system

Abstract

The system according to the embodiment aims to provide the latest trend information based on the user's interests and generate useful chatter even during remote work. [Solution] A system according to an embodiment includes an acquisition unit, a collection unit, a generation unit, and a provision unit. The acquisition unit identifies a user's interests. The collection unit collects the latest trend information based on the interests identified by the acquisition unit. The generation unit generates topics based on the information collected by the collection unit. The provision unit provides the topics generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the lack of casual conversation during remote work can hinder the discovery of new knowledge and ideas.

[0005] The system according to the embodiment aims to provide the latest trend information based on the user's interests and generate useful chatter even during remote work. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a collection unit, a generation unit, and a provision unit. The acquisition unit grasps the interests of a user. The collection unit collects the latest trend information based on the interests grasped by the acquisition unit. The generation unit generates topics based on the information collected by the collection unit. The provision unit provides the topics generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment provides the latest trend information based on the user's interests and can generate useful conversations even during remote work. [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 generation AI system according to an embodiment of the present invention is a voice-based conversational AI system that allows users to converse with a character (including voice quality) they define whenever and however they like. This system avoids making statements that are offensive or stressful to the user. For example, when the user asks, "Have you heard anything interesting recently?", the system provides topics that take recent trends into consideration. This allows users to gain new insights and ideas that can be applied to their work, even during casual conversations and chats, and to acquire new knowledge. For example, when a user talks to the chat generation AI, the AI ​​inputs a question such as, "Tell me about recent trends." The AI ​​then analyzes the input question and generates appropriate topics. The AI ​​provides topics based on the latest trends, news, and the user's interests. For example, the AI ​​may provide a topic such as, "A recent trend is that new tools for improving the efficiency of remote work are gaining attention." The generated topics are then provided to the user via audio. Through conversations with the AI, users can gain new knowledge and come up with ideas that can be applied to their work. For example, they can come up with ideas for new projects based on trend information provided by the AI. This system is expected to inspire ideas and approaches that can be applied to work, and to increase new knowledge, even during remote work, through casual conversations and chats. Users can enjoy conversations with the AI ​​while avoiding remarks that may hurt or cause stress. As a result, the AI ​​chat generation system collects the latest trend information based on the user's interests, generates and provides topics, and allows users to acquire ideas and new knowledge that can be applied to work, even during remote work.

[0029] A chat generation AI system according to an embodiment includes an acquisition unit, a collection unit, a generation unit, and a provision unit. The acquisition unit grasps a user's interests. The user's interests include, but are not limited to, hobbies, interests, and work-related interests. The acquisition unit can grasp the user's interests by, for example, using past conversation history and user setting information. The collection unit collects the latest trend information based on the interests grasped by the acquisition unit. The collection unit can collect the latest trend information by, for example, analyzing public data on the Internet. The collection unit collects information from news sites, blogs, social media posts, and the like. The generation unit generates topics based on the information collected by the collection unit. The generation unit generates topics using, for example, a generation AI. Examples of the generation AI include, but are not limited to, models such as GPT-4 (registered trademark) and Gemini. The generation unit can automatically generate topics based on the user's interests using the generation AI. The provision unit provides the topics generated by the generation unit. The provision unit provides, for example, the topics generated by the generation AI in audio form. The providing unit can provide topics by voice using text-to-speech technology or speech synthesis technology. As a result, the chat generation AI system according to the embodiment can collect the latest trend information based on the user's interests, generate and provide topics, and thereby acquire ideas and new knowledge that can be used in work even during remote work.

[0030] The acquisition unit can use past conversation history or user setting information. The acquisition unit, for example, uses past conversation history to understand the user's interests. Past conversation history includes, but is not limited to, chat logs, email history, and voice recordings. The acquisition unit can also use, for example, user setting information to understand the user's interests. User setting information includes, but is not limited to, profile settings, notification settings, and interest settings. In this way, by using the past conversation history and the user setting information, the user's interests can be more accurately understood.

[0031] The collection unit can analyze public data on the Internet. For example, the collection unit analyzes public data on the Internet to collect the latest trend information. Public data on the Internet includes, but is not limited to, news sites, blogs, and social media posts. For example, the collection unit analyzes news sites to collect the latest trend information. The collection unit can also analyze blogs to collect the latest trend information. Furthermore, the collection unit can analyze social media posts to collect the latest trend information. In this way, the latest trend information can be efficiently collected by analyzing public data on the Internet.

[0032] The generation unit can generate topics using a generation AI. The generation unit generates topics using, for example, a generation AI. Examples of generation AI include, but are not limited to, models such as GPT-4 and Gemini. The generation unit can automatically generate topics based on user interests using the generation AI. For example, the generation unit generates topics using GPT-4. The generation unit can also generate topics using Gemini. Furthermore, the generation unit can generate topics using a Transformer model. This makes it possible to automatically generate topics based on user interests using the generation AI.

[0033] The provision unit can provide the topic generated by the generation AI by voice. For example, the provision unit can provide the topic generated by the generation AI by voice. The provision unit can provide the topic by voice using text-to-speech technology or voice synthesis technology. For example, the provision unit can provide the topic by voice using text-to-speech technology. The provision unit can also provide the topic by voice using voice synthesis technology. In this way, by providing the topic generated by the generation AI by voice, users can enjoy casual conversations and chats even while working remotely.

[0034] The provision unit can provide new project ideas based on the information provided by the generation AI. For example, the provision unit provides new project ideas based on the information provided by the generation AI. The provision unit can obtain new ideas that users can use in their work based on the information provided by the generation AI. For example, the provision unit can provide new project ideas based on trend information provided by the generation AI. The provision unit can also provide new business plans based on news information provided by the generation AI. Furthermore, the provision unit can provide new marketing strategies based on social media trend information provided by the generation AI. In this way, by providing new project ideas based on the information provided by the generation AI, users can obtain new ideas that they can use in their work.

[0035] The acquisition unit can analyze the user's past conversation history and select an appropriate interest acquisition method. The acquisition unit, for example, analyzes the user's past conversation history and selects the optimal interest acquisition method. The acquisition unit can prioritize acquisition of topics that the user has frequently discussed in the past. The acquisition unit can also select a method for acquiring interests during a specific time period from the user's past conversation history. Furthermore, the acquisition unit can acquire related new interests based on topics in which the user has shown interest in the past. In this way, by analyzing the user's past conversation history, the optimal interest acquisition method can be selected and the user's interests can be acquired efficiently.

[0036] The acquisition unit can perform filtering based on the user's current project or field of interest when acquiring the interests. For example, the acquisition unit can perform filtering based on the user's current project or field of interest when acquiring the interests. The acquisition unit can preferentially acquire interests related to a project the user is currently working on. The acquisition unit can also filter and acquire highly relevant interests based on the user's field of interest. Furthermore, the acquisition unit can acquire interests related to the user's current task and provide information useful for work. As a result, by filtering based on the user's current project or field of interest, information useful for work can be efficiently acquired.

[0037] The acquisition unit can prioritize acquiring highly relevant interests by taking into account the user's geographical location information when acquiring interests. For example, the acquisition unit prioritizes acquiring highly relevant interests by taking into account the user's geographical location information when acquiring interests. When the user is in a specific area, the acquisition unit can prioritize acquiring interests related to that area. Furthermore, when the user is traveling, the acquisition unit can acquire interests related to the travel destination. Furthermore, when the user is at home, the acquisition unit can acquire interests related to events and news around the home. In this way, highly relevant interests can be efficiently acquired by taking into account the user's geographical location information.

[0038] The acquisition unit can analyze the user's social media activity and acquire related interests when acquiring the interests. For example, the acquisition unit can analyze the user's social media activity and acquire related interests when acquiring the interests. The acquisition unit can acquire topics that the user frequently mentions on social media. The acquisition unit can also acquire topics that the user's social media followers are interested in. Furthermore, the acquisition unit can acquire interests related to groups and communities that the user participates in on social media. This makes it possible to efficiently acquire related interests by analyzing the user's social media activity.

[0039] The collection unit can select an appropriate information source by referring to the user's past interest history when collecting trend information. For example, the collection unit selects the optimal information source by referring to the user's past interest history when collecting trend information. The collection unit can preferentially select information sources that the user has frequently referenced in the past. The collection unit can also select a highly reliable information source from the user's past interest history. Furthermore, the collection unit can select an information source related to a topic in which the user has shown interest in the past. This makes it possible to select the optimal information source and efficiently collect trend information by referring to the user's past interest history.

[0040] The collection unit can customize the information based on the user's current areas of interest when collecting trend information. For example, the collection unit customizes the information based on the user's current areas of interest when collecting trend information. The collection unit can preferentially collect trend information related to areas in which the user is currently interested. The collection unit can also customize and collect trend information related to the user's current project. Furthermore, the collection unit can collect trend information related to the user's current task and provide information useful for work. In this way, by customizing the information based on the user's current areas of interest, trend information useful for work can be efficiently collected.

[0041] When collecting trend information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting trend information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. When the user is in a specific area, the collection unit can prioritize collecting trend information related to that area. Furthermore, when the user is traveling, the collection unit can collect trend information related to the user's travel destination. Furthermore, when the user is at home, the collection unit can collect trend information related to events and news around the user's home. In this way, by taking into account the user's geographical location information, highly relevant trend information can be efficiently collected.

[0042] The collection unit can analyze the user's social media activities and collect related information when collecting trend information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting trend information. The collection unit can collect trend information related to topics that the user frequently mentions on social media. The collection unit can also collect trend information related to topics in which the user's social media followers are interested. Furthermore, the collection unit can collect trend information related to groups and communities in which the user participates on social media. This makes it possible to efficiently collect related trend information by analyzing the user's social media activities.

[0043] The generation unit can adjust the level of detail of a topic based on the importance of collected trend information when generating a topic. For example, the generation unit can adjust the level of detail of a topic based on the importance of collected trend information when generating a topic. The generation unit can generate a detailed topic based on trend information with high importance. The generation unit can also generate a concise topic based on trend information with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of a topic according to the importance. As a result, by adjusting the level of detail of a topic based on the importance of collected trend information, it is possible to efficiently provide information that is important to a user.

[0044] The generation unit can apply different generation algorithms depending on the user's interest category when generating topics. For example, the generation unit can apply different generation algorithms depending on the user's interest category when generating topics. If the user is interested in technology, the generation unit can apply an algorithm that generates technology-related topics. If the user is interested in entertainment, the generation unit can apply an algorithm that generates entertainment-related topics. If the user is interested in business, the generation unit can apply an algorithm that generates business-related topics. In this way, by applying different generation algorithms depending on the user's interest category, topics that match the user's interests can be efficiently generated.

[0045] The generation unit can determine the priority of topics based on the submission time of collected trend information when generating topics. For example, the generation unit can determine the priority of topics based on the submission time of collected trend information when generating topics. The generation unit can generate topics with priority based on the latest trend information. The generation unit can also set a lower priority based on older trend information. Furthermore, the generation unit can dynamically adjust the priority of topics according to the submission time. This allows the latest information to be provided efficiently by determining the priority of topics based on the submission time of collected trend information.

[0046] The generation unit can adjust the order of topics based on the relevance of the collected trend information when generating topics. For example, the generation unit adjusts the order of topics based on the relevance of the collected trend information when generating topics. The generation unit can generate topics preferentially based on highly relevant trend information. The generation unit can also postpone the order of topics based on less relevant trend information. Furthermore, the generation unit can dynamically adjust the order of topics according to the relevance. As a result, by adjusting the order of topics based on the relevance of the collected trend information, it is possible to efficiently provide information that is highly relevant to the user.

[0047] The providing unit can select an appropriate delivery method by referring to the user's past conversation history when providing a topic. For example, the providing unit can select the optimal delivery method by referring to the user's past conversation history when providing a topic. The providing unit can preferentially select a delivery method that the user has preferred in the past. The providing unit can also select the optimal delivery method from the user's past conversation history. Furthermore, the providing unit can select a delivery method based on topics in which the user has shown interest in the past. In this way, by referring to the user's past conversation history, the optimal delivery method can be selected and topics can be provided efficiently.

[0048] The providing unit can customize the content to be provided based on the user's current project or area of ​​interest when providing a topic. For example, the providing unit customizes the content to be provided based on the user's current project or area of ​​interest when providing a topic. The providing unit can provide a topic related to a project the user is currently working on. The providing unit can also provide highly relevant topics based on the user's area of ​​interest. Furthermore, the providing unit can provide topics related to the user's current task and provide information useful for work. In this way, by customizing the content to be provided based on the user's current project or area of ​​interest, it is possible to efficiently provide information useful for work.

[0049] The provision unit can select an appropriate provision method by taking into consideration the user's geographical location information when providing a topic. For example, the provision unit selects the optimal provision method by taking into consideration the user's geographical location information when providing a topic. When the user is in a specific area, the provision unit can preferentially provide topics related to that area. Furthermore, when the user is traveling, the provision unit can provide topics related to the user's travel destination. Furthermore, when the user is at home, the provision unit can provide topics related to events and news around the user's home. In this way, by taking into consideration the user's geographical location information, the optimal provision method can be selected and topics can be provided efficiently.

[0050] The providing unit can analyze the user's social media activity and provide related topics when providing topics. For example, the providing unit can analyze the user's social media activity and provide related topics when providing topics. The providing unit can provide topics related to topics that the user frequently mentions on social media. The providing unit can also provide topics related to topics that the user's social media followers are interested in. Furthermore, the providing unit can provide topics related to groups or communities in which the user participates on social media. In this way, related topics can be efficiently provided by analyzing the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The chat generation AI system can further include a schedule acquisition unit that acquires the user's schedule information. The schedule acquisition unit acquires schedule information from, for example, the user's calendar app or task management app. This allows the system to provide appropriate topics based on the user's schedule. For example, if the user is about to attend a meeting, the system can provide topics related to the meeting. Also, if the user is on a break, the system can provide topics that will help the user relax. Furthermore, if the user is about to meet a project deadline, the system can provide topics related to the project.

[0053] The chat generation AI system can further include a purchase history acquisition unit that acquires the user's past purchase history. The purchase history acquisition unit acquires the purchase history from, for example, the user's online shopping site or electronic money usage history. This makes it possible to provide appropriate topics based on the user's purchase history. For example, it can provide topics related to products the user recently purchased. It can also provide topics related to products the user frequently purchases. It can also provide topics related to new products that the user may be interested in.

[0054] The chat generation AI system can further include a health information acquisition unit that acquires the user's health information. The health information acquisition unit acquires health information from, for example, the user's fitness app or smartwatch. This allows the system to provide appropriate topics based on the user's health condition. For example, if the user has recently started exercising, the system can provide topics related to exercise. If the user has had a health checkup, the system can provide health-related topics. If the user is on a diet, the system can provide topics related to dieting.

[0055] The chat generation AI system can further provide a news feed customized based on the user's hobbies and interests. For example, if the user is interested in sports, the latest sports news can be provided. If the user is interested in technology, the latest technology news can be provided. If the user is interested in entertainment, the latest entertainment news can be provided. In this way, by providing a news feed customized based on the user's hobbies and interests, information that catches the user's interest can be efficiently provided.

[0056] The chat generation AI system can further include a travel history acquisition unit that acquires the user's past travel history. The travel history acquisition unit acquires the user's travel history, for example, from the user's travel booking site or airline account. This makes it possible to provide appropriate topics based on the user's travel history. For example, it can provide topics related to places the user has recently visited. It can also provide topics related to places the user frequently visits. It can also provide topics related to travel destinations that the user may be interested in.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The acquisition unit identifies the user's interests. The user's interests include hobbies, interests, and work-related interests. The acquisition unit can identify the user's interests by using past conversation history and user setting information. Step 2: The collection unit collects the latest trend information based on the interests identified by the acquisition unit. The collection unit can collect the latest trend information by analyzing public data on the Internet. The collection unit collects information from news sites, blogs, social media posts, etc. Step 3: The generator generates topics based on the information collected by the collector. The generator generates topics using a generative AI. Generative AI includes models such as GPT-4 and the Gemini model. The generator can use the generative AI to automatically generate topics based on the user's interests. Step 4: The providing unit provides the topics generated by the generating unit. The providing unit provides the topics generated by the generating AI by voice. The providing unit can provide the topics by voice using text-to-speech technology or voice synthesis technology.

[0059] (Example 2) The chat generation AI system according to an embodiment of the present invention is a voice-based conversational AI system that allows users to converse with a character (including voice quality) they define whenever and however they like. This system avoids making statements that are offensive or stressful to the user. For example, when the user asks, "Have you heard anything interesting recently?", the system provides topics that take recent trends into consideration. This allows users to gain new insights and ideas that can be applied to their work, even during casual conversations and chats, and to acquire new knowledge. For example, when a user talks to the chat generation AI, the AI ​​inputs a question such as, "Tell me about recent trends." The AI ​​then analyzes the input question and generates appropriate topics. The AI ​​provides topics based on the latest trends, news, and the user's interests. For example, the AI ​​may provide a topic such as, "A recent trend is that new tools for improving the efficiency of remote work are gaining attention." The generated topics are then provided to the user via audio. Through conversations with the AI, users can gain new knowledge and come up with ideas that can be applied to their work. For example, they can come up with ideas for new projects based on trend information provided by the AI. This system is expected to inspire ideas and approaches that can be applied to work, and to increase new knowledge, even during remote work, through casual conversations and chats. Users can enjoy conversations with the AI ​​while avoiding remarks that may hurt or cause stress. As a result, the AI ​​chat generation system collects the latest trend information based on the user's interests, generates and provides topics, and allows users to acquire ideas and new knowledge that can be applied to work, even during remote work.

[0060] A chat generation AI system according to an embodiment includes an acquisition unit, a collection unit, a generation unit, and a provision unit. The acquisition unit grasps a user's interests. The user's interests include, but are not limited to, hobbies, interests, and work-related interests. The acquisition unit can grasp the user's interests by, for example, using past conversation history and user setting information. The collection unit collects the latest trend information based on the interests grasped by the acquisition unit. The collection unit can collect the latest trend information by, for example, analyzing public data on the Internet. The collection unit collects information from news sites, blogs, social media posts, and the like. The generation unit generates topics based on the information collected by the collection unit. The generation unit generates topics using, for example, a generation AI. Examples of the generation AI include, but are not limited to, i-models such as GPT-4 and gemin. The generation unit can automatically generate topics based on the user's interests using the generation AI. The provision unit provides the topics generated by the generation unit. The provision unit provides, for example, the topics generated by the generation AI in audio form. The providing unit can provide topics by voice using text-to-speech technology or speech synthesis technology. As a result, the chat generation AI system according to the embodiment can collect the latest trend information based on the user's interests, generate and provide topics, and thereby acquire ideas and new knowledge that can be used in work even during remote work.

[0061] The acquisition unit can use past conversation history or user setting information. The acquisition unit, for example, uses past conversation history to understand the user's interests. Past conversation history includes, but is not limited to, chat logs, email history, and voice recordings. The acquisition unit can also use, for example, user setting information to understand the user's interests. User setting information includes, but is not limited to, profile settings, notification settings, and interest settings. In this way, by using the past conversation history and the user setting information, the user's interests can be more accurately understood.

[0062] The collection unit can analyze public data on the Internet. For example, the collection unit analyzes public data on the Internet to collect the latest trend information. Public data on the Internet includes, but is not limited to, news sites, blogs, and social media posts. For example, the collection unit analyzes news sites to collect the latest trend information. The collection unit can also analyze blogs to collect the latest trend information. Furthermore, the collection unit can analyze social media posts to collect the latest trend information. In this way, the latest trend information can be efficiently collected by analyzing public data on the Internet.

[0063] The generation unit can generate topics using a generation AI. The generation unit generates topics using, for example, a generation AI. Examples of generation AI include, but are not limited to, models such as GPT-4 and Gemini. The generation unit can automatically generate topics based on user interests using the generation AI. For example, the generation unit generates topics using GPT-4. The generation unit can also generate topics using Gemini. Furthermore, the generation unit can generate topics using a Transformer model. This makes it possible to automatically generate topics based on user interests using the generation AI.

[0064] The provision unit can provide the topic generated by the generation AI by voice. For example, the provision unit can provide the topic generated by the generation AI by voice. The provision unit can provide the topic by voice using text-to-speech technology or voice synthesis technology. For example, the provision unit can provide the topic by voice using text-to-speech technology. The provision unit can also provide the topic by voice using voice synthesis technology. In this way, by providing the topic generated by the generation AI by voice, users can enjoy casual conversations and chats even while working remotely.

[0065] The provision unit can provide new project ideas based on the information provided by the generation AI. For example, the provision unit provides new project ideas based on the information provided by the generation AI. The provision unit can obtain new ideas that users can use in their work based on the information provided by the generation AI. For example, the provision unit can provide new project ideas based on trend information provided by the generation AI. The provision unit can also provide new business plans based on news information provided by the generation AI. Furthermore, the provision unit can provide new marketing strategies based on social media trend information provided by the generation AI. In this way, by providing new project ideas based on the information provided by the generation AI, users can obtain new ideas that they can use in their work.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of interest acquisition based on the estimated user emotions. For example, the acquisition unit can estimate the user's emotions and adjust the timing of interest acquisition based on the estimated user emotions. The acquisition unit can periodically set the timing of interest acquisition when the user is relaxed. Furthermore, the acquisition unit can reduce the timing of interest acquisition when the user is feeling stressed, thereby reducing the burden on the user. Furthermore, the acquisition unit can delay the timing of interest acquisition when the user is concentrating, so as not to disrupt the user's concentration. In this way, by adjusting the timing of interest acquisition according to the user's emotions, the burden on the user can be reduced and interest can be acquired efficiently. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The acquisition unit can analyze the user's past conversation history and select an appropriate interest acquisition method. The acquisition unit, for example, analyzes the user's past conversation history and selects the optimal interest acquisition method. The acquisition unit can prioritize acquisition of topics that the user has frequently discussed in the past. The acquisition unit can also select a method for acquiring interests during a specific time period from the user's past conversation history. Furthermore, the acquisition unit can acquire related new interests based on topics in which the user has shown interest in the past. In this way, by analyzing the user's past conversation history, the optimal interest acquisition method can be selected and the user's interests can be acquired efficiently.

[0068] The acquisition unit can perform filtering based on the user's current project or field of interest when acquiring the interests. For example, the acquisition unit can perform filtering based on the user's current project or field of interest when acquiring the interests. The acquisition unit can preferentially acquire interests related to a project the user is currently working on. The acquisition unit can also filter and acquire highly relevant interests based on the user's field of interest. Furthermore, the acquisition unit can acquire interests related to the user's current task and provide information useful for work. As a result, by filtering based on the user's current project or field of interest, information useful for work can be efficiently acquired.

[0069] The acquisition unit can estimate the user's emotions and determine the priority of the interests to be acquired based on the estimated user emotions. For example, the acquisition unit can estimate the user's emotions and determine the priority of the interests to be acquired based on the estimated user emotions. When the user is relaxed, the acquisition unit can set the priority of the interests high and acquire a large amount of information. When the user is stressed, the acquisition unit can set the priority of the interests low and acquire the minimum amount of information necessary. Furthermore, when the user is concentrating, the acquisition unit can adjust the priority of the interests and acquire only important information. This reduces the burden on the user by determining the priority of the interests according to the user's emotions, and efficiently acquires the interests. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The acquisition unit can prioritize acquiring highly relevant interests by taking into account the user's geographical location information when acquiring interests. For example, the acquisition unit prioritizes acquiring highly relevant interests by taking into account the user's geographical location information when acquiring interests. When the user is in a specific area, the acquisition unit can prioritize acquiring interests related to that area. Furthermore, when the user is traveling, the acquisition unit can acquire interests related to the travel destination. Furthermore, when the user is at home, the acquisition unit can acquire interests related to events and news around the home. In this way, highly relevant interests can be efficiently acquired by taking into account the user's geographical location information.

[0071] The acquisition unit can analyze the user's social media activity and acquire related interests when acquiring the interests. For example, the acquisition unit can analyze the user's social media activity and acquire related interests when acquiring the interests. The acquisition unit can acquire topics that the user frequently mentions on social media. The acquisition unit can also acquire topics that the user's social media followers are interested in. Furthermore, the acquisition unit can acquire interests related to groups and communities that the user participates in on social media. This makes it possible to efficiently acquire related interests by analyzing the user's social media activity.

[0072] The collection unit can estimate the user's emotions and adjust the trend information collection method based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the trend information collection method based on the estimated user emotions. The collection unit can collect a wide range of trend information when the user is relaxed. Furthermore, the collection unit can collect the minimum amount of trend information necessary when the user is stressed. Furthermore, the collection unit can prioritize collecting work-related trend information when the user is concentrating. This reduces the burden on the user by adjusting the trend information collection method according to the user's emotions, and enables trend information to be collected efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit can select an appropriate information source by referring to the user's past interest history when collecting trend information. For example, the collection unit selects the optimal information source by referring to the user's past interest history when collecting trend information. The collection unit can preferentially select information sources that the user has frequently referenced in the past. The collection unit can also select a highly reliable information source from the user's past interest history. Furthermore, the collection unit can select an information source related to a topic in which the user has shown interest in the past. This makes it possible to select the optimal information source and efficiently collect trend information by referring to the user's past interest history.

[0074] The collection unit can customize the information based on the user's current areas of interest when collecting trend information. For example, the collection unit customizes the information based on the user's current areas of interest when collecting trend information. The collection unit can preferentially collect trend information related to areas in which the user is currently interested. The collection unit can also customize and collect trend information related to the user's current project. Furthermore, the collection unit can collect trend information related to the user's current task and provide information useful for work. In this way, by customizing the information based on the user's current areas of interest, trend information useful for work can be efficiently collected.

[0075] The collection unit can estimate the user's emotions and determine the priority of trend information to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of trend information to be collected based on the estimated user emotions. The collection unit can prioritize collecting a wide range of trend information when the user is relaxed. Furthermore, the collection unit can prioritize collecting the minimum amount of trend information necessary when the user is stressed. Furthermore, the collection unit can prioritize collecting work-related trend information when the user is concentrating. This reduces the burden on the user by prioritizing trend information according to the user's emotions and enables trend information to be collected efficiently. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When collecting trend information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting trend information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. When the user is in a specific area, the collection unit can prioritize collecting trend information related to that area. Furthermore, when the user is traveling, the collection unit can collect trend information related to the user's travel destination. Furthermore, when the user is at home, the collection unit can collect trend information related to events and news around the user's home. In this way, by taking into account the user's geographical location information, highly relevant trend information can be efficiently collected.

[0077] The collection unit can analyze the user's social media activities and collect related information when collecting trend information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting trend information. The collection unit can collect trend information related to topics that the user frequently mentions on social media. The collection unit can also collect trend information related to topics in which the user's social media followers are interested. Furthermore, the collection unit can collect trend information related to groups and communities in which the user participates on social media. This makes it possible to efficiently collect related trend information by analyzing the user's social media activities.

[0078] The generation unit can estimate the user's emotion and adjust the topic generation method based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the topic generation method based on the estimated user emotion. When the user is relaxed, the generation unit can generate a topic with a relaxed atmosphere. Furthermore, when the user is stressed, the generation unit can generate a topic that reduces stress. Furthermore, when the user is concentrating, the generation unit can generate a topic related to work. In this way, by adjusting the topic generation method according to the user's emotion, the burden on the user can be reduced and topics can be generated efficiently. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The generation unit can adjust the level of detail of a topic based on the importance of collected trend information when generating a topic. For example, the generation unit can adjust the level of detail of a topic based on the importance of collected trend information when generating a topic. The generation unit can generate a detailed topic based on trend information with high importance. The generation unit can also generate a concise topic based on trend information with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of a topic according to the importance. As a result, by adjusting the level of detail of a topic based on the importance of collected trend information, it is possible to efficiently provide information that is important to a user.

[0080] The generation unit can apply different generation algorithms depending on the user's interest category when generating topics. For example, the generation unit can apply different generation algorithms depending on the user's interest category when generating topics. If the user is interested in technology, the generation unit can apply an algorithm that generates technology-related topics. If the user is interested in entertainment, the generation unit can apply an algorithm that generates entertainment-related topics. If the user is interested in business, the generation unit can apply an algorithm that generates business-related topics. In this way, by applying different generation algorithms depending on the user's interest category, topics that match the user's interests can be efficiently generated.

[0081] The generation unit can estimate the user's emotion and adjust the length of the topic based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the topic based on the estimated user emotion. The generation unit can generate a longer topic when the user is relaxed. Furthermore, the generation unit can generate a shorter topic when the user is stressed. Furthermore, the generation unit can generate a short topic that focuses on the main points when the user is concentrating. This reduces the burden on the user by adjusting the length of the topic according to the user's emotion, and enables topics to be provided efficiently. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The generation unit can determine the priority of topics based on the submission time of collected trend information when generating topics. For example, the generation unit can determine the priority of topics based on the submission time of collected trend information when generating topics. The generation unit can generate topics with priority based on the latest trend information. The generation unit can also set a lower priority based on older trend information. Furthermore, the generation unit can dynamically adjust the priority of topics according to the submission time. This allows the latest information to be provided efficiently by determining the priority of topics based on the submission time of collected trend information.

[0083] The generation unit can adjust the order of topics based on the relevance of the collected trend information when generating topics. For example, the generation unit adjusts the order of topics based on the relevance of the collected trend information when generating topics. The generation unit can generate topics preferentially based on highly relevant trend information. The generation unit can also postpone the order of topics based on less relevant trend information. Furthermore, the generation unit can dynamically adjust the order of topics according to the relevance. As a result, by adjusting the order of topics based on the relevance of the collected trend information, it is possible to efficiently provide information that is highly relevant to the user.

[0084] The providing unit can estimate the user's emotions and adjust the topic provision method based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the topic provision method based on the estimated user emotions. When the user is relaxed, the providing unit can provide topics in a relaxed atmosphere. Furthermore, when the user is stressed, the providing unit can provide topics in a way that reduces stress. Furthermore, when the user is concentrating, the providing unit can provide work-related topics. In this way, by adjusting the topic provision method according to the user's emotions, the burden on the user can be reduced and topics can be provided efficiently. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The providing unit can select an appropriate delivery method by referring to the user's past conversation history when providing a topic. For example, the providing unit can select the optimal delivery method by referring to the user's past conversation history when providing a topic. The providing unit can preferentially select a delivery method that the user has preferred in the past. The providing unit can also select the optimal delivery method from the user's past conversation history. Furthermore, the providing unit can select a delivery method based on topics in which the user has shown interest in the past. In this way, by referring to the user's past conversation history, the optimal delivery method can be selected and topics can be provided efficiently.

[0086] The providing unit can customize the content to be provided based on the user's current project or area of ​​interest when providing a topic. For example, the providing unit customizes the content to be provided based on the user's current project or area of ​​interest when providing a topic. The providing unit can provide a topic related to a project the user is currently working on. The providing unit can also provide highly relevant topics based on the user's area of ​​interest. Furthermore, the providing unit can provide topics related to the user's current task and provide information useful for work. In this way, by customizing the content to be provided based on the user's current project or area of ​​interest, it is possible to efficiently provide information useful for work.

[0087] The providing unit can estimate the user's emotions and adjust the order in which topics are provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the order in which topics are provided based on the estimated user emotions. When the user is relaxed, the providing unit can prioritize providing topics with a relaxing atmosphere. Furthermore, when the user is stressed, the providing unit can prioritize providing topics that will reduce stress. Furthermore, when the user is concentrating, the providing unit can prioritize providing topics related to work. In this way, by adjusting the order in which topics are provided based on the user's emotions, the burden on the user can be reduced and topics can be provided efficiently. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The provision unit can select an appropriate provision method by taking into consideration the user's geographical location information when providing a topic. For example, the provision unit selects the optimal provision method by taking into consideration the user's geographical location information when providing a topic. When the user is in a specific area, the provision unit can preferentially provide topics related to that area. Furthermore, when the user is traveling, the provision unit can provide topics related to the user's travel destination. Furthermore, when the user is at home, the provision unit can provide topics related to events and news around the user's home. In this way, by taking into consideration the user's geographical location information, the optimal provision method can be selected and topics can be provided efficiently.

[0089] The providing unit can analyze the user's social media activity and provide related topics when providing topics. For example, the providing unit can analyze the user's social media activity and provide related topics when providing topics. The providing unit can provide topics related to topics that the user frequently mentions on social media. The providing unit can also provide topics related to topics that the user's social media followers are interested in. Furthermore, the providing unit can provide topics related to groups or communities in which the user participates on social media. In this way, related topics can be efficiently provided by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14 and grasps the user's interests using past conversation history and user setting information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes public data on the Internet to collect the latest trend information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates topics using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated topics by voice. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 and grasps the user's interests using past conversation history and user setting information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes public data on the Internet to collect the latest trend information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates topics using a generation AI. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated topics by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset-type terminal 314 and grasps the user's interests using past conversation history and user setting information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes public data on the Internet to collect the latest trend information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates topics using a generation AI. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated topics by voice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 and grasps the user's interests using past conversation history and user setting information. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes public data on the Internet to collect the latest trend information. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates topics using a generation AI. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated topics by voice.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The chat generation AI system can further include a biometric information acquisition unit that acquires the user's biometric information. The biometric information acquisition unit acquires biometric information such as the user's heart rate, electrodermal activity, and body temperature. This allows the system to grasp the user's stress level and relaxation level in real time and adjust the topics to be generated and the way they are presented. For example, if the user's heart rate is high, relaxing topics can be provided. Also, if the user's electrodermal activity is high, topics that reduce stress can be provided. Furthermore, if the body temperature is rising, it can be determined that the user is concentrating and work-related topics can be provided.

[0092] The chat generation AI system can further include a schedule acquisition unit that acquires the user's schedule information. The schedule acquisition unit acquires schedule information from, for example, the user's calendar app or task management app. This allows the system to provide appropriate topics based on the user's schedule. For example, if the user is about to attend a meeting, the system can provide topics related to the meeting. Also, if the user is on a break, the system can provide topics that will help the user relax. Furthermore, if the user is about to meet a project deadline, the system can provide topics related to the project.

[0093] The chat generation AI system can further include a voice analysis unit that analyzes the user's voice tone. The voice analysis unit, for example, analyzes the tone and pitch of the user's voice to estimate the user's emotional state. This makes it possible to provide appropriate topics based on the user's emotional state. For example, if the user's voice sounds calm, it can provide relaxing topics. If the user's voice sounds tense, it can provide stress-relieving topics. If the user's voice sounds excited, it can provide interesting topics.

[0094] The chat generation AI system can further include a purchase history acquisition unit that acquires the user's past purchase history. The purchase history acquisition unit acquires the purchase history from, for example, the user's online shopping site or electronic money usage history. This makes it possible to provide appropriate topics based on the user's purchase history. For example, it can provide topics related to products the user recently purchased. It can also provide topics related to products the user frequently purchases. It can also provide topics related to new products that the user may be interested in.

[0095] The chat generation AI system can further include a health information acquisition unit that acquires the user's health information. The health information acquisition unit acquires health information from, for example, the user's fitness app or smartwatch. This allows the system to provide appropriate topics based on the user's health condition. For example, if the user has recently started exercising, the system can provide topics related to exercise. If the user has had a health checkup, the system can provide health-related topics. If the user is on a diet, the system can provide topics related to dieting.

[0096] The chat generation AI system can also estimate the user's emotions and adjust the difficulty of the topic based on the estimated user emotions. For example, if the user is relaxed, it can provide more difficult topics. If the user is stressed, it can provide less difficult topics. If the user is concentrating, it can provide more difficult work-related topics. By adjusting the difficulty of topics according to the user's emotions, it is possible to reduce the user's burden and provide topics efficiently.

[0097] The chat generation AI system can further provide a news feed customized based on the user's hobbies and interests. For example, if the user is interested in sports, the latest sports news can be provided. If the user is interested in technology, the latest technology news can be provided. If the user is interested in entertainment, the latest entertainment news can be provided. In this way, by providing a news feed customized based on the user's hobbies and interests, information that catches the user's interest can be efficiently provided.

[0098] The chat generation AI system can also estimate the user's emotions and adjust the speed at which topics are presented based on the estimated user emotions. For example, if the user is relaxed, topics can be presented at a slower speed. If the user is stressed, topics can be presented at a faster speed. Furthermore, if the user is concentrating, topics can be presented at an appropriate speed. In this way, adjusting the speed at which topics are presented according to the user's emotions reduces the burden on the user and enables topics to be presented efficiently.

[0099] The chat generation AI system can further include a travel history acquisition unit that acquires the user's past travel history. The travel history acquisition unit acquires the user's travel history, for example, from the user's travel booking site or airline account. This makes it possible to provide appropriate topics based on the user's travel history. For example, it can provide topics related to places the user has recently visited. It can also provide topics related to places the user frequently visits. It can also provide topics related to travel destinations that the user may be interested in.

[0100] The chat generation AI system can further estimate the user's emotions and adjust the visual presentation of topics based on the estimated user emotions. For example, if the user is relaxed, it can provide visually relaxing images and videos. If the user is stressed, it can provide images and videos that visually reduce stress. Furthermore, if the user is concentrating, it can provide images and videos that visually help the user concentrate. In this way, by adjusting the visual presentation of topics according to the user's emotions, it is possible to reduce the burden on the user and provide topics efficiently.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The acquisition unit identifies the user's interests. The user's interests include hobbies, interests, and work-related interests. The acquisition unit can identify the user's interests by using past conversation history and user setting information. Step 2: The collection unit collects the latest trend information based on the interests identified by the acquisition unit. The collection unit can collect the latest trend information by analyzing public data on the Internet. The collection unit collects information from news sites, blogs, social media posts, etc. Step 3: The generator generates topics based on the information collected by the collector. The generator generates topics using a generative AI. Generative AI includes models such as GPT-4 and the Gemini model. The generator can use the generative AI to automatically generate topics based on the user's interests. Step 4: The providing unit provides the topics generated by the generating unit. The providing unit provides the topics generated by the generating AI by voice. The providing unit can provide the topics by voice using text-to-speech technology or voice synthesis technology.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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, in order to avoid confusion and to 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. an acquisition unit for grasping a user's interests; a collection unit that collects the latest trend information based on the interests identified by the acquisition unit; a generation unit that generates a topic based on the information collected by the collection unit; a providing unit that provides the topic generated by the generating unit. A system characterized by:

2. The acquisition unit Use past conversation history or user settings information 2. The system of claim 1.

3. The collecting unit Analyzing public data on the Internet 2. The system of claim 1.

4. The generation unit Generate topics using generative AI 2. The system of claim 1.

5. The providing unit Provide audio topics generated by generative AI 2. The system of claim 1.

6. The providing unit Providing new project ideas based on information provided by generative AI 2. The system of claim 1.

7. The acquisition unit Estimates user emotions and adjusts timing of interest acquisition based on the estimated user emotions 2. The system of claim 1.

8. The acquisition unit Analyze the user's past conversation history and select the appropriate method to acquire their interest 2. The system of claim 1.

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