system

Abstract

The system according to this embodiment is customizable according to industry and usage scenario, and aims to provide secure communication. [Solution] The system according to the embodiment comprises a customization unit, a cloud provision unit, and a communication unit. The customization unit allows the user to customize the avatar, prompts, and response language according to their industry or usage scenario. The cloud provision unit provides the customized information on the cloud. The communication unit securely communicates the information provided by the cloud provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies had challenges such as difficulty in customizing them to suit different industries and usage scenarios, and the inability to ensure secure communication.

[0005] The system according to this embodiment is customizable according to industry and usage scenario, and aims to provide secure communication. [Means for solving the problem]

[0006] The system according to this embodiment comprises a customization unit, a cloud provision unit, and a communication unit. The customization unit allows users to customize avatars, prompts, and response languages ​​according to their industry or usage scenario. The cloud provision unit provides the customized information via the cloud. The communication unit securely communicates the information provided by the cloud provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can be customized according to the type of business or the usage scenario, and can provide secure communication. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​chatbot system according to an embodiment of the present invention allows users (user companies and government agencies) to customize avatars, prompts, and response languages ​​individually for each industry and usage scenario. This system is available on multiple platforms, including displays, smart mirrors, and smart devices, and is widely used in various industries and public service fields. Because it is provided on the cloud, it offers a learning database customizable for each user, a learning database common to all users, and secure communication lines. For example, users can customize avatars, prompts, and response languages ​​according to their industry and usage scenario. For example, a medical institution might use avatars of doctors or nurses, and prompts might include medical terminology. Response languages ​​are also multilingual, making it possible to accommodate foreign patients. The customized AI chatbot can then be used on multiple platforms, including displays, smart mirrors, and smart devices. For example, when a patient enters a question on a display installed at a hospital reception desk, the AI ​​chatbot provides an appropriate answer. Furthermore, a smart mirror can be used to provide health consultations at home. Furthermore, because it is provided on the cloud, a learning database customizable for each user is available. For example, a specific hospital's database might contain the hospital's medical records and patient information. Furthermore, a learning database common to all users is provided, allowing access to general medical knowledge and the latest medical information. Finally, a secure communication line is provided, protecting user privacy. For example, patient personal information and medical records are not leaked externally, and communication is conducted securely. This leads to increased efficiency in various industries and public service sectors, improving user convenience. For example, medical institutions can streamline reception operations and reduce patient waiting times. In the public service sector, inquiries from residents can be responded to quickly, improving the quality of service. As a result, the AI ​​chatbot system can provide information customized by users according to their industry and usage scenarios on the cloud, and communicate securely.

[0029] The AI ​​chatbot system according to this embodiment comprises a customization unit, a cloud provision unit, and a communication unit. The customization unit allows users to customize avatars, prompts, and response languages ​​according to their industry and usage scenarios. For example, the customization unit allows users in medical institutions to select avatars of doctors or nurses and include medical terminology in prompts. The customization unit can also set the response language to support multiple languages, enabling support for foreign patients. The cloud provision unit provides the customized information on the cloud. For example, the cloud provision unit manages a customizable learning database for each user, and a database for a specific hospital can include the hospital's medical records and patient information. The cloud provision unit also provides a learning database common to all users, allowing them to utilize general medical knowledge and the latest medical information. The communication unit securely communicates the information provided by the cloud provision unit. For example, the communication unit provides a secure communication line to ensure that patient personal information and medical records are not leaked to external parties. As a result, the AI ​​chatbot system according to this embodiment can provide information customized by users according to their industry and usage scenarios on the cloud and communicate securely.

[0030] The customization unit can analyze the user's past customization history and suggest optimal customization options. For example, the customization unit can suggest similar options based on avatar styles and prompts previously selected by the user. The customization unit can also prioritize suggestions based on response languages ​​previously used by the user. The customization unit can also suggest options suitable for specific industries or usage scenarios based on the user's past customization history. This improves the efficiency of customization by suggesting optimal options based on the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, or without, AI, for example. For example, the customization unit can input the user's past customization history data into a generation AI and cause the generation AI to suggest optimal customization options.

[0031] During customization, the customization unit can automatically suggest appropriate prompts and response languages ​​according to the user's industry and usage scenario. For example, the customization unit can suggest prompts including medical terminology and multilingual response languages ​​to users at medical institutions. The customization unit can also suggest prompts including educational terminology and response languages ​​for students to users at educational institutions. The customization unit can also suggest prompts for residents and multilingual response languages ​​to users of public services. This automatically suggests appropriate prompts and response languages ​​according to the user's industry and usage scenario, thereby reducing the effort required for customization. Some or all of the above-described processing by the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data related to the user's industry and usage scenario into the generation AI and cause the generation AI to suggest appropriate prompts and response languages.

[0032] The customization unit can provide region-specific avatars and prompts based on the user's geographical location information during customization. For example, if the user is in Japan, the customization unit can provide an avatar that reflects Japanese culture and scenery. If the user is in the United States, the customization unit can also provide prompts that reflect American culture and scenery. If the user is in France, the customization unit can also provide a response language that reflects French culture and scenery. In this way, region-specific avatars and prompts can be provided by taking the user's geographical location information into consideration. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing region-specific avatars and prompts.

[0033] The customization unit can analyze the user's social media activity during customization and suggest relevant customization options. For example, the customization unit can suggest a response language based on the language the user frequently uses on social media. It can also suggest prompts based on topics the user shows interest in on social media. Furthermore, the customization unit can suggest a specific avatar style based on the user's social media activity. In this way, relevant customization options can be suggested by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant customization options.

[0034] The cloud provider can analyze the user's past usage history and select the optimal database management method when providing cloud services. For example, the cloud provider can prioritize managing data that the user has frequently accessed in the past. Furthermore, based on the user's past usage history, the cloud provider can efficiently manage data that experiences concentrated access during specific time periods. The cloud provider can also apply the optimal database management algorithm based on the user's past usage history. This allows for the selection of the optimal database management method by analyzing the user's past usage history. Some or all of the above processes in the cloud provider may be performed using AI, or without AI. For example, the cloud provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal database management method.

[0035] The cloud provider can apply different database management algorithms to users depending on their industry and usage scenario when providing cloud services. For example, the cloud provider can apply a database management algorithm specialized for medical data to users in medical institutions. It can also apply a database management algorithm specialized for educational data to users in educational institutions. Furthermore, it can apply a database management algorithm specialized for public data to users in public services. This enables efficient data management by applying database management algorithms tailored to the user's industry and usage scenario. Some or all of the above processing in the cloud provider may be performed using AI, for example, or without AI. For example, the cloud provider can input data related to the user's industry and usage scenario into a generating AI and have the generating AI execute the application of different database management algorithms.

[0036] The cloud provider can provide the optimal database management method when providing cloud services, taking into account the user's geographical location. For example, if the user is in Japan, the cloud provider will prioritize using a data center in Japan. Similarly, if the user is in the United States, the cloud provider can prioritize using a data center in the United States. Furthermore, if the user is in Europe, the cloud provider can prioritize using a data center in Europe. This allows the cloud provider to provide the optimal database management method by considering the user's geographical location. Some or all of the above processing in the cloud provider may be performed using AI, for example, or without AI. For example, the cloud provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal database management method.

[0037] The cloud provider can analyze the user's social media activity and propose relevant database management methods when providing cloud services. For example, the cloud provider can prioritize managing data that the user frequently uses on social media. It can also efficiently manage data related to topics the user shows interest in on social media. Furthermore, the cloud provider can propose specific database management methods based on the user's social media activity. This allows for the proposal of relevant database management methods by analyzing the user's social media activity. Some or all of the above processes in the cloud provider may be performed using AI, for example, or not. For example, the cloud provider can input the user's social media activity data into a generating AI and have the generating AI propose relevant database management methods.

[0038] The communication unit can analyze the user's past communication history and select the optimal communication method during communication. For example, the communication unit can prioritize communication methods that the user has frequently used in the past. The communication unit can also select the optimal communication method for a specific time period based on the user's past communication history. Furthermore, the communication unit can select the optimal communication protocol based on the user's past communication history. In this way, the optimal communication method can be selected by analyzing the user's past communication history. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's past communication history data into a generating AI and have the generating AI perform the selection of the optimal communication method.

[0039] The communications unit can apply different communication protocols depending on the user's industry and usage scenario during communication. For example, the communications unit can apply a communication protocol specialized for medical data to users in medical institutions. It can also apply a communication protocol specialized for educational data to users in educational institutions. Furthermore, it can apply a communication protocol specialized for public data to users of public services. This enables efficient communication by applying communication protocols tailored to the user's industry and usage scenario. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can input data about the user's industry and usage scenario into a generating AI and have the generating AI execute the application of different communication protocols.

[0040] The communication unit can provide the optimal communication method during communication by taking into account the user's geographical location information. For example, if the user is in Japan, the communication unit can prioritize the use of Japanese communication infrastructure. Furthermore, if the user is in the United States, the communication unit can also prioritize the use of American communication infrastructure. Furthermore, if the user is in Europe, the communication unit can also prioritize the use of European communication infrastructure. In this way, the optimal communication method can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's geographical location information into the generation AI and cause the generation AI to provide the optimal communication method.

[0041] The communication unit can analyze the user's social media activity during communication and suggest relevant communication methods. For example, the communication unit can prioritize and suggest communication methods that the user frequently uses on social media. The communication unit can also suggest communication methods related to topics in which the user is interested on social media. The communication unit can also suggest specific communication methods based on the user's social media activity. In this way, relevant communication methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the communication unit can be performed using, for example, AI, or can be performed without using AI. For example, the communication unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest relevant communication methods.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The customization section can analyze a user's past conversation history and automatically suggest avatar styles and prompts that the user prefers. For example, it can suggest similar styles based on avatar styles the user has previously selected. It can also suggest relevant prompts based on prompts the user has used in the past. Furthermore, it can suggest options suitable for specific usage scenarios based on the user's past conversation history. This improves the efficiency of customization by suggesting optimal options based on the user's past conversation history.

[0044] The customization feature can provide prompts about region-specific events and holidays based on the user's geographical location. For example, if the user is in Japan, it can provide prompts about Japanese holidays and local events. If the user is in the United States, it can provide prompts about American holidays and local events. Furthermore, if the user is in France, it can provide prompts about French holidays and local events. This allows for the provision of region-specific prompts by taking the user's geographical location into consideration.

[0045] The cloud service provider can propose an optimal database backup schedule based on the user's past usage history. For example, it can propose a schedule that prioritizes backing up data that the user has frequently accessed in the past. It can also propose a schedule that efficiently backs up data that is accessed at specific times of the day. Furthermore, it can propose the optimal backup method based on the user's past usage history. In this way, by analyzing the user's past usage history, it is possible to propose the optimal database backup schedule.

[0046] The cloud provider can select the optimal data center based on the user's geographical location information. For example, if the user is in Asia, the Asian data center can be used preferentially. If the user is in Europe, the European data center can be used preferentially. Furthermore, if the user is in North America, the North American data center can be used preferentially. In this way, the optimal data center can be selected by taking the user's geographical location information into consideration.

[0047] The communication unit can propose the optimal communication protocol based on the user's past communication history. For example, it can preferentially propose a communication protocol that the user has frequently used in the past. It can also propose the optimal communication protocol for a specific time period. Furthermore, it can propose the optimal communication method based on the user's past communication history. In this way, it is possible to propose the optimal communication protocol by analyzing the user's past communication history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The customization section allows users to customize the avatar, prompts, and response language according to their industry and usage scenario. For example, a user at a medical institution can select a doctor or nurse avatar and include medical terminology in the prompts. The response language can also be set to support multiple languages, making it possible to accommodate foreign patients. Step 2: The cloud provider provides the information customized by the customization unit on the cloud. For example, it manages a learning database that can be customized for each user, and a database for a specific hospital can include the medical records and patient information of that hospital. It also provides a learning database common to all users, allowing them to use general medical knowledge and the latest medical information. Step 3: The communications department securely transmits the information provided by the cloud provider department. For example, it provides a secure communication line to ensure that patient personal information and medical records are not leaked to external parties.

[0050] (Example 2) The AI ​​chatbot system according to an embodiment of the present invention allows users (user companies and government agencies) to customize avatars, prompts, and response languages ​​individually for each industry and usage scenario. This system is available on multiple platforms, including displays, smart mirrors, and smart devices, and is widely used in various industries and public service fields. Because it is provided on the cloud, it offers a learning database customizable for each user, a learning database common to all users, and secure communication lines. For example, users can customize avatars, prompts, and response languages ​​according to their industry and usage scenario. For example, a medical institution might use avatars of doctors or nurses, and prompts might include medical terminology. Response languages ​​are also multilingual, making it possible to accommodate foreign patients. The customized AI chatbot can then be used on multiple platforms, including displays, smart mirrors, and smart devices. For example, when a patient enters a question on a display installed at a hospital reception desk, the AI ​​chatbot provides an appropriate answer. Furthermore, a smart mirror can be used to provide health consultations at home. Furthermore, because it is provided on the cloud, a learning database customizable for each user is available. For example, a specific hospital's database might contain the hospital's medical records and patient information. Furthermore, a learning database common to all users is provided, allowing access to general medical knowledge and the latest medical information. Finally, a secure communication line is provided, protecting user privacy. For example, patient personal information and medical records are not leaked externally, and communication is conducted securely. This leads to increased efficiency in various industries and public service sectors, improving user convenience. For example, medical institutions can streamline reception operations and reduce patient waiting times. In the public service sector, inquiries from residents can be responded to quickly, improving the quality of service. As a result, the AI ​​chatbot system can provide information customized by users according to their industry and usage scenarios on the cloud, and communicate securely.

[0051] The AI ​​chatbot system according to this embodiment comprises a customization unit, a cloud provision unit, and a communication unit. The customization unit allows users to customize avatars, prompts, and response languages ​​according to their industry and usage scenarios. For example, the customization unit allows users in medical institutions to select avatars of doctors or nurses and include medical terminology in prompts. The customization unit can also set the response language to support multiple languages, enabling support for foreign patients. The cloud provision unit provides the customized information on the cloud. For example, the cloud provision unit manages a customizable learning database for each user, and a database for a specific hospital can include the hospital's medical records and patient information. The cloud provision unit also provides a learning database common to all users, allowing them to utilize general medical knowledge and the latest medical information. The communication unit securely communicates the information provided by the cloud provision unit. For example, the communication unit provides a secure communication line to ensure that patient personal information and medical records are not leaked to external parties. As a result, the AI ​​chatbot system according to this embodiment can provide information customized by users according to their industry and usage scenarios on the cloud and communicate securely.

[0052] The customization unit estimates the user's emotions and adjusts the avatar's facial expressions and movements based on the estimated emotions. For example, if the user is stressed, the customization unit adjusts the avatar's facial expression to be calm and its movements to be relaxed. It can also adjust the avatar's facial expression to be bright and its movements to be lively if the user is happy. Furthermore, if the user is tired, it can adjust the avatar's facial expression to be calm and its movements to be slow. This allows for more natural dialogue by adjusting the avatar's facial expressions and movements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the customization unit may be performed using AI, or not. For example, the customization unit can input user facial expression data into the generative AI and have the generative AI adjust the avatar's facial expressions and movements.

[0053] The customization unit can analyze the user's past customization history and suggest optimal customization options. For example, the customization unit can suggest similar options based on avatar styles and prompts previously selected by the user. The customization unit can also prioritize suggestions based on response languages ​​previously used by the user. The customization unit can also suggest options suitable for specific industries or usage scenarios based on the user's past customization history. This improves the efficiency of customization by suggesting optimal options based on the user's past customization history. Some or all of the above-described processing in the customization unit may be performed using, or without, AI, for example. For example, the customization unit can input the user's past customization history data into a generation AI and cause the generation AI to suggest optimal customization options.

[0054] During customization, the customization unit can automatically suggest appropriate prompts and response languages ​​according to the user's industry and usage scenario. For example, the customization unit can suggest prompts including medical terminology and multilingual response languages ​​to users at medical institutions. The customization unit can also suggest prompts including educational terminology and response languages ​​for students to users at educational institutions. The customization unit can also suggest prompts for residents and multilingual response languages ​​to users of public services. This automatically suggests appropriate prompts and response languages ​​according to the user's industry and usage scenario, thereby reducing the effort required for customization. Some or all of the above-described processing by the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can input data related to the user's industry and usage scenario into the generation AI and cause the generation AI to suggest appropriate prompts and response languages.

[0055] The customization unit can estimate the user's emotions and determine the priority of customizations based on those emotions. For example, if the user is in a hurry, the customization unit will prioritize displaying the most important customization options. If the user is relaxed, the customization unit can also display detailed customization options in order. If the user is stressed, the customization unit can also prioritize displaying simple customization options. This allows for optimal customization tailored to the user's situation by prioritizing customizations according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI determine the priority of customizations.

[0056] The customization unit can provide region-specific avatars and prompts based on the user's geographical location information during customization. For example, if the user is in Japan, the customization unit can provide an avatar that reflects Japanese culture and scenery. If the user is in the United States, the customization unit can also provide prompts that reflect American culture and scenery. If the user is in France, the customization unit can also provide a response language that reflects French culture and scenery. In this way, region-specific avatars and prompts can be provided by taking the user's geographical location information into consideration. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing region-specific avatars and prompts.

[0057] The customization unit can analyze the user's social media activity during customization and suggest relevant customization options. For example, the customization unit can suggest a response language based on the language the user frequently uses on social media. It can also suggest prompts based on topics the user shows interest in on social media. Furthermore, the customization unit can suggest a specific avatar style based on the user's social media activity. In this way, relevant customization options can be suggested by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or not. For example, the customization unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant customization options.

[0058] The cloud providing unit can estimate the user's emotions and adjust the update frequency of the learning database based on the estimated user emotions. For example, if the user frequently feels stressed, the cloud providing unit can increase the update frequency of the learning database to provide the latest information. Furthermore, if the user is relaxed, the cloud providing unit can maintain the normal update frequency of the learning database. Furthermore, if the user is in a hurry, the cloud providing unit can prioritize updating only important information. This allows the latest information to be provided by adjusting the update frequency of the learning database according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the cloud providing unit can be performed using, for example, an AI, or without an AI. For example, the cloud providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the update frequency of the learning database.

[0059] The cloud provider can analyze the user's past usage history and select the optimal database management method when providing cloud services. For example, the cloud provider can prioritize managing data that the user has frequently accessed in the past. Furthermore, based on the user's past usage history, the cloud provider can efficiently manage data that experiences concentrated access during specific time periods. The cloud provider can also apply the optimal database management algorithm based on the user's past usage history. This allows for the selection of the optimal database management method by analyzing the user's past usage history. Some or all of the above processes in the cloud provider may be performed using AI, or without AI. For example, the cloud provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal database management method.

[0060] The cloud provider can apply different database management algorithms to users depending on their industry and usage scenario when providing cloud services. For example, the cloud provider can apply a database management algorithm specialized for medical data to users in medical institutions. It can also apply a database management algorithm specialized for educational data to users in educational institutions. Furthermore, it can apply a database management algorithm specialized for public data to users in public services. This enables efficient data management by applying database management algorithms tailored to the user's industry and usage scenario. Some or all of the above processing in the cloud provider may be performed using AI, for example, or without AI. For example, the cloud provider can input data related to the user's industry and usage scenario into a generating AI and have the generating AI execute the application of different database management algorithms.

[0061] The cloud delivery unit can estimate the user's emotions and determine the priority of cloud deliveries based on the estimated emotions. For example, if the user is in a hurry, the cloud delivery unit will prioritize providing the most important data. If the user is relaxed, the cloud delivery unit can also provide data with normal priority. Furthermore, if the user is stressed, the cloud delivery unit can quickly provide important data. This allows for the rapid delivery of important data by determining the priority of cloud deliveries according to the user'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 cloud delivery unit may be performed using AI or not. For example, the cloud delivery unit can input user emotion data into a generative AI and have the generative AI determine the priority of cloud deliveries.

[0062] The cloud provider can provide the optimal database management method when providing cloud services, taking into account the user's geographical location. For example, if the user is in Japan, the cloud provider will prioritize using a data center in Japan. Similarly, if the user is in the United States, the cloud provider can prioritize using a data center in the United States. Furthermore, if the user is in Europe, the cloud provider can prioritize using a data center in Europe. This allows the cloud provider to provide the optimal database management method by considering the user's geographical location. Some or all of the above processing in the cloud provider may be performed using AI, for example, or without AI. For example, the cloud provider can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal database management method.

[0063] The cloud provider can analyze the user's social media activity and propose relevant database management methods when providing cloud services. For example, the cloud provider can prioritize managing data that the user frequently uses on social media. It can also efficiently manage data related to topics the user shows interest in on social media. Furthermore, the cloud provider can propose specific database management methods based on the user's social media activity. This allows for the proposal of relevant database management methods by analyzing the user's social media activity. Some or all of the above processes in the cloud provider may be performed using AI, for example, or not. For example, the cloud provider can input the user's social media activity data into a generating AI and have the generating AI propose relevant database management methods.

[0064] The communication unit can estimate the user's emotions and adjust the priority of communications based on the estimated user emotions. For example, when the user is in a hurry, the communication unit can prioritize important communications. Furthermore, when the user is relaxed, the communication unit can also perform communications with normal priority. Furthermore, when the user is stressed, the communication unit can also quickly perform important communications. In this way, by adjusting the priority of communications according to the user's emotions, important communications can be quickly performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the communication unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the priority of communications.

[0065] The communication unit can analyze the user's past communication history and select the optimal communication method during communication. For example, the communication unit can prioritize communication methods that the user has frequently used in the past. The communication unit can also select the optimal communication method for a specific time period based on the user's past communication history. Furthermore, the communication unit can select the optimal communication protocol based on the user's past communication history. In this way, the optimal communication method can be selected by analyzing the user's past communication history. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's past communication history data into a generating AI and have the generating AI perform the selection of the optimal communication method.

[0066] The communications unit can apply different communication protocols depending on the user's industry and usage scenario during communication. For example, the communications unit can apply a communication protocol specialized for medical data to users in medical institutions. It can also apply a communication protocol specialized for educational data to users in educational institutions. Furthermore, it can apply a communication protocol specialized for public data to users of public services. This enables efficient communication by applying communication protocols tailored to the user's industry and usage scenario. Some or all of the above processing in the communications unit may be performed using AI, for example, or without AI. For example, the communications unit can input data about the user's industry and usage scenario into a generating AI and have the generating AI execute the application of different communication protocols.

[0067] The communication unit can estimate the user's emotions and adjust the communication security level based on the estimated user emotions. For example, if the user is nervous, the communication unit can apply the highest level of security. Furthermore, if the user is relaxed, the communication unit can also apply a normal security level. Furthermore, if the user is in a hurry, the communication unit can prioritize quick communication while maintaining security. This allows appropriate security to be ensured by adjusting the communication security level according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the communication unit can input the user's emotion data into the generation AI and have the generation AI adjust the communication security level.

[0068] The communication unit can provide the optimal communication method during communication by taking into account the user's geographical location information. For example, if the user is in Japan, the communication unit can prioritize the use of Japanese communication infrastructure. Furthermore, if the user is in the United States, the communication unit can also prioritize the use of American communication infrastructure. Furthermore, if the user is in Europe, the communication unit can also prioritize the use of European communication infrastructure. In this way, the optimal communication method can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the user's geographical location information into the generation AI and cause the generation AI to provide the optimal communication method.

[0069] The communication unit can analyze the user's social media activity during communication and suggest relevant communication methods. For example, the communication unit can prioritize and suggest communication methods that the user frequently uses on social media. The communication unit can also suggest communication methods related to topics in which the user is interested on social media. The communication unit can also suggest specific communication methods based on the user's social media activity. In this way, relevant communication methods can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the communication unit can be performed using, for example, AI, or can be performed without using AI. For example, the communication unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest relevant communication methods. === Hard Collateral 1-1 === Each of the multiple elements including the above-described customization unit, cloud providing unit, and communication unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the smart device 14, and the user customizes the avatar, prompt, and response language according to the industry or usage scenario. The cloud providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides customized information on the cloud. The communication unit is realized by the specific processing unit 290 of the data processing device 12, and performs secure communication. === Hard Collateral 1-2 === Each of the multiple elements including the customization unit, cloud providing unit, and communication unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the smart glasses 214, and the user customizes the avatar, prompt, and response language according to the industry or usage scenario. The cloud providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides customized information on the cloud. The communication unit is realized by the specific processing unit 290 of the data processing device 12, and performs secure communication. === Hard Collateral 1-3 === Each of the multiple elements including the above-described customization unit, cloud providing unit, and communication unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the headset type terminal 314, and the user customizes the avatar, prompt, and response language according to the industry or usage scenario. Furthermore, the cloud providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides customized information on the cloud. The communication unit is realized by the specific processing unit 290 of the data processing device 12, and performs secure communication. === Hard Collateral 1-4 === Each of the multiple elements described above, including the customization unit, cloud provision unit, and communication unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the customization unit is implemented by the control unit 46A of the robot 414, allowing the user to customize the avatar, prompts, and response language according to the industry and usage scenario. The cloud provision unit is implemented by the specific processing unit 290 of the data processing unit 12, providing customized information on the cloud. The communication unit is implemented by the specific processing unit 290 of the data processing unit 12, performing secure communication.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The customization section can analyze the user's voice data and adjust the avatar's responses based on the tone and speed of the voice. For example, if the user is speaking quickly, the avatar's response speed can be increased. Conversely, if the user is speaking slowly, the avatar's response speed can be slowed down. Furthermore, if the user's voice sounds tense, the avatar's facial expression can be made calmer, creating a more relaxed atmosphere. By adjusting the avatar's responses based on the user's voice data, a more natural conversation becomes possible.

[0072] The customization section can analyze a user's past conversation history and automatically suggest avatar styles and prompts that the user prefers. For example, it can suggest similar styles based on avatar styles the user has previously selected. It can also suggest relevant prompts based on prompts the user has used in the past. Furthermore, it can suggest options suitable for specific usage scenarios based on the user's past conversation history. This improves the efficiency of customization by suggesting optimal options based on the user's past conversation history.

[0073] The customization function can estimate the user's emotions and adjust the avatar's voice tone and pitch based on those estimates. For example, if the user is sad, the avatar's voice can be made gentler and adjusted to a comforting tone. If the user is happy, the avatar's voice can be made brighter and adjusted to a lively tone. Furthermore, if the user is angry, the avatar's voice can be made calmer and adjusted to a calm tone. By adjusting the avatar's voice tone and pitch according to the user's emotions, more empathetic dialogue becomes possible.

[0074] The customization feature can provide prompts about region-specific events and holidays based on the user's geographical location. For example, if the user is in Japan, it can provide prompts about Japanese holidays and local events. If the user is in the United States, it can provide prompts about American holidays and local events. Furthermore, if the user is in France, it can provide prompts about French holidays and local events. This allows for the provision of region-specific prompts by taking the user's geographical location into consideration.

[0075] The customization section can estimate the user's emotions and adjust the customization suggestions based on those emotions. For example, if the user is stressed, it can suggest simple and intuitive customization options. If the user is relaxed, it can suggest more detailed customization options. Furthermore, if the user is excited, it can suggest customization options that include fun elements. By adjusting the customization suggestions according to the user's emotions, it becomes possible to provide optimal customization tailored to the user's situation.

[0076] The cloud service provider can propose an optimal database backup schedule based on the user's past usage history. For example, it can propose a schedule that prioritizes backing up data that the user has frequently accessed in the past. It can also propose a schedule that efficiently backs up data that is accessed at specific times of the day. Furthermore, it can propose the optimal backup method based on the user's past usage history. In this way, by analyzing the user's past usage history, it is possible to propose the optimal database backup schedule.

[0077] The cloud provider can estimate the user's emotions and adjust the allocation of cloud resources based on the estimated user emotions. For example, if the user is in a hurry, cloud resources can be allocated preferentially to provide high-speed processing. If the user is relaxed, normal resource allocation can be performed. Furthermore, if the user is feeling stressed, resources can be allocated efficiently to provide a quick response. In this way, optimal services can be provided by adjusting the allocation of cloud resources according to the user's emotions.

[0078] The cloud provider can select the optimal data center based on the user's geographical location information. For example, if the user is in Asia, the Asian data center can be used preferentially. If the user is in Europe, the European data center can be used preferentially. Furthermore, if the user is in North America, the North American data center can be used preferentially. In this way, the optimal data center can be selected by taking the user's geographical location information into consideration.

[0079] The communication unit can propose the optimal communication protocol based on the user's past communication history. For example, it can preferentially propose a communication protocol that the user has frequently used in the past. It can also propose the optimal communication protocol for a specific time period. Furthermore, it can propose the optimal communication method based on the user's past communication history. In this way, it is possible to propose the optimal communication protocol by analyzing the user's past communication history.

[0080] The communication unit can estimate the user's emotions and adjust the priority of communications based on the estimated user's emotions. For example, if the user is in a hurry, important communications can be prioritized. If the user is relaxed, communications can be performed with normal priority. Furthermore, if the user is feeling stressed, important communications can be performed quickly. In this way, by adjusting the priority of communications according to the user's emotions, important communications can be performed quickly.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The customization section allows users to customize the avatar, prompts, and response language according to their industry and usage scenario. For example, a user at a medical institution can select a doctor or nurse avatar and include medical terminology in the prompts. The response language can also be set to support multiple languages, making it possible to accommodate foreign patients. Step 2: The cloud provider provides the information customized by the customization unit on the cloud. For example, it manages a learning database that can be customized for each user, and a database for a specific hospital can include the medical records and patient information of that hospital. It also provides a learning database common to all users, allowing them to use general medical knowledge and the latest medical information. Step 3: The communications department securely transmits the information provided by the cloud provider department. For example, it provides a secure communication line to ensure that patient personal information and medical records are not leaked to external parties.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

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

[0122] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0127] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0137] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0138] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0139] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0140] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0141] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0142] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0143] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0144] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0145] 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.

[0146] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0147] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0148] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0149] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0150] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0151] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0154] [Explanation of symbols]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A customization section where users can customize avatars, prompts, and response languages ​​according to their industry or usage scenario, A cloud provisioning unit provides the customized information on the cloud, The system includes a communication unit that securely transmits information provided by the aforementioned cloud provider unit. A system characterized by:

2. The aforementioned customization unit is It estimates the user's emotions and adjusts the avatar's facial expressions and movements based on those estimated emotions. The system of claim 1 .

3. The aforementioned customization unit is We analyze the user's past customization history and suggest suitable customization options. The system of claim 1 .

4. The aforementioned customization unit is During customization, the app automatically suggests appropriate prompts and response language based on the user's industry and usage scenario. The system of claim 1 .

5. The aforementioned customization unit is Estimate user emotions and prioritize customization based on the estimated user emotions The system of claim 1 .

6. The customization unit During customization, provide region-specific avatars and prompts based on the user's geographic location The system of claim 1 .

7. The aforementioned customization unit is During customization, the system analyzes the user's social media activity and suggests relevant customization options. The system of claim 1 .

8. The cloud providing unit It estimates the user's emotions and adjusts the update frequency of the learning database based on the estimated user emotions. The system of claim 1 .

Images