system

Abstract

The system according to this embodiment aims to efficiently provide instructions on how to use and operate the devices, thereby effectively improving the operations of mobile phone shops. [Solution] The system according to this embodiment comprises a provision unit, an information provision unit, a question response unit, and a response unit. The provision unit provides instructions on how to use and operate the device in video and audio. The information provision unit provides information tailored to individual needs based on the information provided by the provision unit. The question response unit responds to basic questions based on the information provided by the information provision unit. The response unit provides 24-hour support based on the information responded to by the question response unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time to explain how to use a model and how to operate it in a mobile shop, and it is difficult to respond efficiently with a shortage of staff.

[0005] The system according to the embodiment aims to efficiently provide explanations on how to use a model and how to operate it, and effectively improve the operations of a mobile shop.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a provision unit, an information provision unit, a question response unit, and a response unit. The provision unit provides instructions on how to use and operate the device in video and audio format. The information provision unit provides information tailored to individual needs based on the information provided by the provision unit. The question response unit responds to basic questions based on the information provided by the information provision unit. The response unit provides 24-hour support based on the information handled by the question response unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide instructions on how to use and operate the device, and can effectively improve the operations of mobile phone shops. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The mobile phone shop support system according to an embodiment of the present invention is a system that introduces an AI agent to streamline the explanation of how to use and operate a mobile phone model. This mobile phone shop support system provides explanations of how to use and operate a mobile phone model through video and audio. This allows for immediate responses to general questions that do not require direct interaction from store staff. Customers can obtain information at their own pace, and store staff can concentrate on more specialized tasks. This enables efficient store operations even in the event of staff shortages. Furthermore, the AI ​​agent is expected to provide flexible and efficient support while mimicking natural human interaction. This allows for the effective evolution of mobile phone shop operations while also addressing the psychological needs of customers. For example, the mobile phone shop support system guides customers through basic smartphone operation and settings using video and audio. This allows customers to learn and understand at their own pace. The mobile phone shop support system provides information tailored to individual needs through dialogue with customers. For example, if a customer asks about a specific function, it provides a detailed explanation of that function. The mobile phone shop support system also learns from customer responses and evolves to provide more effective responses. This allows customers to receive more personalized support. The mobile phone shop support system reduces the burden on store staff and supports efficient store operations. For example, by having the system answer basic questions, store staff can concentrate on more specialized tasks. This makes efficient store operations possible even with staff shortages. In addition, the mobile phone shop support system is available 24 hours a day, so customers can get the information they need at any time. This allows mobile phone shops to provide high-quality support to customers while achieving efficient store operations. For example, by having the system provide instructions on how to use the devices, store staff can concentrate on contract-related tasks. Furthermore, the mobile phone shop support system also addresses the psychological needs of customers, providing a sense of security through natural conversation. This allows customers to use the store in a relaxed manner.This allows the mobile phone shop support system to efficiently provide instructions on how to use and operate different models, reducing the burden on store staff and enabling efficient store operations.

[0029] The mobile phone shop support system according to this embodiment comprises a provision unit, an information provision unit, a question response unit, and a response unit. The provision unit provides instructions on how to use and operate the device through video and audio. For example, the provision unit guides users through basic operation and settings of a smartphone using video and audio. For example, the provision unit can explain how to turn on a smartphone, how to install apps, and how to change settings using video and audio. For example, the provision unit can visually explain basic smartphone operation methods using video and provide supplementary explanations with audio. For example, the provision unit can guide users through smartphone settings step-by-step using video and audio. The information provision unit provides information tailored to individual needs based on the information provided by the provision unit. For example, the information provision unit responds immediately to customer questions and provides clear explanations. For example, if a customer asks about a specific function, the information provision unit can provide a detailed explanation of that function. For example, if a customer asks about how to use a specific smartphone app, the information provision unit can provide a clear explanation of how to use that app. The Information Department can, for example, provide detailed instructions on how to change smartphone settings if a customer asks about it. The Question Response Department answers basic questions based on the information provided by the Information Department. The Question Response Department can, for example, learn from customer responses and evolve to provide more effective responses. The Question Response Department can, for example, analyze customer responses and reflect them in future responses. The Question Response Department can, for example, collect customer responses as feedback data and improve its response methods using machine learning algorithms. The Question Response Department can, for example, analyze customer responses in real time and immediately adjust its response methods. The Response Department provides 24-hour support based on the information provided by the Question Response Department. The Response Department can, for example, provide 24-hour support, allowing customers to obtain the information they need at any time. The Response Department can, for example, provide 24-hour support using an automated response system. The Response Department can, for example, implement 24-hour support by employing staff on a shift system.The support unit can, for example, use an AI agent to provide 24-hour support. As a result, the mobile phone shop support system according to this embodiment can efficiently provide instructions on how to use and operate the devices, reduce the burden on store staff, and enable efficient store operations.

[0030] The service provider will provide instructions on how to use and operate the device through video and audio. Specifically, they will guide users through basic smartphone operation and settings using video and audio. For example, they can explain how to turn on a smartphone, how to install apps, and how to change settings using video and audio. The service provider can visually explain basic smartphone operation using video and provide supplementary explanations with audio. This allows users to receive information using both sight and hearing, leading to a deeper understanding. Furthermore, the service provider can guide users through smartphone setup step by step using video and audio. For example, for first-time smartphone users, they can explain everything from how to turn on the power to setting up Wi-Fi, setting up email accounts, and downloading apps. This allows users to learn at their own pace and gain confidence in their operation. In addition, the service provider can adjust the speed and content of the explanation according to the user's level of understanding. For example, they can explain slowly and in detail for beginners and concisely focus on key points for experienced users. This allows the service provider to cater to a wide range of users and provide information tailored to individual needs. Furthermore, the service provider can regularly update video and audio content to provide the latest information. For example, when a new model is released or a software update is made, they can add new explanations corresponding to the changes. This allows the service provider to always provide the latest information and increase user satisfaction.

[0031] The Information Provision Department provides information tailored to individual needs based on the information provided by the department. Specifically, it responds immediately to customer questions and provides clear explanations. For example, if a customer asks about a specific function, the Information Provision Department can provide a detailed explanation of that function. If a customer asks about how to use a specific smartphone app, the Information Provision Department can provide a clear explanation of how to use that app. For example, if a customer asks about how to use the camera app, the Information Provision Department will provide a detailed explanation of how to take photos, use filters, and edit photos. Also, if a customer asks about changing smartphone settings, the Information Provision Department can provide a detailed explanation of how to change those settings. For example, it will provide specific instructions on how to change notification settings and how to adjust privacy settings. The Information Provision Department has an extensive knowledge base to respond quickly and accurately to user questions. Furthermore, the Information Provision Department records the user's question history and can provide more appropriate information by referring to past questions. As a result, the Information Provision Department can provide customized information tailored to user needs and increase user satisfaction.

[0032] The question support department handles basic questions based on information provided by the information provision department. Specifically, it learns from customer responses and evolves to provide more effective responses. For example, it can analyze customer responses and reflect them in future responses. The question support department can collect customer responses as feedback data and improve its response methods using machine learning algorithms. For example, it can evaluate whether a customer was satisfied with a particular question and adjust its response method based on that evaluation. Furthermore, the question support department can analyze customer responses in real time and adjust its response method immediately. For example, if a customer expresses dissatisfaction with a question, it can change its response method on the spot and provide a more appropriate answer. In this way, the question support department can always provide the best possible response and increase user satisfaction. In addition, the question support department can provide customized responses for each user based on past response history. For example, for a user who has asked the same question in the past, it can provide more detailed information by referring to the previous answer. In this way, the question support department can provide flexible responses tailored to user needs and gain user trust.

[0033] The support department provides 24-hour support based on information gathered by the question support department. Specifically, it offers 24-hour support, allowing customers to obtain necessary information at any time. For example, 24-hour support can be provided using an automated response system. The automated response system automatically provides answers to customer questions based on pre-set scenarios. This allows customers to obtain necessary information even during times when staff are unavailable, such as late at night or early in the morning. Furthermore, the support department can achieve 24-hour support by employing staff on a shift system. For example, staff can work in shifts, ensuring that someone is always available to answer questions. This allows for a rapid response in emergencies. The support department can also provide 24-hour support using an AI agent. The AI ​​agent uses natural language processing technology to understand customer questions and provide appropriate answers. For example, if a customer asks about how to set up their smartphone, the AI ​​agent will understand the question and guide them through the specific setup process. This allows the support department to respond to customer questions 24 hours a day and provide quick and accurate information. In addition, the support department can collect user feedback and use it to improve its support methods. For example, by reviewing response procedures based on user evaluations and feedback, we can provide better service. This allows the support department to consistently deliver high-quality service and increase user satisfaction.

[0034] The service provider can guide users through basic smartphone operation and settings using video and audio. For example, the service provider can visually explain how to turn on a smartphone using video and provide supplementary audio explanations. For example, the service provider can guide users through app installation step-by-step using video and audio. For example, the service provider can provide detailed explanations of how to change settings using video and audio. This helps customers understand basic smartphone operation and settings by providing easy-to-understand guidance. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can have a generation AI generate videos and audio explaining how to operate a smartphone.

[0035] The Information Department can respond immediately to customer inquiries and provide clear explanations. For example, if a customer asks about a specific function, the Information Department can provide a detailed explanation of that function. For example, if a customer asks about how to use a specific smartphone app, the Information Department can provide a clear explanation of how to use that app. For example, if a customer asks about changing smartphone settings, the Information Department can provide a detailed explanation of how to change those settings. By responding immediately to customer inquiries and providing clear explanations, customer satisfaction is improved. Some or all of the above processing in the Information Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Information Department can input the customer's question into a generative AI, which can then generate an answer to the question.

[0036] The question response unit can learn from customer responses and evolve to provide more effective responses. For example, the question response unit can analyze customer responses and reflect them in future responses. For example, the question response unit can collect customer responses as feedback data and improve its response methods using machine learning algorithms. For example, the question response unit can analyze customer responses in real time and immediately adjust its response methods. This allows it to learn from customer responses and evolve its responses to provide more effective support. Some or all of the above processes in the question response unit may be performed using, for example, generative AI, or not using generative AI. For example, the question response unit can input customer response data into generative AI, which can then generate feedback to improve its response methods.

[0037] The support department is available 24 hours a day, and customers can obtain the information they need at any time. The support department can provide 24-hour support, for example, by using an automated response system. The support department can achieve 24-hour support, for example, by using a shift-based staffing system. The support department can provide 24-hour support, for example, by using an AI agent. As a result, 24-hour support is possible, and customers can obtain the information they need at any time. Some or all of the above-described processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can have a generative AI execute a 24-hour automated response system.

[0038] The service provider can customize the content of the videos and audio they provide based on the customer's past question history. For example, the service provider can prioritize providing relevant operational instructions based on the questions the customer has asked in the past. For example, the service provider can provide videos and audio that explain content that the customer previously found difficult to understand. For example, the service provider can provide videos and audio that provide detailed explanations of features that the customer has previously shown interest in. In this way, by customizing the content of videos and audio based on the customer's past question history, more appropriate information is provided. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input the customer's past question history data into a generating AI, and the generating AI can generate customized video and audio content.

[0039] The service provider can automatically adjust the speed of the video and audio they provide according to the customer's level of understanding. For example, the service provider can provide video and audio that explains things at a speed that is easy for the customer to understand. For example, if the customer prefers a fast-paced explanation, the service provider can provide video and audio at a faster speed. For example, if the customer prefers a slower explanation, the service provider can provide video and audio at a slower speed. In this way, by adjusting the speed of the video and audio according to the customer's level of understanding, the service provider can provide information that is easier to understand. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or not using a generation AI. For example, the service provider can input customer understanding data into a generation AI, and the generation AI can automatically adjust the speed of the video and audio.

[0040] The service provider can adjust the content of the video and audio they provide according to the customer's age and digital literacy. For example, the service provider can provide video and audio with a slow pace of explanation to elderly customers. For example, the service provider can provide video and audio that explains basic operations to customers with low digital literacy. For example, the service provider can provide video and audio that provides speedy and detailed explanations to young customers. In this way, by adjusting the content of the video and audio according to the customer's age and digital literacy, more appropriate information is provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the customer's age and digital literacy data into a generative AI, and the generative AI can adjust the content of the video and audio.

[0041] The service provider can optimize the video and audio content they provide according to the type of device the customer is using. For example, the service provider can provide video and audio optimized for the smartphone screen size to customers using smartphones. For example, the service provider can provide video and audio optimized for the larger screen to customers using tablets. For example, the service provider can provide video and audio optimized for the PC screen size to customers using PCs. By optimizing the video and audio content according to the type of device the customer is using, the service provider can provide more appropriate information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input customer device type data into a generation AI, which can then optimize the video and audio content.

[0042] The information provision department can analyze a customer's past question history and select the most appropriate method of providing information. For example, the information provision department can prioritize providing relevant information based on the content of questions the customer has asked in the past. For example, the information provision department can provide information that explains content that the customer found difficult to understand in the past. For example, the information provision department can provide detailed information about features that the customer has shown interest in in the past. In this way, by analyzing the customer's past question history, the information provision department can select the most appropriate method of providing information and provide more relevant information. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision department can input the customer's past question history data into a generative AI, and the generative AI can select the most appropriate method of providing information.

[0043] The information provision department can adjust the timing of information provision based on the customer's current situation. For example, if the customer is in a hurry, the information provision department can provide information quickly. For example, if the customer is relaxed, the information provision department can provide detailed information sequentially. For example, if the customer is confused, the information provision department can prioritize providing basic information. By adjusting the timing of information provision according to the customer's current situation, more appropriate information can be provided. Some or all of the above processing in the information provision department may be performed using, for example, a generating AI, or without a generating AI. For example, the information provision department can input the customer's current situation data into a generating AI, and the generating AI can adjust the timing of information provision.

[0044] The information provision department can prioritize providing highly relevant information by taking into account the customer's geographical location. For example, the information provision department can prioritize providing information related to the customer's current location. For example, the information provision department can provide information related to places the customer has visited in the past. For example, the information provision department can provide information related to places the customer plans to visit in the future. By providing highly relevant information based on the customer's geographical location, the department can provide more appropriate information. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision department can input the customer's geographical location data into a generative AI, which can then prioritize providing highly relevant information.

[0045] The Information Provision Department can analyze customers' social media activity and provide relevant information. For example, the Information Provision Department can provide information related to the content that customers have shown interest in on social media. For example, the Information Provision Department can provide information related to the content that customers have shared on social media. For example, the Information Provision Department can provide information related to the accounts that customers follow on social media. By analyzing customers' social media activity, the Information Provision Department can provide relevant information and offer more appropriate support. Some or all of the above processing in the Information Provision Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Information Provision Department can input the customer's social media activity data into a generative AI, and the generative AI can provide relevant information.

[0046] The question response unit can analyze the customer's past question history and select the most appropriate response method. For example, the question response unit can prioritize providing relevant answers based on the content of questions the customer has asked in the past. For example, the question response unit can provide answers that explain content that the customer previously found difficult to understand. For example, the question response unit can provide detailed answers about features that the customer has previously shown interest in. In this way, by analyzing the customer's past question history, the optimal response method is selected and more appropriate answers are provided. Some or all of the above processing in the question response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question response unit can input the customer's past question history data into a generative AI, and the generative AI can select the most appropriate response method.

[0047] The question response unit can adjust the timing of its response based on the customer's current situation. For example, if the customer is in a hurry, the question response unit will provide a quick answer. For example, if the customer is relaxed, the question response unit can provide detailed answers sequentially. For example, if the customer is confused, the question response unit can prioritize providing basic answers. By adjusting the timing of the response according to the customer's current situation, it is possible to provide more appropriate answers. Some or all of the above processing in the question response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question response unit can input data on the customer's current situation into a generative AI, which can then adjust the timing of the response.

[0048] The question response unit can prioritize responding to highly relevant questions by taking into account the customer's geographical location. For example, the question response unit can prioritize responding to questions related to the customer's current location. For example, the question response unit can respond to questions related to places the customer has visited in the past. For example, the question response unit can respond to questions related to places the customer plans to visit in the future. This allows for more appropriate support by responding to highly relevant questions based on the customer's geographical location. Some or all of the above processing in the question response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the question response unit can input the customer's geographical location data into a generative AI, which can then prioritize responding to highly relevant questions.

[0049] The question response unit can analyze the customer's social media activity and respond to relevant questions. For example, the question response unit can respond to questions related to the content the customer has shown interest in on social media. For example, the question response unit can respond to questions related to the content the customer has shared on social media. For example, the question response unit can respond to questions related to the accounts the customer follows on social media. In this way, by analyzing the customer's social media activity, it can respond to relevant questions and provide more appropriate support. Some or all of the above processing in the question response unit may be performed using, for example, generative AI, or not using generative AI. For example, the question response unit can input the customer's social media activity data into a generative AI, and the generative AI can respond to relevant questions.

[0050] The support unit can analyze the customer's past question history and select the most appropriate response method. For example, the support unit can prioritize providing relevant responses based on the content of questions the customer has asked in the past. For example, the support unit can provide responses that explain content that the customer found difficult to understand in the past. For example, the support unit can provide detailed responses regarding features that the customer has shown interest in in the past. In this way, by analyzing the customer's past question history, the support unit can select the most appropriate response method and provide more appropriate support. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the customer's past question history data into a generative AI, which can then select the most appropriate response method.

[0051] The response unit can adjust the timing of its response based on the customer's current situation. For example, if the customer is in a hurry, the response unit will provide a quick response. For example, if the customer is relaxed, the response unit can provide detailed responses sequentially. For example, if the customer is confused, the response unit can prioritize providing basic responses. This allows for more appropriate support by adjusting the timing of the response according to the customer's current situation. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the response unit can input the customer's current situation data into a generating AI, which can then adjust the timing of the response.

[0052] The response unit can prioritize responses that are highly relevant to the customer, taking into account the customer's geographical location. For example, the response unit can prioritize responses related to the customer's current location. For example, the response unit can provide responses related to places the customer has visited in the past. For example, the response unit can provide responses related to places the customer plans to visit in the future. This allows for more appropriate support by providing responses that are highly relevant based on the customer's geographical location. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input the customer's geographical location data into a generative AI, which can then prioritize responses that are highly relevant.

[0053] The response unit can analyze the customer's social media activity and take relevant actions. For example, the response unit can take actions related to the content the customer has shown interest in on social media. For example, the response unit can take actions related to the content the customer has shared on social media. For example, the response unit can take actions related to the accounts the customer follows on social media. By analyzing the customer's social media activity, the response unit can take relevant actions and provide more appropriate support. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input the customer's social media activity data into a generative AI, which can then take relevant actions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The service provider can adjust the method of delivering video and audio according to the battery level of the user's device. For example, if the battery level is low, the service provider can deliver short video and audio. Conversely, if the battery level is sufficient, it can deliver detailed video and audio. In addition, if the battery level is low, the service provider can deliver in power-saving mode. This allows users to obtain information without worrying about their battery level.

[0056] The information provision department can provide relevant information based on the user's past purchase history. For example, it can provide information about smartphone accessories the user has purchased in the past. It can also provide upgrade information for models the user has purchased in the past. Furthermore, it can prioritize providing support information for products the user has purchased in the past. This allows users to quickly obtain information relevant to them.

[0057] The system can adjust how information is provided based on the user's current network connection status. For example, if the network connection is unstable, the system can prioritize text-based information. Conversely, if the network connection is stable, it can provide information using video and audio. Furthermore, if the network connection is interrupted, it can provide information that can be used offline. This ensures that users can obtain the necessary information regardless of their network connection status.

[0058] The information delivery system can customize the method of information delivery according to the user's learning style. For example, users who prefer visual learning can be provided with video-based information. Conversely, users who prefer auditory learning can be provided with audio-based information. Furthermore, users who prefer practical learning can be provided with interactive content. This allows users to obtain information in a way that suits their learning style.

[0059] The question response unit can analyze a user's past question history and select the most appropriate response method. For example, it can prioritize providing relevant answers based on the user's past questions. It can also provide answers that explain concepts the user previously found difficult to understand. Furthermore, it can provide detailed answers regarding features the user has shown interest in in the past. This allows users to quickly obtain information relevant to them.

[0060] The information delivery system can optimize the method of information delivery according to the type of device the user is using. For example, users using smartphones can be provided with video and audio optimized for the smartphone screen size. Conversely, users using tablets can be provided with video and audio optimized for larger screens. Furthermore, users using PCs can be provided with video and audio optimized for their PC screen size. This allows users to obtain information optimized for their own device.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The service provider will provide instructions on how to use and operate the device through video and audio. For example, they will guide users through the basic operation and settings of a smartphone using video and audio. Specifically, they can explain how to turn on a smartphone, how to install apps, and how to change settings using video and audio. They can also visually explain the basic operation of a smartphone using video and provide supplementary explanations with audio. Furthermore, they can guide users through the smartphone's settings step by step using video and audio. Step 2: The information provision department provides information tailored to individual needs based on the information provided by the department. For example, they respond immediately to customer questions and provide clear explanations. Specifically, if a customer asks about a particular function, they can provide a detailed explanation of that function. Also, if a customer asks about how to use a particular smartphone app, they can provide a clear explanation of how to use that app. Furthermore, if a customer asks about changing smartphone settings, they can provide a detailed explanation of how to change those settings. Step 3: The question response unit answers basic questions based on the information provided by the information provision unit. For example, it learns from customer responses and evolves to provide more effective responses. Specifically, it can analyze customer responses and reflect them in future responses. It can also collect customer responses as feedback data and use machine learning algorithms to improve response methods. Furthermore, it can analyze customer responses in real time and adjust response methods immediately. Step 4: The support department provides 24-hour support based on the information provided by the question support department. For example, 24-hour support is available, allowing customers to obtain the information they need at any time. Specifically, 24-hour support can be provided using an automated response system. Alternatively, 24-hour support can be achieved by employing staff on a shift system. Furthermore, 24-hour support can be provided using an AI agent.

[0063] (Example of form 2) The mobile phone shop support system according to an embodiment of the present invention is a system that introduces an AI agent to streamline the explanation of how to use and operate a mobile phone model. This mobile phone shop support system provides explanations of how to use and operate a mobile phone model through video and audio. This allows for immediate responses to general questions that do not require direct interaction from store staff. Customers can obtain information at their own pace, and store staff can concentrate on more specialized tasks. This enables efficient store operations even in the event of staff shortages. Furthermore, the AI ​​agent is expected to provide flexible and efficient support while mimicking natural human interaction. This allows for the effective evolution of mobile phone shop operations while also addressing the psychological needs of customers. For example, the mobile phone shop support system guides customers through basic smartphone operation and settings using video and audio. This allows customers to learn and understand at their own pace. The mobile phone shop support system provides information tailored to individual needs through dialogue with customers. For example, if a customer asks about a specific function, it provides a detailed explanation of that function. The mobile phone shop support system also learns from customer responses and evolves to provide more effective responses. This allows customers to receive more personalized support. The mobile phone shop support system reduces the burden on store staff and supports efficient store operations. For example, by having the system answer basic questions, store staff can concentrate on more specialized tasks. This makes efficient store operations possible even with staff shortages. In addition, the mobile phone shop support system is available 24 hours a day, so customers can get the information they need at any time. This allows mobile phone shops to provide high-quality support to customers while achieving efficient store operations. For example, by having the system provide instructions on how to use the devices, store staff can concentrate on contract-related tasks. Furthermore, the mobile phone shop support system also addresses the psychological needs of customers, providing a sense of security through natural conversation. This allows customers to use the store in a relaxed manner.This allows the mobile phone shop support system to efficiently provide instructions on how to use and operate different models, reducing the burden on store staff and enabling efficient store operations.

[0064] The mobile phone shop support system according to this embodiment comprises a provision unit, an information provision unit, a question response unit, and a response unit. The provision unit provides instructions on how to use and operate the device through video and audio. For example, the provision unit guides users through basic operation and settings of a smartphone using video and audio. For example, the provision unit can explain how to turn on a smartphone, how to install apps, and how to change settings using video and audio. For example, the provision unit can visually explain basic smartphone operation methods using video and provide supplementary explanations with audio. For example, the provision unit can guide users through smartphone settings step-by-step using video and audio. The information provision unit provides information tailored to individual needs based on the information provided by the provision unit. For example, the information provision unit responds immediately to customer questions and provides clear explanations. For example, if a customer asks about a specific function, the information provision unit can provide a detailed explanation of that function. For example, if a customer asks about how to use a specific smartphone app, the information provision unit can provide a clear explanation of how to use that app. The Information Department can, for example, provide detailed instructions on how to change smartphone settings if a customer asks about it. The Question Response Department answers basic questions based on the information provided by the Information Department. The Question Response Department can, for example, learn from customer responses and evolve to provide more effective responses. The Question Response Department can, for example, analyze customer responses and reflect them in future responses. The Question Response Department can, for example, collect customer responses as feedback data and improve its response methods using machine learning algorithms. The Question Response Department can, for example, analyze customer responses in real time and immediately adjust its response methods. The Response Department provides 24-hour support based on the information provided by the Question Response Department. The Response Department can, for example, provide 24-hour support, allowing customers to obtain the information they need at any time. The Response Department can, for example, provide 24-hour support using an automated response system. The Response Department can, for example, implement 24-hour support by employing staff on a shift system.The support unit can, for example, use an AI agent to provide 24-hour support. As a result, the mobile phone shop support system according to this embodiment can efficiently provide instructions on how to use and operate the devices, reduce the burden on store staff, and enable efficient store operations.

[0065] The service provider will provide instructions on how to use and operate the device through video and audio. Specifically, they will guide users through basic smartphone operation and settings using video and audio. For example, they can explain how to turn on a smartphone, how to install apps, and how to change settings using video and audio. The service provider can visually explain basic smartphone operation using video and provide supplementary explanations with audio. This allows users to receive information using both sight and hearing, leading to a deeper understanding. Furthermore, the service provider can guide users through smartphone setup step by step using video and audio. For example, for first-time smartphone users, they can explain everything from how to turn on the power to setting up Wi-Fi, setting up email accounts, and downloading apps. This allows users to learn at their own pace and gain confidence in their operation. In addition, the service provider can adjust the speed and content of the explanation according to the user's level of understanding. For example, they can explain slowly and in detail for beginners and concisely focus on key points for experienced users. This allows the service provider to cater to a wide range of users and provide information tailored to individual needs. Furthermore, the service provider can regularly update video and audio content to provide the latest information. For example, when a new model is released or a software update is made, they can add new explanations corresponding to the changes. This allows the service provider to always provide the latest information and increase user satisfaction.

[0066] The Information Provision Department provides information tailored to individual needs based on the information provided by the department. Specifically, it responds immediately to customer questions and provides clear explanations. For example, if a customer asks about a specific function, the Information Provision Department can provide a detailed explanation of that function. If a customer asks about how to use a specific smartphone app, the Information Provision Department can provide a clear explanation of how to use that app. For example, if a customer asks about how to use the camera app, the Information Provision Department will provide a detailed explanation of how to take photos, use filters, and edit photos. Also, if a customer asks about changing smartphone settings, the Information Provision Department can provide a detailed explanation of how to change those settings. For example, it will provide specific instructions on how to change notification settings and how to adjust privacy settings. The Information Provision Department has an extensive knowledge base to respond quickly and accurately to user questions. Furthermore, the Information Provision Department records the user's question history and can provide more appropriate information by referring to past questions. As a result, the Information Provision Department can provide customized information tailored to user needs and increase user satisfaction.

[0067] The question support department handles basic questions based on information provided by the information provision department. Specifically, it learns from customer responses and evolves to provide more effective responses. For example, it can analyze customer responses and reflect them in future responses. The question support department can collect customer responses as feedback data and improve its response methods using machine learning algorithms. For example, it can evaluate whether a customer was satisfied with a particular question and adjust its response method based on that evaluation. Furthermore, the question support department can analyze customer responses in real time and adjust its response method immediately. For example, if a customer expresses dissatisfaction with a question, it can change its response method on the spot and provide a more appropriate answer. In this way, the question support department can always provide the best possible response and increase user satisfaction. In addition, the question support department can provide customized responses for each user based on past response history. For example, for a user who has asked the same question in the past, it can provide more detailed information by referring to the previous answer. In this way, the question support department can provide flexible responses tailored to user needs and gain user trust.

[0068] The support department provides 24-hour support based on information gathered by the question support department. Specifically, it offers 24-hour support, allowing customers to obtain necessary information at any time. For example, 24-hour support can be provided using an automated response system. The automated response system automatically provides answers to customer questions based on pre-set scenarios. This allows customers to obtain necessary information even during times when staff are unavailable, such as late at night or early in the morning. Furthermore, the support department can achieve 24-hour support by employing staff on a shift system. For example, staff can work in shifts, ensuring that someone is always available to answer questions. This allows for a rapid response in emergencies. The support department can also provide 24-hour support using an AI agent. The AI ​​agent uses natural language processing technology to understand customer questions and provide appropriate answers. For example, if a customer asks about how to set up their smartphone, the AI ​​agent will understand the question and guide them through the specific setup process. This allows the support department to respond to customer questions 24 hours a day and provide quick and accurate information. In addition, the support department can collect user feedback and use it to improve its support methods. For example, by reviewing response procedures based on user evaluations and feedback, we can provide better service. This allows the support department to consistently deliver high-quality service and increase user satisfaction.

[0069] The service provider can guide users through basic smartphone operation and settings using video and audio. For example, the service provider can visually explain how to turn on a smartphone using video and provide supplementary audio explanations. For example, the service provider can guide users through app installation step-by-step using video and audio. For example, the service provider can provide detailed explanations of how to change settings using video and audio. This helps customers understand basic smartphone operation and settings by providing easy-to-understand guidance. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can have a generation AI generate videos and audio explaining how to operate a smartphone.

[0070] The Information Department can respond immediately to customer inquiries and provide clear explanations. For example, if a customer asks about a specific function, the Information Department can provide a detailed explanation of that function. For example, if a customer asks about how to use a specific smartphone app, the Information Department can provide a clear explanation of how to use that app. For example, if a customer asks about changing smartphone settings, the Information Department can provide a detailed explanation of how to change those settings. By responding immediately to customer inquiries and providing clear explanations, customer satisfaction is improved. Some or all of the above processing in the Information Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Information Department can input the customer's question into a generative AI, which can then generate an answer to the question.

[0071] The question response unit can learn from customer responses and evolve to provide more effective responses. For example, the question response unit can analyze customer responses and reflect them in future responses. For example, the question response unit can collect customer responses as feedback data and improve its response methods using machine learning algorithms. For example, the question response unit can analyze customer responses in real time and immediately adjust its response methods. This allows it to learn from customer responses and evolve its responses to provide more effective support. Some or all of the above processes in the question response unit may be performed using, for example, generative AI, or not using generative AI. For example, the question response unit can input customer response data into generative AI, which can then generate feedback to improve its response methods.

[0072] The support department is available 24 hours a day, and customers can obtain the information they need at any time. The support department can provide 24-hour support, for example, by using an automated response system. The support department can achieve 24-hour support, for example, by using a shift-based staffing system. The support department can provide 24-hour support, for example, by using an AI agent. As a result, 24-hour support is possible, and customers can obtain the information they need at any time. Some or all of the above-described processes in the support department may be performed using, for example, a generative AI, or not using a generative AI. For example, the support department can have a generative AI execute a 24-hour automated response system.

[0073] The service provider can estimate the customer's emotions and adjust the way video and audio are delivered based on those estimated emotions. For example, if the customer is feeling anxious, the service provider can provide video and audio with a calm tone and slow explanations. If the customer is excited, the service provider can provide video and audio with a bright and energetic tone. If the customer is tired, the service provider can provide video and audio with simple and short explanations. By adjusting the way video and audio are delivered according to the customer's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input customer emotion data into a generative AI, which can then adjust the way video and audio are delivered based on the emotion.

[0074] The service provider can customize the content of the videos and audio they provide based on the customer's past question history. For example, the service provider can prioritize providing relevant operational instructions based on the questions the customer has asked in the past. For example, the service provider can provide videos and audio that explain content that the customer previously found difficult to understand. For example, the service provider can provide videos and audio that provide detailed explanations of features that the customer has previously shown interest in. In this way, by customizing the content of videos and audio based on the customer's past question history, more appropriate information is provided. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or not using a generating AI. For example, the service provider can input the customer's past question history data into a generating AI, and the generating AI can generate customized video and audio content.

[0075] The service provider can automatically adjust the speed of the video and audio they provide according to the customer's level of understanding. For example, the service provider can provide video and audio that explains things at a speed that is easy for the customer to understand. For example, if the customer prefers a fast-paced explanation, the service provider can provide video and audio at a faster speed. For example, if the customer prefers a slower explanation, the service provider can provide video and audio at a slower speed. In this way, by adjusting the speed of the video and audio according to the customer's level of understanding, the service provider can provide information that is easier to understand. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or not using a generation AI. For example, the service provider can input customer understanding data into a generation AI, and the generation AI can automatically adjust the speed of the video and audio.

[0076] The service provider can estimate the customer's emotions and determine the priority of information to provide based on the estimated emotions. For example, if the customer is anxious, the service provider will prioritize providing important information. For example, if the customer is relaxed, the service provider can provide detailed information sequentially. For example, if the customer is confused, the service provider can prioritize providing basic information. This allows for the provision of more appropriate information by prioritizing information according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input customer emotion data into a generative AI, which can then determine the priority of information based on the emotions.

[0077] The service provider can adjust the content of the video and audio they provide according to the customer's age and digital literacy. For example, the service provider can provide video and audio with a slow pace of explanation to elderly customers. For example, the service provider can provide video and audio that explains basic operations to customers with low digital literacy. For example, the service provider can provide video and audio that provides speedy and detailed explanations to young customers. In this way, by adjusting the content of the video and audio according to the customer's age and digital literacy, more appropriate information is provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the customer's age and digital literacy data into a generative AI, and the generative AI can adjust the content of the video and audio.

[0078] The service provider can optimize the video and audio content they provide according to the type of device the customer is using. For example, the service provider can provide video and audio optimized for the smartphone screen size to customers using smartphones. For example, the service provider can provide video and audio optimized for the larger screen to customers using tablets. For example, the service provider can provide video and audio optimized for the PC screen size to customers using PCs. By optimizing the video and audio content according to the type of device the customer is using, the service provider can provide more appropriate information. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input customer device type data into a generation AI, which can then optimize the video and audio content.

[0079] The information provision department can estimate the customer's emotions and adjust the way information is presented based on the estimated emotions. For example, if the customer is feeling anxious, the information provision department can provide information in a calm tone. For example, if the customer is excited, the information provision department can provide information in a bright and cheerful tone. For example, if the customer is tired, the information provision department can provide simple and short information. In this way, by adjusting the way information is presented according to the customer's emotions, more appropriate information is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision department may be performed using a generative AI, or not using a generative AI. For example, the information provision department can input customer emotion data into a generative AI, and the generative AI can adjust the way information is presented based on the emotion.

[0080] The information provision department can analyze a customer's past question history and select the most appropriate method of providing information. For example, the information provision department can prioritize providing relevant information based on the content of questions the customer has asked in the past. For example, the information provision department can provide information that explains content that the customer found difficult to understand in the past. For example, the information provision department can provide detailed information about features that the customer has shown interest in in the past. In this way, by analyzing the customer's past question history, the information provision department can select the most appropriate method of providing information and provide more relevant information. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or not using a generative AI. For example, the information provision department can input the customer's past question history data into a generative AI, and the generative AI can select the most appropriate method of providing information.

[0081] The information provision department can adjust the timing of information provision based on the customer's current situation. For example, if the customer is in a hurry, the information provision department can provide information quickly. For example, if the customer is relaxed, the information provision department can provide detailed information sequentially. For example, if the customer is confused, the information provision department can prioritize providing basic information. By adjusting the timing of information provision according to the customer's current situation, more appropriate information can be provided. Some or all of the above processing in the information provision department may be performed using, for example, a generating AI, or without a generating AI. For example, the information provision department can input the customer's current situation data into a generating AI, and the generating AI can adjust the timing of information provision.

[0082] The information provision unit can estimate the customer's emotions and adjust the level of detail of the information based on the estimated emotions. For example, if the customer is feeling anxious, the information provision unit can provide detailed information. For example, if the customer is excited, the information provision unit can provide concise information. For example, if the customer is tired, the information provision unit can provide simple and short information. In this way, by adjusting the level of detail of the information according to the customer's emotions, more appropriate information is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provision unit may be performed using a generative AI, for example, or without a generative AI. For example, the information provision unit can input customer emotion data into a generative AI, and the generative AI can adjust the level of detail of the information based on the emotion.

[0083] The information provision department can prioritize providing highly relevant information by taking into account the customer's geographical location. For example, the information provision department can prioritize providing information related to the customer's current location. For example, the information provision department can provide information related to places the customer has visited in the past. For example, the information provision department can provide information related to places the customer plans to visit in the future. By providing highly relevant information based on the customer's geographical location, the department can provide more appropriate information. Some or all of the above processing in the information provision department may be performed using, for example, a generative AI, or without a generative AI. For example, the information provision department can input the customer's geographical location data into a generative AI, which can then prioritize providing highly relevant information.

[0084] The Information Provision Department can analyze customers' social media activity and provide relevant information. For example, the Information Provision Department can provide information related to the content that customers have shown interest in on social media. For example, the Information Provision Department can provide information related to the content that customers have shared on social media. For example, the Information Provision Department can provide information related to the accounts that customers follow on social media. By analyzing customers' social media activity, the Information Provision Department can provide relevant information and offer more appropriate support. Some or all of the above processing in the Information Provision Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Information Provision Department can input the customer's social media activity data into a generative AI, and the generative AI can provide relevant information.

[0085] The question response unit can estimate the customer's emotions and adjust its response method based on the estimated emotions. For example, if the customer is feeling anxious, the question response unit can provide a polite and calm response. If the customer is excited, the question response unit can provide a cheerful and energetic response. If the customer is tired, the question response unit can provide a concise and quick response. By adjusting the response method according to the customer's emotions, more appropriate support can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question response unit may be performed using a generative AI, or not using a generative AI. For example, the question response unit can input customer emotion data into a generative AI, which can then adjust its response method based on the emotion.

[0086] The question response unit can analyze the customer's past question history and select the most appropriate response method. For example, the question response unit can prioritize providing relevant answers based on the content of questions the customer has asked in the past. For example, the question response unit can provide answers that explain content that the customer previously found difficult to understand. For example, the question response unit can provide detailed answers about features that the customer has previously shown interest in. In this way, by analyzing the customer's past question history, the optimal response method is selected and more appropriate answers are provided. Some or all of the above processing in the question response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question response unit can input the customer's past question history data into a generative AI, and the generative AI can select the most appropriate response method.

[0087] The question response unit can adjust the timing of its response based on the customer's current situation. For example, if the customer is in a hurry, the question response unit will provide a quick answer. For example, if the customer is relaxed, the question response unit can provide detailed answers sequentially. For example, if the customer is confused, the question response unit can prioritize providing basic answers. By adjusting the timing of the response according to the customer's current situation, it is possible to provide more appropriate answers. Some or all of the above processing in the question response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the question response unit can input data on the customer's current situation into a generative AI, which can then adjust the timing of the response.

[0088] The question response unit can estimate the customer's emotions and determine the priority of question responses based on the estimated emotions. For example, if the customer is anxious, the question response unit will prioritize important questions. If the customer is relaxed, the question response unit can sequentially answer detailed questions. If the customer is confused, the question response unit can prioritize basic questions. This allows for more appropriate support by determining the priority of question responses according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question response unit may be performed using a generative AI, or not. For example, the question response unit can input customer emotion data into a generative AI, which can then determine the priority of question responses based on the emotions.

[0089] The question response unit can prioritize responding to highly relevant questions by taking into account the customer's geographical location. For example, the question response unit can prioritize responding to questions related to the customer's current location. For example, the question response unit can respond to questions related to places the customer has visited in the past. For example, the question response unit can respond to questions related to places the customer plans to visit in the future. This allows for more appropriate support by responding to highly relevant questions based on the customer's geographical location. Some or all of the above processing in the question response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the question response unit can input the customer's geographical location data into a generative AI, which can then prioritize responding to highly relevant questions.

[0090] The question response unit can analyze the customer's social media activity and respond to relevant questions. For example, the question response unit can respond to questions related to the content the customer has shown interest in on social media. For example, the question response unit can respond to questions related to the content the customer has shared on social media. For example, the question response unit can respond to questions related to the accounts the customer follows on social media. In this way, by analyzing the customer's social media activity, it can respond to relevant questions and provide more appropriate support. Some or all of the above processing in the question response unit may be performed using, for example, generative AI, or not using generative AI. For example, the question response unit can input the customer's social media activity data into a generative AI, and the generative AI can respond to relevant questions.

[0091] The response unit can estimate the customer's emotions and adjust its response method based on the estimated emotions. For example, if the customer is feeling anxious, the response unit can provide a polite and calm response. For example, if the customer is excited, the response unit can provide a cheerful and energetic response. For example, if the customer is tired, the response unit can provide a concise and quick response. By adjusting the response method according to the customer's emotions, more appropriate support is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using a generative AI, or not using a generative AI. For example, the response unit can input customer emotion data into a generative AI, and the generative AI can adjust its response method based on the emotion.

[0092] The support unit can analyze the customer's past question history and select the most appropriate response method. For example, the support unit can prioritize providing relevant responses based on the content of questions the customer has asked in the past. For example, the support unit can provide responses that explain content that the customer found difficult to understand in the past. For example, the support unit can provide detailed responses regarding features that the customer has shown interest in in the past. In this way, by analyzing the customer's past question history, the support unit can select the most appropriate response method and provide more appropriate support. Some or all of the above processing in the support unit may be performed using, for example, a generative AI, or without a generative AI. For example, the support unit can input the customer's past question history data into a generative AI, which can then select the most appropriate response method.

[0093] The response unit can adjust the timing of its response based on the customer's current situation. For example, if the customer is in a hurry, the response unit will provide a quick response. For example, if the customer is relaxed, the response unit can provide detailed responses sequentially. For example, if the customer is confused, the response unit can prioritize providing basic responses. This allows for more appropriate support by adjusting the timing of the response according to the customer's current situation. Some or all of the above processing in the response unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the response unit can input the customer's current situation data into a generating AI, which can then adjust the timing of the response.

[0094] The response unit can estimate the customer's emotions and determine the priority of responses based on the estimated emotions. For example, if the customer is anxious, the response unit will prioritize important responses. If the customer is relaxed, the response unit can provide detailed responses sequentially. If the customer is confused, the response unit can prioritize basic responses. This allows for more appropriate support by determining the priority of responses according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using a generative AI, or not using a generative AI. For example, the response unit can input customer emotion data into a generative AI, which can then determine the priority of responses based on the emotions.

[0095] The response unit can prioritize responses that are highly relevant to the customer, taking into account the customer's geographical location. For example, the response unit can prioritize responses related to the customer's current location. For example, the response unit can provide responses related to places the customer has visited in the past. For example, the response unit can provide responses related to places the customer plans to visit in the future. This allows for more appropriate support by providing responses that are highly relevant based on the customer's geographical location. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input the customer's geographical location data into a generative AI, which can then prioritize responses that are highly relevant.

[0096] The response unit can analyze the customer's social media activity and take relevant actions. For example, the response unit can take actions related to the content the customer has shown interest in on social media. For example, the response unit can take actions related to the content the customer has shared on social media. For example, the response unit can take actions related to the accounts the customer follows on social media. By analyzing the customer's social media activity, the response unit can take relevant actions and provide more appropriate support. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input the customer's social media activity data into a generative AI, which can then take relevant actions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The service provider can adjust the method of delivering video and audio according to the battery level of the user's device. For example, if the battery level is low, the service provider can deliver short video and audio. Conversely, if the battery level is sufficient, it can deliver detailed video and audio. In addition, if the battery level is low, the service provider can deliver in power-saving mode. This allows users to obtain information without worrying about their battery level.

[0099] The information provision department can provide relevant information based on the user's past purchase history. For example, it can provide information about smartphone accessories the user has purchased in the past. It can also provide upgrade information for models the user has purchased in the past. Furthermore, it can prioritize providing support information for products the user has purchased in the past. This allows users to quickly obtain information relevant to them.

[0100] The question-answering unit can estimate the user's emotions from their tone of voice and manner of speaking, and adjust its response based on the estimated emotion. For example, if the user is angry, the question-answering unit can respond calmly and composedly. Conversely, if the user is happy, the question-answering unit can respond brightly and cheerfully. Also, if the user is sad, the question-answering unit can respond gently and politely. In this way, users can receive appropriate responses that match their emotions.

[0101] The system can adjust how information is provided based on the user's current network connection status. For example, if the network connection is unstable, the system can prioritize text-based information. Conversely, if the network connection is stable, it can provide information using video and audio. Furthermore, if the network connection is interrupted, it can provide information that can be used offline. This ensures that users can obtain the necessary information regardless of their network connection status.

[0102] The information delivery system can customize the method of information delivery according to the user's learning style. For example, users who prefer visual learning can be provided with video-based information. Conversely, users who prefer auditory learning can be provided with audio-based information. Furthermore, users who prefer practical learning can be provided with interactive content. This allows users to obtain information in a way that suits their learning style.

[0103] The information delivery unit can estimate the user's emotions and adjust the way information is presented based on those estimates. For example, if a user is feeling anxious, information can be delivered in a calm tone. Conversely, if a user is excited, information can be delivered in a bright and cheerful tone. Also, if a user is tired, simple and concise information can be delivered. This allows users to receive information appropriate to their emotions.

[0104] The question response unit can analyze a user's past question history and select the most appropriate response method. For example, it can prioritize providing relevant answers based on the user's past questions. It can also provide answers that explain concepts the user previously found difficult to understand. Furthermore, it can provide detailed answers regarding features the user has shown interest in in the past. This allows users to quickly obtain information relevant to them.

[0105] The response unit can estimate the user's emotions and determine the priority of responses based on those emotions. For example, if the user is anxious, important responses can be prioritized. Conversely, if the user is relaxed, detailed responses can be provided sequentially. Also, if the user is confused, basic responses can be prioritized. This ensures that users receive appropriate responses that match their emotions.

[0106] The information delivery system can optimize the method of information delivery according to the type of device the user is using. For example, users using smartphones can be provided with video and audio optimized for the smartphone screen size. Conversely, users using tablets can be provided with video and audio optimized for larger screens. Furthermore, users using PCs can be provided with video and audio optimized for their PC screen size. This allows users to obtain information optimized for their own device.

[0107] The response unit can estimate the user's emotions and adjust its response method based on those estimates. For example, if the user is feeling anxious, it can provide a polite and calm response. Conversely, if the user is excited, it can provide a cheerful and energetic response. Also, if the user is tired, it can provide a concise and quick response. This ensures that users receive appropriate responses that match their emotions.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The service provider will provide instructions on how to use and operate the device through video and audio. For example, they will guide users through the basic operation and settings of a smartphone using video and audio. Specifically, they can explain how to turn on a smartphone, how to install apps, and how to change settings using video and audio. They can also visually explain the basic operation of a smartphone using video and provide supplementary explanations with audio. Furthermore, they can guide users through the smartphone's settings step by step using video and audio. Step 2: The information provision department provides information tailored to individual needs based on the information provided by the department. For example, they respond immediately to customer questions and provide clear explanations. Specifically, if a customer asks about a particular function, they can provide a detailed explanation of that function. Also, if a customer asks about how to use a particular smartphone app, they can provide a clear explanation of how to use that app. Furthermore, if a customer asks about changing smartphone settings, they can provide a detailed explanation of how to change those settings. Step 3: The question response unit answers basic questions based on the information provided by the information provision unit. For example, it learns from customer responses and evolves to provide more effective responses. Specifically, it can analyze customer responses and reflect them in future responses. It can also collect customer responses as feedback data and use machine learning algorithms to improve response methods. Furthermore, it can analyze customer responses in real time and adjust response methods immediately. Step 4: The support department provides 24-hour support based on the information provided by the question support department. For example, 24-hour support is available, allowing customers to obtain the information they need at any time. Specifically, 24-hour support can be provided using an automated response system. Alternatively, 24-hour support can be achieved by employing staff on a shift system. Furthermore, 24-hour support can be provided using an AI agent.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the provision unit, information provision unit, question response unit, and response unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the smart device 14 and provides guidance on basic operation and settings of the smartphone using video and audio. The information provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides information tailored to individual needs based on the information provided by the provision unit. The question response unit is implemented, for example, by the control unit 46A of the smart device 14 and evolves to learn customer responses and provide more effective responses. The response unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and can provide 24-hour support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the provision unit, information provision unit, question response unit, and response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the smart glasses 214 and provides guidance on basic operation and settings of a smartphone using video and audio. The information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information tailored to individual needs based on the information provided by the provision unit. The question response unit is implemented by the control unit 46A of the smart glasses 214 and learns customer responses to evolve and provide more effective responses. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and can provide 24-hour support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the provision unit, information provision unit, question response unit, and response unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the headset terminal 314 and provides guidance on basic operation and settings of the smartphone using video and audio. The information provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides information tailored to individual needs based on the information provided by the provision unit. The question response unit is implemented, for example, by the control unit 46A of the headset terminal 314 and evolves to learn customer responses and provide more effective responses. The response unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and can provide 24-hour support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the provision unit, information provision unit, question response unit, and response unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the provision unit is implemented by the control unit 46A of the robot 414 and provides guidance on basic operation and settings of a smartphone using video and audio. The information provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides information tailored to individual needs based on the information provided by the provision unit. The question response unit is implemented by, for example, the control unit 46A of the robot 414 and evolves to learn customer responses and provide more effective responses. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and can provide 24-hour support. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) The service department provides instructions on how to use and operate the device through video and audio. An information provision unit provides information tailored to individual needs based on the information provided by the aforementioned provision unit, A question response unit that responds to basic questions based on the information provided by the aforementioned information provision unit, The system includes a response unit that can respond 24 hours a day based on the information handled by the aforementioned question response unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, This service provides video and audio guidance on basic smartphone operation and settings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned information provision unit, Responding immediately to customer questions and providing clear explanations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned question handling unit is: It learns from customer reactions and evolves to provide more effective responses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The corresponding part is, We offer 24-hour support, so customers can get the information they need at any time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We estimate the customer's emotions and adjust the way we deliver video and audio based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, The video and audio content provided will be customized based on the customer's past question history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, The speed of the video and audio provided will be automatically adjusted according to the customer's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, We estimate the customer's emotions and prioritize the information we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, The video and audio content provided will be adjusted according to the customer's age and digital literacy. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, The video and audio content provided will be optimized according to the type of device the customer is using. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information provision unit, We estimate the customer's emotions and adjust the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned information provision unit, We analyze the customer's past question history and select the most appropriate method of providing information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned information provision unit, We will adjust the timing of information provision based on the customer's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned information provision unit, We estimate the customer's emotions and adjust the level of detail of the information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned information provision unit, We prioritize providing highly relevant information by taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned information provision unit, We analyze our customers' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question handling unit is: We estimate the customer's emotions and adjust our questioning methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned question handling unit is: We analyze the customer's past question history and select the most appropriate answer method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned question handling unit is: We will adjust the timing of our response to your questions based on your current situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned question handling unit is: We estimate the customer's emotions and prioritize how to respond to their questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned question handling unit is: We take your geographical location into consideration and prioritize responding to the most relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned question handling unit is: We analyze our customers' social media activity and respond to related questions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The corresponding part is, We estimate the customer's emotions and adjust our response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The corresponding part is, We analyze the customer's past question history and select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The corresponding part is, We will adjust the timing of our response based on the customer's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The corresponding part is, We estimate the customer's emotions and determine the priority of our response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The corresponding part is, We prioritize providing highly relevant support by taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The corresponding part is, We analyze our customers' social media activity and take appropriate action. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The service department provides instructions on how to use and operate the device through video and audio. An information provision unit provides information tailored to individual needs based on the information provided by the aforementioned provision unit, A question response unit that responds to basic questions based on the information provided by the aforementioned information provision unit, The system includes a response unit that can respond 24 hours a day based on the information handled by the aforementioned question response unit. A system characterized by the following features.

2. The aforementioned supply unit is, This service provides video and audio guidance on basic smartphone operation and settings. The system according to feature 1.

3. The aforementioned information provision unit, Responding immediately to customer questions and providing clear explanations. The system according to feature 1.

4. The aforementioned question handling unit is: It learns from customer reactions and evolves to provide more effective responses. The system according to feature 1.

5. The corresponding part is, We offer 24-hour support, so customers can get the information they need at any time. The system according to feature 1.

6. The aforementioned supply unit is, We estimate the customer's emotions and adjust the way we deliver video and audio based on those estimated emotions. The system according to feature 1.

7. The aforementioned supply unit is, The video and audio content provided will be customized based on the customer's past question history. The system according to feature 1.

8. The aforementioned supply unit is, The speed of the video and audio provided will be automatically adjusted according to the customer's level of understanding. The system according to feature 1.

9. The aforementioned supply unit is, We estimate the customer's emotions and prioritize the information we provide based on those estimated emotions. The system according to feature 1.

10. The aforementioned supply unit is, The video and audio content provided will be adjusted according to the customer's age and digital literacy. The system according to feature 1.

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