System

Abstract

A system in accordance with an embodiment aims for the user to receive optimal guidance at any time 24 hours.SOLUTION: A system includes a chat reception unit, an analysis unit, and a proposal unit. The chat reception unit receives a question or a request from a user. The analysis unit analyzes the question or the request received by the chat reception unit. The suggestion unit provides an optimal guide on the basis of the content analyzed by the analysis unit and on the basis of the user's preference and the current situation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not allow users to receive optimal guidance 24 hours a day, and there is room for improvement.

[0005] The system according to the embodiment aims to enable users to receive the most suitable guide 24 hours a day. [Means for solving the problem]

[0006] The system according to the embodiment includes a chat reception unit, an analysis unit, and a suggestion unit. The chat reception unit receives questions and requests from users. The analysis unit analyzes the questions and requests received by the chat reception unit. The suggestion unit provides optimal guidance based on the user's preferences and current situation, based on the content analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to receive the most suitable guide 24 hours a day. [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 chat-style AI service according to the embodiment of the present invention is a system in which a generating AI makes individually customized suggestions to help users maximize their enjoyment of their travels. This allows the chat-style AI service to provide optimal guidance based on the user's questions and requests.

[0029] A chat-style AI service according to an embodiment includes a chat reception unit, an analysis unit, and a suggestion unit. The chat reception unit accepts questions and requests from a user. For example, if a user asks, "What restaurants near where I am now would you recommend?", the chat reception unit accepts the question. Similarly, if a user requests, "What is a good tourist spot to visit next?", the chat reception unit accepts the request. The analysis unit analyzes the questions and requests accepted by the chat reception unit. For example, the analysis unit uses natural language processing technology to understand the intent of the user's questions and requests. The analysis unit can also identify the user's preferences and current situation using a machine learning algorithm. Furthermore, the analysis unit can analyze the user's past behavior history and real-time location information. The suggestion unit provides an optimal guide based on the user's preferences and current situation, based on the content analyzed by the analysis unit. For example, the suggestion unit can suggest restaurants serving the user's favorite cuisine. The suggestion unit can also suggest historical landmarks that the user might be interested in. Furthermore, the suggestion unit can provide real-time information based on the user's current location and time zone. As a result, the chat-style AI service according to the embodiment can provide optimal guidance based on the user's questions and requests. For example, even if a user's plans suddenly change during a trip, the generation AI can make new suggestions in real time, allowing the user to enjoy the trip to the fullest. In addition, customized suggestions tailored to the user's preferences improve the user's satisfaction with the trip.

[0030] The analysis unit analyzes the user's past chat history and learns frequently used phrases and keywords, thereby improving the accuracy of responses. The analysis unit, for example, analyzes the user's past chat history and extracts frequently used phrases and keywords. For example, it learns keywords such as "recommended restaurants" and "tourist spots." The analysis unit also estimates the user's preferences and interests based on the learned phrases and keywords, and customizes the response content based on that. For example, it prioritizes providing information about areas the user frequently visits. The analysis unit also makes personalized suggestions based on the user's past chat history. For example, it suggests new spots related to places the user has visited in the past. This makes it possible to improve the accuracy of responses based on the user's past chat history.

[0031] The system can add a guide function using a voice assistant or AR in addition to the chat format. For example, the system provides a guide function using a voice assistant in addition to the chat format. For example, when a user asks a question by voice, the system responds by voice. The system also uses AR technology to display information about the surroundings in real time through the user's smartphone camera. For example, it displays information about tourist attractions and restaurants at the location where the camera is pointed. The system also combines a voice assistant and AR to allow the user to ask a question by voice and provide visual information using AR. For example, information about the location where the question was asked by voice is displayed using AR. This allows guides to be provided in ways other than chat format.

[0032] The system can realize seamless chat integration between different devices, allowing the same conversation to be continued on a smartphone, tablet, smartwatch, etc. For example, the system provides a function that allows a user to seamlessly continue a chat started on a smartphone on a tablet or smartwatch. For example, even if a chat is interrupted on a smartphone, it can be resumed on a tablet. The system also synchronizes chat history between different devices, allowing a user to continue the same conversation on any device. For example, the chat history on a smartwatch can be checked on a smartphone. The system also provides a cloud-based chat history storage function to realize seamless integration between devices. For example, chat history stored in the cloud can be accessed from any device. This allows a chat to be continued seamlessly across different devices.

[0033] The suggestion unit can analyze the user's social media account and make suggestions that reflect the user's interests and concerns in real time. The suggestion unit, for example, analyzes the user's social media account and makes suggestions that reflect the user's interests and concerns in real time. For example, the suggestion unit makes suggestions based on posts that the user has recently "liked." The suggestion unit also analyzes the content of social media posts to understand the user's current interests and concerns. For example, the suggestion unit makes suggestions based on photos and comments that the user has recently posted. The suggestion unit also makes personalized suggestions based on data obtained from the user's social media account. For example, the suggestion unit makes suggestions based on information about accounts the user follows. This makes it possible to make suggestions that reflect the user's interests and concerns in real time based on the user's social media account.

[0034] The suggestion unit can suggest activities that match the user's physical condition, taking into account the user's health condition and fitness data. The suggestion unit, for example, analyzes the user's fitness data and suggests activities that match the user's physical condition. For example, it suggests activities with an appropriate amount of exercise based on the user's heart rate and step count data. The suggestion unit also considers the user's health condition and suggests places to eat and rest that match the user's physical condition. For example, it suggests cafes and restaurants where the user can relax based on the user's sleep data. The suggestion unit also suggests tourist spots and activities that match the user's physical condition, based on the user's fitness data. For example, it suggests light walks or hikes depending on the user's exercise volume. In this way, it is possible to suggest activities that match the user's physical condition, based on the user's health condition and fitness data.

[0035] The suggestion unit can make suggestions for group fun, taking into account the preferences of the user's family and friends. The suggestion unit, for example, makes suggestions for group fun, taking into account the preferences of the user's family and friends. For example, it suggests tourist spots and activities that the whole family can enjoy. The suggestion unit also suggests restaurants and cafes that can be enjoyed by groups, based on the preferences of the user's friends. For example, it suggests restaurants that serve dishes that the friends like. The suggestion unit also analyzes the past behavior history of the user's family and friends, and makes suggestions for group fun based on that. For example, it suggests new spots related to places that have been visited in the past. This makes it possible to make suggestions for group fun based on the preferences of the user's family and friends.

[0036] The suggestion unit can suggest similar unvisited spots based on data of the user's past travel destinations. For example, the suggestion unit analyzes data of the user's past travel destinations to suggest similar unvisited spots. For example, it suggests new cities similar to cities the user has visited in the past. The suggestion unit also suggests unvisited tourist spots and activities based on data of the user's past travel destinations. For example, it suggests new museums similar to museums the user has visited in the past. The suggestion unit also suggests unvisited restaurants and cafes based on data of the user's past travel destinations. For example, it suggests new restaurants similar to restaurants the user has visited in the past. In this way, similar unvisited spots can be suggested based on data of the user's past travel destinations.

[0037] The suggestion unit can combine the user's real-time location information and weather information to suggest optimal activities. For example, the suggestion unit acquires the user's current location and weather information in real time and suggests optimal activities based on the information. For example, outdoor tourist spots are suggested when the weather is fine. The suggestion unit also combines the user's location information and weather information to suggest restaurants and cafes that suit the weather. For example, indoor places that can be enjoyed when it rains. The suggestion unit also suggests events and activities that suit the season and time of day based on the user's real-time location information and weather information. For example, spots where you can see the sunset are suggested in the evening. In this way, optimal activities can be suggested based on the user's real-time location information and weather information.

[0038] The suggestion unit can provide information about the history and culture of a place the user plans to visit, thereby promoting a deeper understanding. The suggestion unit, for example, provides information about the history and culture of a place the user plans to visit. For example, it explains the historical background and cultural significance of the place. The suggestion unit also provides information about historical events and people related to the place the user plans to visit. For example, it introduces stories about historical people associated with the place. The suggestion unit also provides information about cultural events and festivals at the place the user plans to visit. For example, it introduces details of traditional festivals and events held at the place. This allows the user to be provided with information about the history and culture of the place they plan to visit, thereby promoting a deeper understanding.

[0039] The suggestion unit can add a community function that allows users to exchange information with other travelers in real time. The suggestion unit, for example, provides a chat function that allows users to exchange information with other travelers in real time. For example, travelers visiting the same place can share information with each other. The suggestion unit also provides a community function that allows users to share photos and videos with other travelers. For example, photos taken during a trip can be shared and comments from other travelers can be received. The suggestion unit also provides a function that allows users to plan activities with other travelers in real time. For example, travelers visiting the same place can make plans to go sightseeing together. This allows a community function to be added that allows users to exchange information with other travelers in real time.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The analysis unit analyzes the user's past chat history and learns frequently used phrases and keywords, thereby improving the accuracy of responses. The analysis unit, for example, analyzes the user's past chat history and extracts frequently used phrases and keywords. For example, it learns keywords such as "recommended restaurants" and "tourist spots." The analysis unit also estimates the user's preferences and interests based on the learned phrases and keywords, and customizes the response content based on that. For example, it prioritizes providing information about areas the user frequently visits. The analysis unit also makes personalized suggestions based on the user's past chat history. For example, it suggests new spots related to places the user has visited in the past. This makes it possible to improve the accuracy of responses based on the user's past chat history.

[0042] The system can add a guide function using a voice assistant or AR in addition to the chat format. For example, the system provides a guide function using a voice assistant in addition to the chat format. For example, when a user asks a question by voice, the system responds by voice. The system also uses AR technology to display information about the surroundings in real time through the user's smartphone camera. For example, it displays information about tourist attractions and restaurants at the location where the camera is pointed. The system also combines a voice assistant and AR to allow the user to ask a question by voice and provide visual information using AR. For example, information about the location where the question was asked by voice is displayed using AR. This allows guides to be provided in ways other than chat format.

[0043] The system can realize seamless chat integration between different devices, allowing the same conversation to be continued on a smartphone, tablet, smartwatch, etc. For example, the system provides a function that allows a user to seamlessly continue a chat started on a smartphone on a tablet or smartwatch. For example, even if a chat is interrupted on a smartphone, it can be resumed on a tablet. The system also synchronizes chat history between different devices, allowing a user to continue the same conversation on any device. For example, the chat history on a smartwatch can be checked on a smartphone. The system also provides a cloud-based chat history storage function to realize seamless integration between devices. For example, chat history stored in the cloud can be accessed from any device. This allows a chat to be continued seamlessly across different devices.

[0044] The suggestion unit can analyze the user's social media account and make suggestions that reflect the user's interests and concerns in real time. The suggestion unit, for example, analyzes the user's social media account and makes suggestions that reflect the user's interests and concerns in real time. For example, the suggestion unit makes suggestions based on posts that the user has recently "liked." The suggestion unit also analyzes the content of social media posts to understand the user's current interests and concerns. For example, the suggestion unit makes suggestions based on photos and comments that the user has recently posted. The suggestion unit also makes personalized suggestions based on data obtained from the user's social media account. For example, the suggestion unit makes suggestions based on information about accounts the user follows. This makes it possible to make suggestions that reflect the user's interests and concerns in real time based on the user's social media account.

[0045] The suggestion unit can suggest activities that match the user's physical condition, taking into account the user's health condition and fitness data. The suggestion unit, for example, analyzes the user's fitness data and suggests activities that match the user's physical condition. For example, it suggests activities with an appropriate amount of exercise based on the user's heart rate and step count data. The suggestion unit also considers the user's health condition and suggests places to eat and rest that match the user's physical condition. For example, it suggests cafes and restaurants where the user can relax based on the user's sleep data. The suggestion unit also suggests tourist spots and activities that match the user's physical condition, based on the user's fitness data. For example, it suggests light walks or hikes depending on the user's exercise volume. In this way, it is possible to suggest activities that match the user's physical condition, based on the user's health condition and fitness data.

[0046] The suggestion unit can make suggestions for group fun, taking into account the preferences of the user's family and friends. The suggestion unit, for example, makes suggestions for group fun, taking into account the preferences of the user's family and friends. For example, it suggests tourist spots and activities that the whole family can enjoy. The suggestion unit also suggests restaurants and cafes that can be enjoyed by groups, based on the preferences of the user's friends. For example, it suggests restaurants that serve dishes that the friends like. The suggestion unit also analyzes the past behavior history of the user's family and friends, and makes suggestions for group fun based on that. For example, it suggests new spots related to places that have been visited in the past. This makes it possible to make suggestions for group fun based on the preferences of the user's family and friends.

[0047] The suggestion unit can suggest similar unvisited spots based on data of the user's past travel destinations. For example, the suggestion unit analyzes data of the user's past travel destinations to suggest similar unvisited spots. For example, it suggests new cities similar to cities the user has visited in the past. The suggestion unit also suggests unvisited tourist spots and activities based on data of the user's past travel destinations. For example, it suggests new museums similar to museums the user has visited in the past. The suggestion unit also suggests unvisited restaurants and cafes based on data of the user's past travel destinations. For example, it suggests new restaurants similar to restaurants the user has visited in the past. In this way, similar unvisited spots can be suggested based on data of the user's past travel destinations.

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

[0049] Step 1: The chat reception unit accepts questions and requests from users. For example, when a user asks, "What restaurants do you recommend near where I am now?" or requests, "Which tourist spot would be good next?", the chat reception unit accepts these. Step 2: The analysis unit analyzes the questions and requests received by the chat reception unit. For example, it uses natural language processing technology to understand the intent of the user's questions and requests, and uses machine learning algorithms to identify the user's preferences and current situation. It can also analyze the user's past behavioral history and real-time location information. Step 3: The suggestion unit uses the information analyzed by the analysis unit to provide optimal guidance based on the user's preferences and current situation. For example, it suggests restaurants serving the user's favorite cuisine or historical sites that may interest the user, and provides real-time information based on the user's current location and time of day.

[0050] (Example 2) The chat-style AI service according to the embodiment of the present invention is a system in which a generating AI makes individually customized suggestions to help users maximize their enjoyment of their travels. This allows the chat-style AI service to provide optimal guidance based on the user's questions and requests.

[0051] A chat-style AI service according to an embodiment includes a chat reception unit, an analysis unit, and a suggestion unit. The chat reception unit accepts questions and requests from a user. For example, if a user asks, "What restaurants near where I am now would you recommend?", the chat reception unit accepts the question. Similarly, if a user requests, "What is a good tourist spot to visit next?", the chat reception unit accepts the request. The analysis unit analyzes the questions and requests accepted by the chat reception unit. For example, the analysis unit uses natural language processing technology to understand the intent of the user's questions and requests. The analysis unit can also identify the user's preferences and current situation using a machine learning algorithm. Furthermore, the analysis unit can analyze the user's past behavior history and real-time location information. The suggestion unit provides an optimal guide based on the user's preferences and current situation, based on the content analyzed by the analysis unit. For example, the suggestion unit can suggest restaurants serving the user's favorite cuisine. The suggestion unit can also suggest historical landmarks that the user might be interested in. Furthermore, the suggestion unit can provide real-time information based on the user's current location and time zone. As a result, the chat-style AI service according to the embodiment can provide optimal guidance based on the user's questions and requests. For example, even if a user's plans suddenly change during a trip, the generation AI can make new suggestions in real time, allowing the user to enjoy the trip to the fullest. In addition, customized suggestions tailored to the user's preferences improve the user's satisfaction with the trip.

[0052] The suggestion unit can analyze the tone and speed of the user's voice, estimate the user's emotional state, and adjust the response content accordingly. For example, when the user inputs a question in chat format, the suggestion unit uses voice input and analyzes the tone and speed of the voice. For example, if the user is in a hurry, the suggestion unit provides a short and concise response. Furthermore, if the user's voice tone is calm, the suggestion unit provides detailed information or additional suggestions. For example, if the user's voice tone is relaxed, the suggestion unit suggests tourist spots that can be enjoyed at a leisurely pace. Furthermore, if the user's voice speed is fast, the suggestion unit prioritizes providing information with a high degree of urgency. For example, if the user is in a hurry, the suggestion unit suggests the nearest means of transportation or a fast route. This makes it possible to provide a response content that suits the user's emotional state.

[0053] The analysis unit analyzes the user's past chat history and learns frequently used phrases and keywords, thereby improving the accuracy of responses. The analysis unit, for example, analyzes the user's past chat history and extracts frequently used phrases and keywords. For example, it learns keywords such as "recommended restaurants" and "tourist spots." The analysis unit also estimates the user's preferences and interests based on the learned phrases and keywords, and customizes the response content based on that. For example, it prioritizes providing information about areas the user frequently visits. The analysis unit also makes personalized suggestions based on the user's past chat history. For example, it suggests new spots related to places the user has visited in the past. This makes it possible to improve the accuracy of responses based on the user's past chat history.

[0054] The suggestion unit uses the emotion estimation function to estimate the emotion of a user when starting a chat in real time and can respond in an optimal tone. For example, the suggestion unit uses the emotion estimation function to analyze the emotion in real time when the user starts a chat. For example, the suggestion unit estimates the emotion based on the user's input content and typing speed. The suggestion unit also adjusts the tone of the response based on the estimated emotion. For example, if the user is feeling stressed, the suggestion unit responds in a gentle tone that helps the user relax. The suggestion unit also uses the emotion estimation function to respond in a bright and cheerful tone when the user has positive emotions. For example, if the user is having fun, the suggestion unit makes suggestions to make the conversation even more enjoyable. This makes it possible to respond in an optimal tone according to the user's emotion.

[0055] The system can add a guide function using a voice assistant or AR in addition to the chat format. For example, the system provides a guide function using a voice assistant in addition to the chat format. For example, when a user asks a question by voice, the system responds by voice. The system also uses AR technology to display information about the surroundings in real time through the user's smartphone camera. For example, it displays information about tourist attractions and restaurants at the location where the camera is pointed. The system also combines a voice assistant and AR to allow the user to ask a question by voice and provide visual information using AR. For example, information about the location where the question was asked by voice is displayed using AR. This allows guides to be provided in ways other than chat format.

[0056] The system can realize seamless chat integration between different devices, allowing the same conversation to be continued on a smartphone, tablet, smartwatch, etc. For example, the system provides a function that allows a user to seamlessly continue a chat started on a smartphone on a tablet or smartwatch. For example, even if a chat is interrupted on a smartphone, it can be resumed on a tablet. The system also synchronizes chat history between different devices, allowing a user to continue the same conversation on any device. For example, the chat history on a smartwatch can be checked on a smartphone. The system also provides a cloud-based chat history storage function to realize seamless integration between devices. For example, chat history stored in the cloud can be accessed from any device. This allows a chat to be continued seamlessly across different devices.

[0057] The suggestion unit can analyze the user's social media account and make suggestions that reflect the user's interests and concerns in real time. The suggestion unit, for example, analyzes the user's social media account and makes suggestions that reflect the user's interests and concerns in real time. For example, the suggestion unit makes suggestions based on posts that the user has recently "liked." The suggestion unit also analyzes the content of social media posts to understand the user's current interests and concerns. For example, the suggestion unit makes suggestions based on photos and comments that the user has recently posted. The suggestion unit also makes personalized suggestions based on data obtained from the user's social media account. For example, the suggestion unit makes suggestions based on information about accounts the user follows. This makes it possible to make suggestions that reflect the user's interests and concerns in real time based on the user's social media account.

[0058] The suggestion unit can suggest activities that match the user's physical condition, taking into account the user's health condition and fitness data. The suggestion unit, for example, analyzes the user's fitness data and suggests activities that match the user's physical condition. For example, it suggests activities with an appropriate amount of exercise based on the user's heart rate and step count data. The suggestion unit also considers the user's health condition and suggests places to eat and rest that match the user's physical condition. For example, it suggests cafes and restaurants where the user can relax based on the user's sleep data. The suggestion unit also suggests tourist spots and activities that match the user's physical condition, based on the user's fitness data. For example, it suggests light walks or hikes depending on the user's exercise volume. In this way, it is possible to suggest activities that match the user's physical condition, based on the user's health condition and fitness data.

[0059] The suggestion unit can use the emotion estimation function to make optimal suggestions when the user is in a specific emotional state. For example, the suggestion unit uses the emotion estimation function to make optimal suggestions when the user is in a specific emotional state. For example, if the user is feeling stressed, the suggestion unit suggests places where the user can relax. The suggestion unit also analyzes the user's emotional state in real time and customizes the suggestion content based on the results. For example, if the user is excited, the suggestion unit suggests active activities. The suggestion unit also uses the emotion estimation function to make even more enjoyable suggestions when the user is feeling positive. For example, if the user is having fun, the suggestion unit suggests additional tourist spots. This makes it possible to make optimal suggestions according to the user's emotional state.

[0060] The suggestion unit can make suggestions for group fun, taking into account the preferences of the user's family and friends. The suggestion unit, for example, makes suggestions for group fun, taking into account the preferences of the user's family and friends. For example, it suggests tourist spots and activities that the whole family can enjoy. The suggestion unit also suggests restaurants and cafes that can be enjoyed by groups, based on the preferences of the user's friends. For example, it suggests restaurants that serve dishes that the friends like. The suggestion unit also analyzes the past behavior history of the user's family and friends, and makes suggestions for group fun based on that. For example, it suggests new spots related to places that have been visited in the past. This makes it possible to make suggestions for group fun based on the preferences of the user's family and friends.

[0061] The suggestion unit can suggest similar unvisited spots based on data of the user's past travel destinations. For example, the suggestion unit analyzes data of the user's past travel destinations to suggest similar unvisited spots. For example, it suggests new cities similar to cities the user has visited in the past. The suggestion unit also suggests unvisited tourist spots and activities based on data of the user's past travel destinations. For example, it suggests new museums similar to museums the user has visited in the past. The suggestion unit also suggests unvisited restaurants and cafes based on data of the user's past travel destinations. For example, it suggests new restaurants similar to restaurants the user has visited in the past. In this way, similar unvisited spots can be suggested based on data of the user's past travel destinations.

[0062] The suggestion unit can use the emotion estimation function to repeat a suggestion that was popular in the past when the user is in a specific emotional state. For example, the suggestion unit can use the emotion estimation function to repeat a suggestion that was popular in the past when the user is in a specific emotional state. For example, if the user is feeling stressed, the suggestion unit can suggest places where the user was able to relax in the past. The suggestion unit can also analyze the user's emotional state in real time and repeat a suggestion that was popular in the past based on the results of the analysis. For example, if the user is excited, the suggestion unit can suggest an activity that the user enjoyed in the past. The suggestion unit can also use the emotion estimation function to repeat a suggestion that was popular in the past when the user is feeling positive. For example, if the user is having fun, the suggestion unit can resuggest a tourist spot that the user visited in the past. In this way, a suggestion that was popular in the past can be repeated according to the user's emotional state.

[0063] The suggestion unit can combine the user's real-time location information and weather information to suggest optimal activities. For example, the suggestion unit acquires the user's current location and weather information in real time and suggests optimal activities based on the information. For example, outdoor tourist spots are suggested when the weather is fine. The suggestion unit also combines the user's location information and weather information to suggest restaurants and cafes that suit the weather. For example, indoor places that can be enjoyed when it rains. The suggestion unit also suggests events and activities that suit the season and time of day based on the user's real-time location information and weather information. For example, spots where you can see the sunset are suggested in the evening. In this way, optimal activities can be suggested based on the user's real-time location information and weather information.

[0064] The suggestion unit can use the emotion estimation function to suggest ways to have fun that match the user's emotions when the user is in a specific emotional state. For example, the suggestion unit uses the emotion estimation function to suggest ways to have fun that match the user's emotions when the user is in a specific emotional state. For example, if the user feels like relaxing, the suggestion unit suggests a quiet place. The suggestion unit also analyzes the user's emotional state in real time and customizes ways to have fun based on the results. For example, if the user is excited, the suggestion unit suggests an active activity. The suggestion unit also uses the emotion estimation function to make suggestions for further enjoyment when the user is feeling positive. For example, if the user is having fun, the suggestion unit suggests additional tourist spots. In this way, ways to have fun can be suggested according to the user's emotional state.

[0065] The suggestion unit can provide information about the history and culture of a place the user plans to visit, thereby promoting a deeper understanding. The suggestion unit, for example, provides information about the history and culture of a place the user plans to visit. For example, it explains the historical background and cultural significance of the place. The suggestion unit also provides information about historical events and people related to the place the user plans to visit. For example, it introduces stories about historical people associated with the place. The suggestion unit also provides information about cultural events and festivals at the place the user plans to visit. For example, it introduces details of traditional festivals and events held at the place. This allows the user to be provided with information about the history and culture of the place they plan to visit, thereby promoting a deeper understanding.

[0066] The suggestion unit can add a community function that allows users to exchange information with other travelers in real time. The suggestion unit, for example, provides a chat function that allows users to exchange information with other travelers in real time. For example, travelers visiting the same place can share information with each other. The suggestion unit also provides a community function that allows users to share photos and videos with other travelers. For example, photos taken during a trip can be shared and comments from other travelers can be received. The suggestion unit also provides a function that allows users to plan activities with other travelers in real time. For example, travelers visiting the same place can make plans to go sightseeing together. This allows a community function to be added that allows users to exchange information with other travelers in real time.

[0067] The suggestion unit can use the emotion estimation function to make suggestions that have received positive emotional responses from other travelers when the user is in a specific emotional state. For example, the suggestion unit can use the emotion estimation function to make suggestions that have received positive emotional responses from other travelers when the user is in a specific emotional state. For example, if the user feels like relaxing, the suggestion unit can suggest places where other travelers found it relaxing. The suggestion unit can also analyze the user's emotional state in real time and make suggestions that have received positive emotional responses from other travelers based on the results. For example, if the user is excited, the suggestion unit can suggest activities that other travelers enjoyed. The suggestion unit can also use the emotion estimation function to make suggestions that have received positive emotional responses from other travelers when the user is feeling positive. For example, if the user is having fun, the suggestion unit can suggest tourist spots that other travelers liked. This makes it possible to make suggestions that have received positive emotional responses from other travelers according to the user's emotional state.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The suggestion unit can analyze the tone and speed of the user's voice, estimate the user's emotional state, and adjust the response content accordingly. For example, when the user inputs a question in chat format, the suggestion unit uses voice input and analyzes the tone and speed of the voice. For example, if the user is in a hurry, the suggestion unit provides a short and concise response. Furthermore, if the user's voice tone is calm, the suggestion unit provides detailed information or additional suggestions. For example, if the user's voice tone is relaxed, the suggestion unit suggests tourist spots that can be enjoyed at a leisurely pace. Furthermore, if the user's voice speed is fast, the suggestion unit prioritizes providing information with a high degree of urgency. For example, if the user is in a hurry, the suggestion unit suggests the nearest means of transportation or a fast route. This makes it possible to provide a response content that suits the user's emotional state.

[0070] The analysis unit analyzes the user's past chat history and learns frequently used phrases and keywords, thereby improving the accuracy of responses. The analysis unit, for example, analyzes the user's past chat history and extracts frequently used phrases and keywords. For example, it learns keywords such as "recommended restaurants" and "tourist spots." The analysis unit also estimates the user's preferences and interests based on the learned phrases and keywords, and customizes the response content based on that. For example, it prioritizes providing information about areas the user frequently visits. The analysis unit also makes personalized suggestions based on the user's past chat history. For example, it suggests new spots related to places the user has visited in the past. This makes it possible to improve the accuracy of responses based on the user's past chat history.

[0071] The suggestion unit uses the emotion estimation function to estimate the emotion of a user when starting a chat in real time and can respond in an optimal tone. For example, the suggestion unit uses the emotion estimation function to analyze the emotion in real time when the user starts a chat. For example, the suggestion unit estimates the emotion based on the user's input content and typing speed. The suggestion unit also adjusts the tone of the response based on the estimated emotion. For example, if the user is feeling stressed, the suggestion unit responds in a gentle tone that helps the user relax. The suggestion unit also uses the emotion estimation function to respond in a bright and cheerful tone when the user has positive emotions. For example, if the user is having fun, the suggestion unit makes suggestions to make the conversation even more enjoyable. This makes it possible to respond in an optimal tone according to the user's emotion.

[0072] The system can add a guide function using a voice assistant or AR in addition to the chat format. For example, the system provides a guide function using a voice assistant in addition to the chat format. For example, when a user asks a question by voice, the system responds by voice. The system also uses AR technology to display information about the surroundings in real time through the user's smartphone camera. For example, it displays information about tourist attractions and restaurants at the location where the camera is pointed. The system also combines a voice assistant and AR to allow the user to ask a question by voice and provide visual information using AR. For example, information about the location where the question was asked by voice is displayed using AR. This allows guides to be provided in ways other than chat format.

[0073] The system can realize seamless chat integration between different devices, allowing the same conversation to be continued on a smartphone, tablet, smartwatch, etc. For example, the system provides a function that allows a user to seamlessly continue a chat started on a smartphone on a tablet or smartwatch. For example, even if a chat is interrupted on a smartphone, it can be resumed on a tablet. The system also synchronizes chat history between different devices, allowing a user to continue the same conversation on any device. For example, the chat history on a smartwatch can be checked on a smartphone. The system also provides a cloud-based chat history storage function to realize seamless integration between devices. For example, chat history stored in the cloud can be accessed from any device. This allows a chat to be continued seamlessly across different devices.

[0074] The suggestion unit can analyze the user's social media account and make suggestions that reflect the user's interests and concerns in real time. The suggestion unit, for example, analyzes the user's social media account and makes suggestions that reflect the user's interests and concerns in real time. For example, the suggestion unit makes suggestions based on posts that the user has recently "liked." The suggestion unit also analyzes the content of social media posts to understand the user's current interests and concerns. For example, the suggestion unit makes suggestions based on photos and comments that the user has recently posted. The suggestion unit also makes personalized suggestions based on data obtained from the user's social media account. For example, the suggestion unit makes suggestions based on information about accounts the user follows. This makes it possible to make suggestions that reflect the user's interests and concerns in real time based on the user's social media account.

[0075] The suggestion unit can suggest activities that match the user's physical condition, taking into account the user's health condition and fitness data. The suggestion unit, for example, analyzes the user's fitness data and suggests activities that match the user's physical condition. For example, it suggests activities with an appropriate amount of exercise based on the user's heart rate and step count data. The suggestion unit also considers the user's health condition and suggests places to eat and rest that match the user's physical condition. For example, it suggests cafes and restaurants where the user can relax based on the user's sleep data. The suggestion unit also suggests tourist spots and activities that match the user's physical condition, based on the user's fitness data. For example, it suggests light walks or hikes depending on the user's exercise volume. In this way, it is possible to suggest activities that match the user's physical condition, based on the user's health condition and fitness data.

[0076] The suggestion unit can use the emotion estimation function to make optimal suggestions when the user is in a specific emotional state. For example, the suggestion unit uses the emotion estimation function to make optimal suggestions when the user is in a specific emotional state. For example, if the user is feeling stressed, the suggestion unit suggests places where the user can relax. The suggestion unit also analyzes the user's emotional state in real time and customizes the suggestion content based on the results. For example, if the user is excited, the suggestion unit suggests active activities. The suggestion unit also uses the emotion estimation function to make even more enjoyable suggestions when the user is feeling positive. For example, if the user is having fun, the suggestion unit suggests additional tourist spots. This makes it possible to make optimal suggestions according to the user's emotional state.

[0077] The suggestion unit can make suggestions for group fun, taking into account the preferences of the user's family and friends. The suggestion unit, for example, makes suggestions for group fun, taking into account the preferences of the user's family and friends. For example, it suggests tourist spots and activities that the whole family can enjoy. The suggestion unit also suggests restaurants and cafes that can be enjoyed by groups, based on the preferences of the user's friends. For example, it suggests restaurants that serve dishes that the friends like. The suggestion unit also analyzes the past behavior history of the user's family and friends, and makes suggestions for group fun based on that. For example, it suggests new spots related to places that have been visited in the past. This makes it possible to make suggestions for group fun based on the preferences of the user's family and friends.

[0078] The suggestion unit can suggest similar unvisited spots based on data of the user's past travel destinations. For example, the suggestion unit analyzes data of the user's past travel destinations to suggest similar unvisited spots. For example, it suggests new cities similar to cities the user has visited in the past. The suggestion unit also suggests unvisited tourist spots and activities based on data of the user's past travel destinations. For example, it suggests new museums similar to museums the user has visited in the past. The suggestion unit also suggests unvisited restaurants and cafes based on data of the user's past travel destinations. For example, it suggests new restaurants similar to restaurants the user has visited in the past. In this way, similar unvisited spots can be suggested based on data of the user's past travel destinations.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The chat reception unit accepts questions and requests from users. For example, when a user asks, "What restaurants do you recommend near where I am now?" or requests, "Which tourist spot would be good next?", the chat reception unit accepts these. Step 2: The analysis unit analyzes the questions and requests received by the chat reception unit. For example, it uses natural language processing technology to understand the intent of the user's questions and requests, and uses machine learning algorithms to identify the user's preferences and current situation. It can also analyze the user's past behavioral history and real-time location information. Step 3: The suggestion unit uses the information analyzed by the analysis unit to provide optimal guidance based on the user's preferences and current situation. For example, it suggests restaurants serving the user's favorite cuisine or historical sites that may interest the user, and provides real-time information based on the user's current location and time of day.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 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 chat reception section that receives questions and requests from users; an analysis unit that analyzes the question or request accepted by the chat acceptance unit; a suggestion unit that provides an optimal guide based on the user's preferences and current situation, based on the content analyzed by the analysis unit. A system characterized by:

2. The proposal unit Analyzing the tone and speed of the user's voice, inferring their emotional state and adjusting the response accordingly 2. The system of claim 1.

3. The analysis unit Analyze the user's past chat history and learn frequently used phrases and keywords to improve response accuracy 2. The system of claim 1.

4. The proposal unit The emotion of the user when starting a chat is estimated in real time, and a response is made in the most appropriate tone.

2. The system of claim 1.

5. The system comprises: In addition to chat, we will add a guide function using voice assistants and the aforementioned AR.

2. The system of claim 1.

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