a burden on a store staff.SOLUTION: A system according to an embodiment includes a generation AI, a video providing unit, a voice providing unit, and a question handling unit. The video providing part provides the usage and operation explanation of the model by video. The voice providing unit provides a method of using the model and an operation explanation by voice. The question corresponding part corresponds to a general question.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] With conventional technology, store staff had to directly explain how to use the machine and how to operate it, which led to issues such as staff shortages and reduced efficiency.

[0005] The system according to the embodiment aims to efficiently provide instructions on how to use a machine and how to operate it, thereby reducing the burden on store staff. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a video providing unit, an audio providing unit, and a question responding unit. The video providing unit provides video instructions on how to use the model and operation instructions. The audio providing unit provides audio instructions on how to use the model and operation instructions. The question responding unit responds to general questions. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide instructions on how to use a model and how to operate it, thereby reducing the burden on store staff. [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 system according to an embodiment of the present invention uses generative AI to provide video and audio instructions on how to use a mobile phone and how to operate it. This allows for immediate responses to general questions that do not require direct response from store staff. This allows the system to allow customers to obtain information at their own pace, allowing store staff to focus on more specialized tasks. This enables efficient store operations even with a staff shortage. Furthermore, the introduction of generative AI is expected to provide flexible and efficient support while imitating natural human conversations. This will effectively evolve the operations of mobile phone shops while also meeting the psychological needs of customers.

[0029] The system according to the embodiment includes a generation AI, a video providing unit, an audio providing unit, and a question response unit. The generation AI provides instructions on how to use a model and operation instructions. For example, the generation AI instantly answers general questions, such as how to set up a new smartphone or how to install an app. The generation AI generates appropriate video and audio based on prompts containing instructions on what the user wants the generation AI to do. For example, in response to a prompt such as, "Please tell me how to set up my new smartphone," the generation AI provides specific instructions in the form of video and audio. The video providing unit provides video generated by the generation AI. For example, the video providing unit displays operation procedures on the smartphone screen. The video providing unit also allows the user to play, stop, rewind, and fast-forward the video. The audio providing unit provides audio generated by the generation AI. For example, the audio providing unit provides audio guidance on operation procedures from the smartphone speaker. The audio providing unit also allows the user to play, stop, rewind, and fast-forward the audio. The question response unit responds to general questions. For example, if a user asks a question such as "Can you tell me more about this function?", the generation AI will provide an appropriate answer to the question. This allows the system to use the generation AI to provide instructions on how to use the model and how to operate it, reducing the burden on store staff and enabling efficient store operations.

[0030] The generation AI can learn a user's past operation history and provide individually customized operating instructions. For example, the generation AI analyzes a user's past operation history to identify frequently used functions and operations. For example, a user who frequently uses a particular app can be provided with detailed operating instructions for that app. The generation AI can also identify operations that the user has difficulty with based on the user's operation history and provide customized instructions for those operations. For example, if a user has difficulty changing a particular setting, the generation AI can provide detailed instructions for changing that setting. Furthermore, the generation AI can suggest new features and apps that the user might be interested in based on the user's operation history. For example, if a user frequently uses photo editing apps, the generation AI can introduce new photo editing apps. This makes it possible to provide individually customized operating instructions based on the user's past operation history.

[0031] Generative AI can monitor user operations in real time and immediately suggest how to correct them if an error occurs. For example, generative AI can monitor user operations in real time and immediately suggest how to correct them if an error is detected. For example, if an incorrect setting change is made, the correct procedure is displayed. Furthermore, when a user makes an error, generative AI analyzes the cause of the error and provides advice on how to prevent it from happening again. For example, if a user accidentally deletes an app, the generative AI will prompt the user to recheck the app deletion procedure. Furthermore, when a user makes an error, generative AI will suggest measures to minimize the impact of the error. For example, if the settings are accidentally reset, the generative AI will guide the user on how to restore the settings from a backup. This makes it possible to detect errors in real time and immediately suggest how to correct them.

[0032] The generating AI can use AR technology to overlay and display operating instructions on a device. For example, the generating AI uses AR technology to overlay and display operating instructions on an actual device. For example, it overlays operating procedures on a smartphone screen. The generating AI also uses AR technology to display operating procedures and guides on the actual screen of the device operated by the user. For example, it displays operating procedures on a screen viewed through a camera. Furthermore, the generating AI uses AR technology to display operating procedures on the actual physical buttons and interface of the device operated by the user. For example, it displays operating procedures on top of buttons. In this way, it is possible to overlay and display operating instructions on an actual device using AR technology.

[0033] The generation AI can automatically generate operation instructions that correspond to different languages ​​and cultures, making it possible to accommodate global users. For example, the generation AI can automatically generate operation instructions that correspond to different languages ​​and provide them to global users. For example, operation instructions can be provided in multiple languages ​​such as English, French, and Chinese. The generation AI can also automatically generate operation instructions that correspond to different cultures and provide explanations that take into account the user's cultural background. For example, the generation AI can provide explanations that include operating procedures and precautions that take cultural differences into account. Furthermore, in order to provide operation instructions that correspond to different languages ​​and cultures, the generation AI can automatically generate explanations that are tailored to the appropriate language and culture based on the user's language settings and regional information. This makes it possible to automatically generate operation instructions that correspond to different languages ​​and cultures and accommodate global users.

[0034] The generation AI can analyze the user's learning speed and provide information at an optimal pace. The generation AI can, for example, analyze the user's learning speed and provide information at an optimal pace. For example, it can provide information at a slower pace to a user who is a slow learner. The generation AI can also adjust the way information is provided according to the user's learning speed. For example, it can provide information that concisely summarizes the main points to a user who is a fast learner. The generation AI can also adjust the frequency of information provision according to the user's learning speed. For example, it can provide information in stages to a user who is a slow learner. This makes it possible to provide information at an optimal pace according to the user's learning speed.

[0035] The generation AI can evaluate the user's level of understanding in real time and add supplementary explanations as necessary. The generation AI, for example, evaluates the user's level of understanding in real time and adds supplementary explanations as necessary. For example, if the level of understanding is low, it provides a detailed explanation. The generation AI also adjusts the content of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is high, it provides a concise supplementary explanation. Furthermore, the generation AI changes the format of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is low, it provides a video tutorial. This makes it possible to provide necessary supplementary explanations in real time according to the user's level of understanding.

[0036] The generation AI can deliver information in stages according to the user's schedule. For example, the generation AI analyzes the user's schedule and delivers information in stages at the optimal timing. For example, it provides information according to the user's free time. The generation AI also adjusts the frequency of information delivery according to the user's schedule. For example, it reduces the frequency of information delivery during busy periods and increases the frequency of delivery during periods when the user has less free time. Furthermore, the generation AI changes the format of information delivery according to the user's schedule. For example, it provides information in a format that can be understood in a short amount of time. This allows information to be delivered in stages according to the user's schedule.

[0037] The generation AI can select and provide an information format (text, audio, video) according to the user's preferences. The generation AI, for example, selects an information format according to the user's preferences and provides the information. For example, it selects the format the user prefers from text, audio, or video. The generation AI also adjusts the method of providing information according to the user's preferences. For example, it provides video to a user who prefers visual information, and audio to a user who prefers auditory information. Furthermore, the generation AI adjusts the frequency of information provision according to the user's preferences. For example, it provides information frequently to a user who prefers detailed information, and provides summary information to a user who prefers concise information. This makes it possible to provide information in an information format that suits the user's preferences.

[0038] The generation AI can manage store staff schedules and assign specialized tasks at the optimal time. For example, the generation AI can analyze store staff schedules and assign specialized tasks at the optimal time. For example, it can assign tasks based on staff's free time. The generation AI can also adjust the priority of tasks according to the store staff schedules. For example, it can assign tasks with a high degree of urgency first. Furthermore, the generation AI can change the method of task assignment according to the store staff schedules. For example, it can assign tasks based on the staff's skills and experience. This makes it possible to manage store staff schedules and assign specialized tasks at the optimal time.

[0039] Generative AI can evaluate the skill levels of store staff and suggest appropriate training programs. For example, generative AI can evaluate the skill levels of store staff and suggest individually customized training programs. For example, it can provide training to strengthen specific skills to staff who lack them. Generative AI can also adjust the content of the training program according to the skill level of the store staff. For example, it can provide basic training for beginners to advanced training for advanced staff. Furthermore, generative AI can change the format of the training program according to the skill level of the store staff. For example, it can provide online courses, workshops, on-the-job training, etc. This makes it possible to suggest appropriate training programs according to the skill level of the store staff.

[0040] Generative AI can automatically record the work of store staff and analyze their work efficiency. For example, generative AI can build a system that automatically records the work of store staff and analyzes their work efficiency. For example, it can record the progress of work and the time it is completed. Generative AI can also evaluate work efficiency based on the work records of store staff. For example, it can analyze work time and error rates. Furthermore, generative AI can make suggestions to improve work efficiency. For example, it can suggest improvements to work procedures or the introduction of tools. This makes it possible to automatically record the work of store staff and analyze their work efficiency.

[0041] Generative AI can share store staff's work with other staff members, improving the efficiency of the entire team. For example, generative AI can build a system that allows store staff members to share their work with other staff members, improving the efficiency of the entire team. For example, they can share the progress of work and work together to get the job done. Generative AI can also prevent duplication of work by sharing the work content of store staff with other staff members. For example, it can adjust so that multiple staff members do not perform the same work. Furthermore, by sharing store staff members' work with other staff members, generative AI can improve the skills of the entire team. For example, experienced staff members can teach new staff members their work. This allows store staff members to share their work with other staff members, improving the efficiency of the entire team.

[0042] Generative AI can analyze store visitor data and propose optimal staff allocation. For example, generative AI can analyze store visitor data and build a system that proposes optimal staff allocation. For example, it can allocate staff according to peak visitor times. Generative AI can also adjust staff allocation based on store visitor data. For example, if customers are concentrated during a specific time period, it can increase the number of staff during that time period. Furthermore, generative AI can propose allocation based on staff skills and experience based on store visitor data. For example, it can allocate experienced staff during peak hours. This makes it possible to analyze store visitor data and propose optimal staff allocation.

[0043] Generative AI can automate store inventory management and ensure efficient product replenishment. For example, generative AI analyzes store inventory data and builds a system for efficient product replenishment. For example, it automatically replenishes products that are running low on stock. Generative AI also adjusts the timing of product replenishment based on the store's inventory data. For example, it replenishes best-selling products at the appropriate time so that they do not run out of stock. Furthermore, generative AI adjusts the amount of product replenishment based on the store's inventory data. For example, it increases or decreases the amount replenished depending on demand. This allows for automated store inventory management and efficient product replenishment.

[0044] Generative AI can strengthen collaboration with other stores and promote resource sharing. For example, generative AI can build a system that strengthens collaboration with other stores and promotes resource sharing. For example, it can share inventory and staff. Generative AI can also work to efficiently use resources by collaborating with other stores. For example, it can replenish inventory from other stores to adjust inventory surpluses and shortages. Furthermore, generative AI can work to reduce costs by sharing resources with other stores. For example, it can reduce costs by using common resources. This can strengthen collaboration with other stores and promote resource sharing.

[0045] Generative AI can optimize a store's energy consumption and reduce costs. For example, generative AI can analyze a store's energy consumption data and build a system that proposes an optimal energy consumption plan. For example, by avoiding peak energy consumption times. Generative AI can also make suggestions to improve energy efficiency based on the store's energy consumption data. For example, it can suggest the introduction of highly energy-efficient equipment. Furthermore, generative AI can make suggestions to reduce energy waste based on the store's energy consumption data. For example, it can encourage the use of unnecessary lighting and equipment to be reduced. This makes it possible to optimize a store's energy consumption and reduce costs.

[0046] The generative AI can learn from a user's past interaction history and provide more personalized responses. For example, the generative AI analyzes a user's past interaction history and provides individually customized responses. For example, it provides related information based on questions asked in the past. The generative AI can also identify a user's preferences and interests based on the user's interaction history and provide responses accordingly. For example, a user who is interested in a particular topic can be provided with information related to that topic. Furthermore, the generative AI can identify areas where the user has had difficulty in the past based on the user's interaction history and provide enhanced support for those areas. For example, a user who has difficulty with a particular operation can be provided with detailed explanations of that operation. This allows for more personalized responses based on the user's past interaction history.

[0047] Generative AI can analyze a user's non-verbal communication (facial expressions, gestures) to achieve more natural dialogue. For example, generative AI can analyze a user's facial expressions to achieve a natural dialogue that corresponds to their emotional state. For example, when the user smiles, it returns a positive response. Generative AI can also analyze a user's gestures to smooth the flow of the dialogue. For example, when the user nods, it moves on to the next explanation. Furthermore, generative AI can analyze the user's posture and movements to adjust the content of the dialogue. For example, if the user is relaxed, it will speak in a casual tone. This allows the user's non-verbal communication to be analyzed and more natural dialogue to be achieved.

[0048] Generative AI can seamlessly take over conversations between different devices and provide consistent support. For example, generative AI can build a system that seamlessly takes over conversations between different devices and provides consistent support. For example, a conversation can be taken over from a smartphone to a tablet. Generative AI can also synchronize the conversation history between different devices, allowing users to receive consistent support regardless of which device they use. For example, a conversation started on a desktop computer can be continued on a smartphone. Furthermore, when taking over a conversation between different devices, generative AI retains the content and progress of the conversation. For example, it can smoothly proceed with the next conversation based on the content of the previous conversation. This makes it possible to seamlessly take over conversations between different devices and provide consistent support.

[0049] The generation AI can select a conversation style (formal or casual) according to the user's preferences. For example, the generation AI selects a conversation style according to the user's preferences and responds formally or casually. For example, formal language is used in business situations. The generation AI also adjusts the tone and language of the conversation according to the user's preferences. For example, it responds in a friendly tone to a user who prefers casual conversations. Furthermore, the generation AI adjusts the content of the conversation according to the user's preferences. For example, it provides detailed information to a user who prefers detailed explanations, and provides information that summarizes the main points to a user who prefers concise explanations. This allows the generation AI to respond in a conversation style that suits the user's preferences.

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

[0051] Based on the user's operation history, the generative AI can automatically customize and place the user's frequently used functions and apps on the home screen. For example, it can place frequently used apps in a prominent position on the home screen. The generative AI can also hide unnecessary apps and functions based on the user's operation history. For example, it can automatically organize apps that have not been used for a long time into folders. Furthermore, the generative AI can add frequently used settings and shortcuts to the home screen based on the user's operation history. For example, it can make frequently used Wi-Fi settings and Bluetooth on / off settings easily accessible. This allows the home screen to be customized based on the user's operation history, providing a more user-friendly interface.

[0052] Based on the user's operation history, the generative AI can suggest new apps and features that the user might be interested in. For example, for a user who frequently uses photo editing apps, it can suggest new photo editing apps and features. The generative AI can also suggest events and campaigns that the user might be interested in based on the user's operation history. For example, for a user who frequently uses music apps, it can suggest music-related events and campaigns. Furthermore, the generative AI can suggest news and articles that the user might be interested in based on the user's operation history. For example, for a user who frequently uses sports apps, it can suggest the latest sports news and articles. This makes it possible to suggest new apps and features that the user might be interested in based on the user's operation history.

[0053] Based on the user's operation history, the generation AI can automatically create shortcuts for frequently used functions and apps. For example, it can add shortcuts for frequently used apps to the home screen. The generation AI can also create shortcuts for frequently used settings and functions based on the user's operation history. For example, it can add shortcuts for frequently used Wi-Fi settings or Bluetooth on / off to the home screen. Furthermore, the generation AI can automatically organize shortcuts for frequently used functions and apps based on the user's operation history. For example, it can organize shortcuts for frequently used apps into folders. This allows shortcuts to be automatically created based on the user's operation history, providing a more user-friendly interface.

[0054] The generating AI can use AR technology to overlay operating instructions on the device. For example, it can overlay operating instructions on the screen of a smartphone. The generating AI can also use AR technology to display operating instructions and guides on the actual screen of the device operated by the user. For example, it can display operating instructions on a screen viewed through a camera. Furthermore, the generating AI can use AR technology to display operating instructions for the actual physical buttons and interface of the device operated by the user. For example, it can display operating instructions on top of a button. This makes it possible to overlay operating instructions on the actual device using AR technology.

[0055] The generation AI can automatically generate operating instructions that correspond to different languages ​​and cultures, making it possible to accommodate global users. For example, it can provide operating instructions in multiple languages, such as English, French, and Chinese. The generation AI can also automatically generate operating instructions that correspond to different cultures and provide explanations that take into account the user's cultural background. For example, it can provide explanations that include operating procedures and precautions that take cultural differences into account. Furthermore, in order to provide operating instructions that correspond to different languages ​​and cultures, the generation AI can automatically generate instructions that are tailored to the appropriate language and culture based on the user's language settings and regional information. This makes it possible to automatically generate operating instructions that correspond to different languages ​​and cultures, making it possible to accommodate global users.

[0056] The generation AI can analyze the user's learning speed and provide information at an optimal pace. For example, it can provide information at a slow pace to a user who is a slow learner. The generation AI can also adjust the way information is provided according to the user's learning speed. For example, it can provide information that summarizes the main points concisely to a user who is a fast learner. Furthermore, the generation AI can adjust the frequency of information provision according to the user's learning speed. For example, it can provide information in stages to a user who is a slow learner. This makes it possible to provide information at an optimal pace according to the user's learning speed.

[0057] The generation AI can evaluate the user's level of understanding in real time and add supplementary explanations as necessary. For example, if the level of understanding is low, it can provide detailed explanations. The generation AI can also adjust the content of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is high, it can provide concise supplementary explanations. Furthermore, the generation AI can change the format of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is low, it can provide a video tutorial. This makes it possible to provide necessary supplementary explanations in real time according to the user's level of understanding.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The generating AI provides instructions on how to use the device and how to operate it based on prompts containing instructions on what the user wants the generating AI to do. For example, in response to the prompt "Please tell me how to set up my new smartphone," the generating AI generates specific instructions in the form of video and audio. Step 2: The video provider provides the video generated by the AI. For example, the video provider may display operating instructions on a smartphone screen, allowing the user to play, stop, rewind, and fast-forward the video. Step 3: The voice provider provides the voice generated by the AI. For example, the voice provider may provide voice instructions through the smartphone speaker, allowing the user to play, stop, rewind, and fast-forward the voice. Step 4: The question response section responds to general questions. For example, if a user asks, "Can you tell me more about this feature?", the generative AI will provide an appropriate answer to that question.

[0060] (Example 2) The system according to an embodiment of the present invention uses generative AI to provide video and audio instructions on how to use a mobile phone and how to operate it. This allows for immediate responses to general questions that do not require direct response from store staff. This allows the system to allow customers to obtain information at their own pace, allowing store staff to focus on more specialized tasks. This enables efficient store operations even with a staff shortage. Furthermore, the introduction of generative AI is expected to provide flexible and efficient support while imitating natural human conversations. This will effectively evolve the operations of mobile phone shops while also meeting the psychological needs of customers.

[0061] The system according to the embodiment includes a generation AI, a video providing unit, an audio providing unit, and a question response unit. The generation AI provides instructions on how to use a model and operation instructions. For example, the generation AI instantly answers general questions, such as how to set up a new smartphone or how to install an app. The generation AI generates appropriate video and audio based on prompts containing instructions on what the user wants the generation AI to do. For example, in response to a prompt such as, "Please tell me how to set up my new smartphone," the generation AI provides specific instructions in the form of video and audio. The video providing unit provides video generated by the generation AI. For example, the video providing unit displays operation procedures on the smartphone screen. The video providing unit also allows the user to play, stop, rewind, and fast-forward the video. The audio providing unit provides audio generated by the generation AI. For example, the audio providing unit provides audio guidance on operation procedures from the smartphone speaker. The audio providing unit also allows the user to play, stop, rewind, and fast-forward the audio. The question response unit responds to general questions. For example, if a user asks a question such as "Can you tell me more about this function?", the generation AI will provide an appropriate answer to the question. This allows the system to use the generation AI to provide instructions on how to use the model and how to operate it, reducing the burden on store staff and enabling efficient store operations.

[0062] The generation AI can learn a user's past operation history and provide individually customized operating instructions. For example, the generation AI analyzes a user's past operation history to identify frequently used functions and operations. For example, a user who frequently uses a particular app can be provided with detailed operating instructions for that app. The generation AI can also identify operations that the user has difficulty with based on the user's operation history and provide customized instructions for those operations. For example, if a user has difficulty changing a particular setting, the generation AI can provide detailed instructions for changing that setting. Furthermore, the generation AI can suggest new features and apps that the user might be interested in based on the user's operation history. For example, if a user frequently uses photo editing apps, the generation AI can introduce new photo editing apps. This makes it possible to provide individually customized operating instructions based on the user's past operation history.

[0063] Generative AI can monitor user operations in real time and immediately suggest how to correct them if an error occurs. For example, generative AI can monitor user operations in real time and immediately suggest how to correct them if an error is detected. For example, if an incorrect setting change is made, the correct procedure is displayed. Furthermore, when a user makes an error, generative AI analyzes the cause of the error and provides advice on how to prevent it from happening again. For example, if a user accidentally deletes an app, the generative AI will prompt the user to recheck the app deletion procedure. Furthermore, when a user makes an error, generative AI will suggest measures to minimize the impact of the error. For example, if the settings are accidentally reset, the generative AI will guide the user on how to restore the settings from a backup. This makes it possible to detect errors in real time and immediately suggest how to correct them.

[0064] Using its emotion estimation function, the generative AI can detect stress or confusion the user feels while operating the device and provide advice to help them relax. For example, the generative AI can analyze the user's facial expressions and tone of voice to detect stress or confusion while operating the device. For example, when it detects a confused expression, it can provide advice to take deep breaths to relax. The generative AI can also suggest relaxation techniques to reduce the stress or confusion the user feels while operating the device. For example, it can encourage the user to take a short break. Furthermore, the generative AI can suggest environmental adjustments to reduce the stress or confusion the user feels while operating the device. For example, it can guide the user on how to adjust the music or lighting while operating the device. This makes it possible to detect stress or confusion in the user and provide advice to help them relax.

[0065] The generating AI can use AR technology to overlay and display operating instructions on a device. For example, the generating AI uses AR technology to overlay and display operating instructions on an actual device. For example, it overlays operating procedures on a smartphone screen. The generating AI also uses AR technology to display operating procedures and guides on the actual screen of the device operated by the user. For example, it displays operating procedures on a screen viewed through a camera. Furthermore, the generating AI uses AR technology to display operating procedures on the actual physical buttons and interface of the device operated by the user. For example, it displays operating procedures on top of buttons. In this way, it is possible to overlay and display operating instructions on an actual device using AR technology.

[0066] The generation AI can automatically generate operation instructions that correspond to different languages ​​and cultures, making it possible to accommodate global users. For example, the generation AI can automatically generate operation instructions that correspond to different languages ​​and provide them to global users. For example, operation instructions can be provided in multiple languages ​​such as English, French, and Chinese. The generation AI can also automatically generate operation instructions that correspond to different cultures and provide explanations that take into account the user's cultural background. For example, the generation AI can provide explanations that include operating procedures and precautions that take cultural differences into account. Furthermore, in order to provide operation instructions that correspond to different languages ​​and cultures, the generation AI can automatically generate explanations that are tailored to the appropriate language and culture based on the user's language settings and regional information. This makes it possible to automatically generate operation instructions that correspond to different languages ​​and cultures and accommodate global users.

[0067] The generation AI uses its emotion estimation function to analyze the emotions of the user when receiving operation instructions, and can provide additional support at the optimal timing. For example, the generation AI uses its emotion estimation function to analyze the emotions of the user when receiving operation instructions in real time, and can provide additional support at the optimal timing. For example, if it detects a confused expression, it will add detailed explanations. The generation AI also adjusts the pace and content of the operation instructions according to the user's emotional state. For example, if the user is feeling impatient, it will slow down the pace of the explanations. Furthermore, the generation AI changes the format of the operation instructions according to the user's emotional state. For example, if the user is relaxed, it will explain in a casual tone. This allows the generation AI to analyze the user's emotions and provide additional support at the optimal timing.

[0068] The generation AI can analyze the user's learning speed and provide information at an optimal pace. The generation AI can, for example, analyze the user's learning speed and provide information at an optimal pace. For example, it can provide information at a slower pace to a user who is a slow learner. The generation AI can also adjust the way information is provided according to the user's learning speed. For example, it can provide information that concisely summarizes the main points to a user who is a fast learner. The generation AI can also adjust the frequency of information provision according to the user's learning speed. For example, it can provide information in stages to a user who is a slow learner. This makes it possible to provide information at an optimal pace according to the user's learning speed.

[0069] The generation AI can evaluate the user's level of understanding in real time and add supplementary explanations as necessary. The generation AI, for example, evaluates the user's level of understanding in real time and adds supplementary explanations as necessary. For example, if the level of understanding is low, it provides a detailed explanation. The generation AI also adjusts the content of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is high, it provides a concise supplementary explanation. Furthermore, the generation AI changes the format of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is low, it provides a video tutorial. This makes it possible to provide necessary supplementary explanations in real time according to the user's level of understanding.

[0070] The generation AI can use the emotion estimation function to detect the interests and concerns of the user when obtaining information and suggest related information. For example, the generation AI can use the emotion estimation function to detect the interests and concerns of the user when obtaining information in real time and suggest related information. For example, it can provide information related to topics in which the user has shown interest. The generation AI can also adjust the way information is provided according to the user's interests and concerns. For example, it can provide detailed information for topics in which the user has shown interest. Furthermore, the generation AI can adjust the frequency of information provision according to the user's interests and concerns. For example, it can regularly provide related information for topics in which the user has shown interest. This makes it possible to suggest related information according to the user's interests and concerns.

[0071] The generation AI can deliver information in stages according to the user's schedule. For example, the generation AI analyzes the user's schedule and delivers information in stages at the optimal timing. For example, it provides information according to the user's free time. The generation AI also adjusts the frequency of information delivery according to the user's schedule. For example, it reduces the frequency of information delivery during busy periods and increases the frequency of delivery during periods when the user has less free time. Furthermore, the generation AI changes the format of information delivery according to the user's schedule. For example, it provides information in a format that can be understood in a short amount of time. This allows information to be delivered in stages according to the user's schedule.

[0072] The generation AI can select and provide an information format (text, audio, video) according to the user's preferences. The generation AI, for example, selects an information format according to the user's preferences and provides the information. For example, it selects the format the user prefers from text, audio, or video. The generation AI also adjusts the method of providing information according to the user's preferences. For example, it provides video to a user who prefers visual information, and audio to a user who prefers auditory information. Furthermore, the generation AI adjusts the frequency of information provision according to the user's preferences. For example, it provides information frequently to a user who prefers detailed information, and provides summary information to a user who prefers concise information. This makes it possible to provide information in an information format that suits the user's preferences.

[0073] The generation AI can use its emotion estimation function to analyze the emotions a user feels when obtaining information and select the optimal method of providing information. For example, the generation AI can use its emotion estimation function to analyze the emotions a user feels when obtaining information in real time and select the optimal method of providing information. For example, if the user is feeling stressed, the generation AI can provide information in a relaxing format. The generation AI also adjusts the method of providing information according to the user's emotional state. For example, if the user is excited, the generation AI can provide information in a calm manner. Furthermore, the generation AI can adjust the frequency of information provision according to the user's emotional state. For example, if the user is concentrating, the generation AI can provide detailed information, and if the user is tired, the generation AI can provide concise information. This makes it possible to select the optimal method of providing information according to the user's emotions.

[0074] The generation AI can manage store staff schedules and assign specialized tasks at the optimal time. For example, the generation AI can analyze store staff schedules and assign specialized tasks at the optimal time. For example, it can assign tasks based on staff's free time. The generation AI can also adjust the priority of tasks according to the store staff schedules. For example, it can assign tasks with a high degree of urgency first. Furthermore, the generation AI can change the method of task assignment according to the store staff schedules. For example, it can assign tasks based on the staff's skills and experience. This makes it possible to manage store staff schedules and assign specialized tasks at the optimal time.

[0075] Generative AI can evaluate the skill levels of store staff and suggest appropriate training programs. For example, generative AI can evaluate the skill levels of store staff and suggest individually customized training programs. For example, it can provide training to strengthen specific skills to staff who lack them. Generative AI can also adjust the content of the training program according to the skill level of the store staff. For example, it can provide basic training for beginners to advanced training for advanced staff. Furthermore, generative AI can change the format of the training program according to the skill level of the store staff. For example, it can provide online courses, workshops, on-the-job training, etc. This makes it possible to suggest appropriate training programs according to the skill level of the store staff.

[0076] The generation AI can use its emotion estimation function to monitor the stress levels of store staff and suggest breaks to refresh them. For example, the generation AI can use its emotion estimation function to monitor the stress levels of store staff in real time and suggest breaks to refresh them. For example, it can notify them to take a break when stress levels increase. The generation AI can also adjust the content of breaks according to the store staff's stress levels. For example, it can suggest short breaks or relaxation exercises. Furthermore, the generation AI can adjust the timing of breaks according to the store staff's stress levels. For example, it can encourage them to take breaks at appropriate times between work tasks. This makes it possible to monitor the stress levels of store staff and suggest breaks to refresh them.

[0077] Generative AI can automatically record the work of store staff and analyze their work efficiency. For example, generative AI can build a system that automatically records the work of store staff and analyzes their work efficiency. For example, it can record the progress of work and the time it is completed. Generative AI can also evaluate work efficiency based on the work records of store staff. For example, it can analyze work time and error rates. Furthermore, generative AI can make suggestions to improve work efficiency. For example, it can suggest improvements to work procedures or the introduction of tools. This makes it possible to automatically record the work of store staff and analyze their work efficiency.

[0078] Generative AI can share store staff's work with other staff members, improving the efficiency of the entire team. For example, generative AI can build a system that allows store staff members to share their work with other staff members, improving the efficiency of the entire team. For example, they can share the progress of work and work together to get the job done. Generative AI can also prevent duplication of work by sharing the work content of store staff with other staff members. For example, it can adjust so that multiple staff members do not perform the same work. Furthermore, by sharing store staff members' work with other staff members, generative AI can improve the skills of the entire team. For example, experienced staff members can teach new staff members their work. This allows store staff members to share their work with other staff members, improving the efficiency of the entire team.

[0079] The generation AI can use its emotion estimation function to analyze the emotional state of store staff and provide feedback to increase their motivation. For example, the generation AI can use its emotion estimation function to analyze the emotional state of store staff in real time and provide feedback to increase their motivation. For example, it can display positive feedback or encouraging messages. The generation AI can also adjust the content of the feedback depending on the emotional state of the store staff. For example, it can provide relaxing feedback to a staff member who is feeling stressed. Furthermore, the generation AI can change the form of feedback depending on the emotional state of the store staff. For example, it can provide verbal feedback, written feedback, digital feedback, etc. This makes it possible to analyze the emotional state of store staff and provide feedback to increase their motivation.

[0080] Generative AI can analyze store visitor data and propose optimal staff allocation. For example, generative AI can analyze store visitor data and build a system that proposes optimal staff allocation. For example, it can allocate staff according to peak visitor times. Generative AI can also adjust staff allocation based on store visitor data. For example, if customers are concentrated during a specific time period, it can increase the number of staff during that time period. Furthermore, generative AI can propose allocation based on staff skills and experience based on store visitor data. For example, it can allocate experienced staff during peak hours. This makes it possible to analyze store visitor data and propose optimal staff allocation.

[0081] Generative AI can automate store inventory management and ensure efficient product replenishment. For example, generative AI analyzes store inventory data and builds a system for efficient product replenishment. For example, it automatically replenishes products that are running low on stock. Generative AI also adjusts the timing of product replenishment based on the store's inventory data. For example, it replenishes best-selling products at the appropriate time so that they do not run out of stock. Furthermore, generative AI adjusts the amount of product replenishment based on the store's inventory data. For example, it increases or decreases the amount replenished depending on demand. This allows for automated store inventory management and efficient product replenishment.

[0082] The generation AI can use the emotion estimation function to monitor customer satisfaction in real time and make suggestions for improving services. For example, the generation AI can use the emotion estimation function to build a system that monitors customer satisfaction in real time and makes suggestions for improving services. For example, if satisfaction is low, it will suggest improvements. The generation AI also adjusts the content of services according to customer satisfaction. For example, if satisfaction is high, it will maintain the current service, and if satisfaction is low, it will improve the service. Furthermore, the generation AI changes the way the service is provided according to customer satisfaction. For example, if satisfaction is low, it will provide a more attentive response. This makes it possible to monitor customer satisfaction in real time and make suggestions for improving services.

[0083] Generative AI can strengthen collaboration with other stores and promote resource sharing. For example, generative AI can build a system that strengthens collaboration with other stores and promotes resource sharing. For example, it can share inventory and staff. Generative AI can also work to efficiently use resources by collaborating with other stores. For example, it can replenish inventory from other stores to adjust inventory surpluses and shortages. Furthermore, generative AI can work to reduce costs by sharing resources with other stores. For example, it can reduce costs by using common resources. This can strengthen collaboration with other stores and promote resource sharing.

[0084] Generative AI can optimize a store's energy consumption and reduce costs. For example, generative AI can analyze a store's energy consumption data and build a system that proposes an optimal energy consumption plan. For example, by avoiding peak energy consumption times. Generative AI can also make suggestions to improve energy efficiency based on the store's energy consumption data. For example, it can suggest the introduction of highly energy-efficient equipment. Furthermore, generative AI can make suggestions to reduce energy waste based on the store's energy consumption data. For example, it can encourage the use of unnecessary lighting and equipment to be reduced. This makes it possible to optimize a store's energy consumption and reduce costs.

[0085] The generative AI can use its emotion estimation function to analyze customers' emotional data and improve the store's atmosphere and service. For example, the generative AI can use its emotion estimation function to analyze customers' emotional data in real time and build a system to improve the store's atmosphere and service. For example, the generative AI can adjust the store's music and lighting according to the customer's emotional state. The generative AI can also adjust the content of services based on the customer's emotional data. For example, if the customer is relaxed, the current service can be maintained, and if the customer is stressed, the service can be improved. Furthermore, the generative AI can change the way the service is provided based on the customer's emotional data. For example, if the customer is satisfied, the current service can be continued, and if the customer is dissatisfied, the service can be more attentive. This makes it possible to analyze customers' emotional data and improve the store's atmosphere and service.

[0086] The generative AI can learn from a user's past interaction history and provide more personalized responses. For example, the generative AI analyzes a user's past interaction history and provides individually customized responses. For example, it provides related information based on questions asked in the past. The generative AI can also identify a user's preferences and interests based on the user's interaction history and provide responses accordingly. For example, a user who is interested in a particular topic can be provided with information related to that topic. Furthermore, the generative AI can identify areas where the user has had difficulty in the past based on the user's interaction history and provide enhanced support for those areas. For example, a user who has difficulty with a particular operation can be provided with detailed explanations of that operation. This allows for more personalized responses based on the user's past interaction history.

[0087] Generative AI can analyze a user's non-verbal communication (facial expressions, gestures) to achieve more natural dialogue. For example, generative AI can analyze a user's facial expressions to achieve a natural dialogue that corresponds to their emotional state. For example, when the user smiles, it returns a positive response. Generative AI can also analyze a user's gestures to smooth the flow of the dialogue. For example, when the user nods, it moves on to the next explanation. Furthermore, generative AI can analyze the user's posture and movements to adjust the content of the dialogue. For example, if the user is relaxed, it will speak in a casual tone. This allows the user's non-verbal communication to be analyzed and more natural dialogue to be achieved.

[0088] The generation AI can use the emotion estimation function to grasp the user's emotional state in real time and respond appropriately. For example, the generation AI can use the emotion estimation function to build a system that grasps the user's emotional state in real time and responds appropriately. For example, if the user is feeling anxious, it will respond to reassure the user. The generation AI also adjusts the content of the dialogue depending on the user's emotional state. For example, if the user is excited, it will provide information calmly. Furthermore, the generation AI changes the style of the dialogue depending on the user's emotional state. For example, if the user is relaxed, it will speak in a casual tone. This makes it possible to grasp the user's emotional state in real time and respond appropriately.

[0089] Generative AI can seamlessly take over conversations between different devices and provide consistent support. For example, generative AI can build a system that seamlessly takes over conversations between different devices and provides consistent support. For example, a conversation can be taken over from a smartphone to a tablet. Generative AI can also synchronize the conversation history between different devices, allowing users to receive consistent support regardless of which device they use. For example, a conversation started on a desktop computer can be continued on a smartphone. Furthermore, when taking over a conversation between different devices, generative AI retains the content and progress of the conversation. For example, it can smoothly proceed with the next conversation based on the content of the previous conversation. This makes it possible to seamlessly take over conversations between different devices and provide consistent support.

[0090] The generation AI can select a conversation style (formal or casual) according to the user's preferences. For example, the generation AI selects a conversation style according to the user's preferences and responds formally or casually. For example, formal language is used in business situations. The generation AI also adjusts the tone and language of the conversation according to the user's preferences. For example, it responds in a friendly tone to a user who prefers casual conversations. Furthermore, the generation AI adjusts the content of the conversation according to the user's preferences. For example, it provides detailed information to a user who prefers detailed explanations, and provides information that summarizes the main points to a user who prefers concise explanations. This allows the generation AI to respond in a conversation style that suits the user's preferences.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] Based on the user's operation history, the generative AI can automatically customize and place the user's frequently used functions and apps on the home screen. For example, it can place frequently used apps in a prominent position on the home screen. The generative AI can also hide unnecessary apps and functions based on the user's operation history. For example, it can automatically organize apps that have not been used for a long time into folders. Furthermore, the generative AI can add frequently used settings and shortcuts to the home screen based on the user's operation history. For example, it can make frequently used Wi-Fi settings and Bluetooth on / off settings easily accessible. This allows the home screen to be customized based on the user's operation history, providing a more user-friendly interface.

[0093] Based on the user's operation history, the generative AI can suggest new apps and features that the user might be interested in. For example, for a user who frequently uses photo editing apps, it can suggest new photo editing apps and features. The generative AI can also suggest events and campaigns that the user might be interested in based on the user's operation history. For example, for a user who frequently uses music apps, it can suggest music-related events and campaigns. Furthermore, the generative AI can suggest news and articles that the user might be interested in based on the user's operation history. For example, for a user who frequently uses sports apps, it can suggest the latest sports news and articles. This makes it possible to suggest new apps and features that the user might be interested in based on the user's operation history.

[0094] Based on the user's operation history, the generation AI can automatically create shortcuts for frequently used functions and apps. For example, it can add shortcuts for frequently used apps to the home screen. The generation AI can also create shortcuts for frequently used settings and functions based on the user's operation history. For example, it can add shortcuts for frequently used Wi-Fi settings or Bluetooth on / off to the home screen. Furthermore, the generation AI can automatically organize shortcuts for frequently used functions and apps based on the user's operation history. For example, it can organize shortcuts for frequently used apps into folders. This allows shortcuts to be automatically created based on the user's operation history, providing a more user-friendly interface.

[0095] Using its emotion estimation function, the generative AI can detect stress or confusion felt by the user while operating the device and provide advice to help them relax. For example, if the user shows a confused expression, it can provide advice to take a deep breath to relax. The generative AI can also suggest relaxation techniques to reduce the stress or confusion felt by the user while operating the device. For example, it can encourage the user to take a short break. Furthermore, the generative AI can suggest environmental adjustments to reduce the stress or confusion felt by the user while operating the device. For example, it can guide the user on how to adjust the music or lighting while operating the device. This makes it possible to detect stress or confusion felt by the user and provide advice to help them relax.

[0096] The generating AI can use AR technology to overlay operating instructions on the device. For example, it can overlay operating instructions on the screen of a smartphone. The generating AI can also use AR technology to display operating instructions and guides on the actual screen of the device operated by the user. For example, it can display operating instructions on a screen viewed through a camera. Furthermore, the generating AI can use AR technology to display operating instructions for the actual physical buttons and interface of the device operated by the user. For example, it can display operating instructions on top of a button. This makes it possible to overlay operating instructions on the actual device using AR technology.

[0097] The generation AI can automatically generate operating instructions that correspond to different languages ​​and cultures, making it possible to accommodate global users. For example, it can provide operating instructions in multiple languages, such as English, French, and Chinese. The generation AI can also automatically generate operating instructions that correspond to different cultures and provide explanations that take into account the user's cultural background. For example, it can provide explanations that include operating procedures and precautions that take cultural differences into account. Furthermore, in order to provide operating instructions that correspond to different languages ​​and cultures, the generation AI can automatically generate instructions that are tailored to the appropriate language and culture based on the user's language settings and regional information. This makes it possible to automatically generate operating instructions that correspond to different languages ​​and cultures, making it possible to accommodate global users.

[0098] Using its emotion estimation function, the generation AI can analyze the user's emotions when receiving instructions and provide additional support at the optimal time. For example, if it detects a confused expression, it can add detailed instructions. The generation AI can also adjust the pace and content of the instructions depending on the user's emotional state. For example, if the user is feeling impatient, it can slow down the pace of the instructions. Furthermore, the generation AI can change the format of the instructions depending on the user's emotional state. For example, if the user is relaxed, it can provide instructions in a casual tone. This allows it to analyze the user's emotions and provide additional support at the optimal time.

[0099] The generation AI can analyze the user's learning speed and provide information at an optimal pace. For example, it can provide information at a slow pace to a user who is a slow learner. The generation AI can also adjust the way information is provided according to the user's learning speed. For example, it can provide information that summarizes the main points concisely to a user who is a fast learner. Furthermore, the generation AI can adjust the frequency of information provision according to the user's learning speed. For example, it can provide information in stages to a user who is a slow learner. This makes it possible to provide information at an optimal pace according to the user's learning speed.

[0100] The generation AI can evaluate the user's level of understanding in real time and add supplementary explanations as necessary. For example, if the level of understanding is low, it can provide detailed explanations. The generation AI can also adjust the content of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is high, it can provide concise supplementary explanations. Furthermore, the generation AI can change the format of the supplementary explanations according to the user's level of understanding. For example, if the level of understanding is low, it can provide a video tutorial. This makes it possible to provide necessary supplementary explanations in real time according to the user's level of understanding.

[0101] The generation AI can use the emotion estimation function to detect the user's interests and concerns when obtaining information and suggest related information. For example, the emotion estimation function can be used to detect the user's interests and concerns when obtaining information in real time and suggest related information. For example, information related to topics that the user has shown interest can be provided. The generation AI can also adjust the way information is provided according to the user's interests and concerns. For example, detailed information can be provided for topics that the user has shown interest in. Furthermore, the generation AI can adjust the frequency of information provision according to the user's interests and concerns. For example, related information can be provided periodically for topics that the user has shown interest in. This makes it possible to suggest related information according to the user's interests and concerns.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The generating AI provides instructions on how to use the device and how to operate it based on prompts containing instructions on what the user wants the generating AI to do. For example, in response to the prompt "Please tell me how to set up my new smartphone," the generating AI generates specific instructions in the form of video and audio. Step 2: The video provider provides the video generated by the AI. For example, the video provider may display operating instructions on a smartphone screen, allowing the user to play, stop, rewind, and fast-forward the video. Step 3: The voice provider provides the voice generated by the AI. For example, the voice provider may provide voice instructions through the smartphone speaker, allowing the user to play, stop, rewind, and fast-forward the voice. Step 4: The question response section responds to general questions. For example, if a user asks, "Can you tell me more about this feature?", the generative AI will provide an appropriate answer to that question.

[0104] 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.

[0105] 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.

[0106] 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.

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

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

[0138] 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.

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

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

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

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

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

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

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

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

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] 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.

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

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

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

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

[0153] The 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. Equipped with generative AI, A video provider provides video instructions on how to use the model and how to operate it. A voice providing section that provides voice instructions on how to use the device and operation instructions; A question response unit that responds to general questions is provided. A system characterized by:

2. The generated AI is The operating instructions are displayed overlaid on the device using AR technology.

2. The system of claim 1.

3. The generated AI is Analyze the user's learning speed and provide information at the optimal pace 2. The system of claim 1.

4. The generated AI is Manage store staff schedules and assign specialized tasks at optimal times 2. The system of claim 1.

5. The generated AI is Monitor customer satisfaction in real time and make suggestions for improving services 2. The system of claim 1.

6. The generated AI is Detects stress or confusion felt by users in relation to operations and provides advice to help them relax 2. The system of claim 1.

7. The generated AI is Detecting the user's interests and concerns when obtaining information and suggesting related information 2. The system of claim 1.

8. The generated AI is Analyze the emotional state of store staff and provide feedback to boost motivation 2. The system of claim 1.

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