system

Abstract

The system according to the embodiment aims to centrally process user questions, task management, information searches, and the like. [Solution] The system according to the embodiment comprises a question receiving unit, an answering unit, a task management unit, a reminder unit, an information retrieval unit, and an information provision unit. The question receiving unit receives questions from users. The answering unit immediately answers questions received by the question receiving unit. The task management unit receives task requests from users. The reminder unit adds tasks received by the task management unit to the schedule and sets reminders. The information retrieval unit receives information retrieval requests from users. The information provision unit provides information based on information retrieval requests received by the information retrieval unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to centrally process user questions, task management, information searches, and other tasks.

[0005] The system according to the embodiment aims to centrally process user questions, task management, information searches, and the like. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, an answering unit, a task management unit, a reminder unit, an information search unit, and an information providing unit. The question receiving unit receives questions from users. The answering unit immediately answers questions received by the question receiving unit. The task management unit receives task requests from users. The reminder unit adds tasks received by the task management unit to a schedule and sets reminders. The information search unit receives information search requests from users. The information providing unit provides information based on the information search requests received by the information search unit. [Effects of the Invention]

[0007] The system according to this embodiment can centrally process user inquiries, task management, information retrieval, and more. [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) A virtual assistant system according to an embodiment of the present invention is a system in which a robot or character acts as a virtual assistant as an AI chatbot. When a user opens an application, this virtual assistant system displays a character and accepts the user's questions and requests. For example, when a user asks, "What's the weather like tomorrow?", the AI ​​chatbot instantly provides weather information. When a user requests, "Please remind me about tomorrow's meeting," the AI ​​chatbot adds the meeting to the schedule and sets a reminder. Furthermore, the system also has an information search function. When a user asks, "What restaurants are nearby?", the AI ​​chatbot provides information about nearby restaurants. In this way, by responding to various user needs, convenience is improved. This service allows users to easily obtain information and manage tasks through the app, thereby improving the efficiency of daily life. For example, when a user opens an application, a character appears and accepts the user's questions and requests. For example, when a user asks, "What's the weather like tomorrow?", the AI ​​chatbot instantly provides weather information. When a user requests, "Please remind me about tomorrow's meeting," the AI ​​chatbot adds the meeting to the schedule and sets a reminder. Furthermore, the system also has an information search function. When a user requests, "What restaurants are nearby?", the AI ​​chatbot provides information about nearby restaurants. In this way, responding to various user needs improves convenience. This service allows users to easily obtain information and manage tasks through the app, thereby improving the efficiency of daily life. As a result, the virtual assistant system can immediately respond to users' questions, task requests, and information search requests, improving convenience.

[0029] The virtual assistant system according to this embodiment comprises a question receiving unit, an answering unit, a task management unit, a reminder unit, an information retrieval unit, and an information provision unit. The question receiving unit receives questions from the user. User questions include, but are not limited to, weather information, schedule management, reminder setting, and information retrieval. The question receiving unit receives questions, for example, through a character displayed when the user opens the application. The question receiving unit can also receive questions through voice input or text input. For example, if the user asks, "What's the weather like tomorrow?", the question receiving unit receives the question. The answering unit immediately answers the question received by the question receiving unit. For example, the answering unit accesses a weather database to obtain the latest weather information in order to provide weather information. The answering unit can also access a calendar application to add schedules and set reminders for schedule management and reminder setting. For example, if the user requests, "Remind me of tomorrow's meeting," the answering unit adds the meeting to the schedule and sets a reminder based on the request. The Task Management Unit accepts task requests from users. For example, if a user asks, "Tell me about nearby restaurants," the Task Management Unit accepts the task. The Reminder Unit adds the task accepted by the Task Management Unit to the schedule and sets a reminder. For example, if a user requests, "Remind me about tomorrow's meeting," the Reminder Unit adds the meeting to the schedule and sets a reminder based on the request. The Information Retrieval Unit accepts information retrieval requests from users. For example, if a user asks, "Tell me about nearby restaurants," the Information Retrieval Unit accepts the information retrieval request. The Information Provision Unit provides information based on the information retrieval requests accepted by the Information Retrieval Unit. For example, to provide information on nearby restaurants, the Information Provision Unit accesses a restaurant database and obtains the latest restaurant information. As a result, the virtual assistant system according to this embodiment can respond immediately to user questions, task requests, and information retrieval requests, improving convenience.

[0030] When accepting a question, the question acceptance unit can analyze the user's past question history and select an appropriate acceptance method. For example, the question acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The question acceptance unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. Furthermore, the question acceptance unit can predict and suggest question content to be used in a specific time period based on the user's past question history. This makes it possible to provide a more appropriate question acceptance method by taking the user's past question history into consideration. Some or all of the above-mentioned processing in the question acceptance unit may be performed using, or without, AI. For example, the question acceptance unit can input the user's past question history data into a generation AI and have the generation AI select the optimal acceptance method.

[0031] The question receiving unit can perform filtering based on the user's current situation or area of ​​interest when receiving a question. For example, the question receiving unit preferentially receives questions related to the user's current location. The question receiving unit can also automatically filter related questions based on the user's area of ​​interest. Furthermore, the question receiving unit can also suggest appropriate questions based on the user's current situation (time of day, weather, etc.). This makes it possible to receive appropriate questions based on the user's current situation and area of ​​interest. Some or all of the above-described processing in the question receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the question receiving unit can input the user's location information data to the generation AI and cause the generation AI to filter related questions.

[0032] When receiving a question, the question receiving unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, when the user is in a specific area, the question receiving unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the question receiving unit can also prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the question receiving unit can also prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the question receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the question receiving unit can input the user's location information data to the generation AI and have the generation AI execute the prioritization of related questions.

[0033] The question reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the question reception unit can prioritize receiving questions related to topics the user is discussing on social media. The question reception unit can also analyze the content of the user's social media posts and suggest related questions. Furthermore, the question reception unit can also receive questions related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, related questions can be appropriately received. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related questions.

[0034] The answering unit can adjust the level of detail in its answers based on the importance of the question. For example, it can provide detailed answers to high-importance questions, and concise answers to low-importance questions. Furthermore, it can provide necessary information appropriately according to the importance of the question. This allows for a response that meets the user's needs by providing detailed answers according to the importance of the question. The importance of a question is evaluated based on criteria such as urgency and impact. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input question importance data into a generating AI and have the generating AI adjust the level of detail in the answers.

[0035] The answering unit can use different answering algorithms depending on the category of the question when providing an answer. For example, the answering unit can apply a specialized answering algorithm to technical questions. It can also apply a concise answering algorithm to general questions. Furthermore, it can apply a customer support-specific answering algorithm to questions related to customer support. This allows for more accurate answers by applying the appropriate answering algorithm according to the category of the question. Question categories are identified by criteria such as technical questions or business-related questions. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input question category data into a generating AI and have the generating AI apply different answering algorithms.

[0036] When answering, the answering unit can set the priority of answers based on the time of submission of the question. The answering unit can determine the priority based on, for example, the time period when the question was submitted. The answering unit can also determine the priority based on the date when the question was submitted. Furthermore, the answering unit can quickly provide answers based on the timing when the question was submitted. This enables a prompt and appropriate response by determining the priority based on the time when the question was submitted. The time when the question was submitted is identified based on, for example, the date and time of submission or the elapsed time since submission. Some or all of the above-mentioned processing in the answering unit can be performed using, for example, AI, or can be performed without using AI. For example, the answering unit can input question submission time data into the generating AI and have the generating AI set the priority of the answers.

[0037] The answering unit can change the order of answers based on the relevance of the question when answering. For example, the answering unit can prioritize providing the most relevant answer based on the relevance of the question. The answering unit can also adjust the order of answers based on the relevance of the question. Furthermore, the answering unit can also provide appropriate answers based on the relevance of the question. This enables responses that meet the needs of users by providing an order of answers based on the relevance of the question. The relevance of a question is evaluated based on criteria such as the similarity of the question content or related topics. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input question relevance data to a generation AI and have the generation AI adjust the order of the answers.

[0038] The task management unit can analyze the user's past task history and select an appropriate task management method during task management. For example, the task management unit can automatically display tasks that the user has frequently requested in the past as candidates. The task management unit can also prioritize and suggest task management methods (such as voice and text) that the user has used in the past. Furthermore, the task management unit can predict and suggest tasks to be used during a specific time period based on the user's past task history. This makes it possible to provide a more appropriate task management method by taking the user's past task history into consideration. Some or all of the above-described processing in the task management unit can be performed using, for example, AI, or without AI. For example, the task management unit can input the user's past task history data into a generation AI and have the generation AI select the optimal task management method.

[0039] The task management unit can perform filtering based on the user's current situation or areas of interest when managing tasks. For example, the task management unit prioritizes managing tasks related to the user's current location. The task management unit can also automatically filter related tasks based on the user's areas of interest. Furthermore, the task management unit can also suggest appropriate tasks based on the user's current situation (time of day, weather, etc.). This makes it possible to manage appropriate tasks based on the user's current situation and areas of interest. Some or all of the above-described processing in the task management unit may be performed using AI, for example, or may be performed without using AI. For example, the task management unit can input the user's location information data into the generation AI and have the generation AI filter related tasks.

[0040] During task management, the task management unit can prioritize highly relevant tasks based on the user's geographical location information. For example, when the user is in a specific area, the task management unit prioritizes tasks related to that area. Furthermore, when the user is traveling, the task management unit can prioritize tasks related to the travel destination. Furthermore, when the user is at home, the task management unit can prioritize tasks related to the area around the user's home. This allows highly relevant tasks to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the task management unit may be performed using, or without, AI. For example, the task management unit can input the user's location information data into a generation AI and have the generation AI prioritize related tasks.

[0041] During task management, the task management unit can analyze the user's social media activities and manage related tasks. For example, the task management unit prioritizes managing tasks related to topics the user is discussing on social media. The task management unit can also analyze the content of the user's social media posts and suggest related tasks. Furthermore, the task management unit can manage tasks related to topics that the user's social media followers and friends are interested in. This allows for appropriate management of related tasks by analyzing the user's social media activities. Some or all of the above-described processing in the task management unit may be performed using, for example, AI, or may be performed without using AI. For example, the task management unit can input the user's social media data into a generation AI and have the generation AI manage related tasks.

[0042] When setting a reminder, the reminder unit can analyze the user's past reminder history and select an appropriate setting method. For example, the reminder unit can automatically display reminders that the user has frequently set in the past as candidates. The reminder unit can also prioritize and suggest reminder setting methods (voice, text, etc.) that the user has used in the past. Furthermore, the reminder unit can predict and suggest reminders to be used in specific time periods based on the user's past reminder history. This makes it possible to provide a more appropriate reminder setting method by taking the user's past reminder history into consideration. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input the user's past reminder history data into a generation AI and have the generation AI select the optimal setting method.

[0043] When setting a reminder, the reminder unit can filter reminders based on the user's current situation or areas of interest. For example, the reminder unit can prioritize setting reminders related to the user's current location. The reminder unit can also automatically filter relevant reminders based on the user's areas of interest. Furthermore, the reminder unit can also suggest appropriate reminders based on the user's current situation (time of day, weather, etc.). This makes it possible to set appropriate reminders based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit can input the user's location information data into the generation AI and have the generation AI filter relevant reminders.

[0044] When setting a reminder, the reminder unit can prioritize highly relevant reminders based on the user's geographical location information. For example, if the user is in a specific area, the reminder unit can prioritize reminders related to that area. Furthermore, if the user is traveling, the reminder unit can prioritize reminders related to the travel destination. Furthermore, if the user is at home, the reminder unit can prioritize reminders related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant reminders can be prioritized. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's location information data into the generation AI and cause the generation AI to prioritize relevant reminders.

[0045] When setting a reminder, the reminder unit can analyze the user's social media activity and set a relevant reminder. For example, the reminder unit can prioritize setting reminders related to topics the user is talking about on social media. The reminder unit can also analyze the content of the user's social media posts and suggest relevant reminders. Furthermore, the reminder unit can set reminders related to topics that the user's social media followers and friends are interested in. This allows relevant reminders to be appropriately set by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input the user's social media data into a generation AI and have the generation AI set relevant reminders.

[0046] The information retrieval unit can analyze the user's past search history and select an appropriate search method when an information retrieval is performed. For example, the information retrieval unit can automatically display as suggestions what the user has frequently searched for in the past. The information retrieval unit can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. Furthermore, the information retrieval unit can predict and suggest search content that the user will use at a specific time of day based on their past search history. This allows for the provision of a more appropriate search method by considering the user's past search history. Some or all of the above processing in the information retrieval unit may be performed using AI, for example, or without AI. For example, the information retrieval unit can input the user's past search history data into a generating AI and have the generating AI select the optimal search method.

[0047] The information retrieval unit can filter information based on the user's current situation or areas of interest during an information retrieval. For example, the information retrieval unit may prioritize searching for information related to the user's current location. The information retrieval unit can also automatically filter relevant information based on the user's areas of interest. Furthermore, the information retrieval unit can suggest appropriate information based on the user's current situation (time of day, weather, etc.). This allows the user to search for appropriate information based on their current situation and areas of interest. Some or all of the above processing in the information retrieval unit may be performed using AI, for example, or without AI. For example, the information retrieval unit can input the user's location data into a generating AI and have the generating AI perform the filtering of relevant information.

[0048] When searching for information, the information search unit can prioritize searching for highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the information search unit can prioritize searching for information related to that area. Furthermore, if the user is traveling, the information search unit can also prioritize searching for information related to the travel destination. Furthermore, if the user is at home, the information search unit can also prioritize searching for information related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant information can be prioritized. Some or all of the above-described processing in the information search unit may be performed using AI, for example, or may be performed without using AI. For example, the information search unit can input the user's location information data into the generation AI and have the generation AI prioritize relevant information.

[0049] When searching for information, the information search unit can analyze the user's social media activity and search for related information. For example, the information search unit can prioritize searching for information related to topics the user is talking about on social media. The information search unit can also analyze the content of the user's social media posts and suggest related information. Furthermore, the information search unit can search for information related to topics that the user's social media followers and friends are interested in. This makes it possible to appropriately search for related information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the information search unit may be performed using, or without, AI. For example, the information search unit can input the user's social media data into the generation AI and cause the generation AI to search for related information.

[0050] The information providing unit can change the level of detail of the information provided based on the importance of the search results when providing the information. For example, the information providing unit provides detailed information for information with high importance. The information providing unit can also provide concise information for information with low importance. Furthermore, the information providing unit can appropriately provide necessary information according to the importance of the search results. This makes it possible to respond to user needs by providing detailed information according to the importance of the search results. The importance of the search results is evaluated based on criteria such as urgency and impact. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input importance data of the search results to the generation AI and cause the generation AI to adjust the level of detail of the information provided.

[0051] When providing information, the information providing unit can use different provision algorithms depending on the category of the search result. For example, the information providing unit can apply a specialized provision algorithm to technical information. The information providing unit can also apply a simple provision algorithm to general information. Furthermore, the information providing unit can apply a provision algorithm specialized for customer support to information related to customer support. This allows more accurate information to be provided by applying an appropriate provision algorithm depending on the category of the search result. The category of the search result is identified based on criteria such as technical information or business information. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input category data of the search result to the generation AI and cause the generation AI to apply different provision algorithms.

[0052] When providing information, the information providing unit can set a priority for providing the information based on the time of submission of the search results. The information providing unit can determine the priority based on, for example, the time period in which the search results were submitted. The information providing unit can also determine the priority based on the date the search results were submitted. Furthermore, the information providing unit can quickly provide information based on the timing of submission of the search results. This enables a prompt and appropriate response by determining the priority based on the time of submission of the search results. The time of submission of the search results is specified based on, for example, the date and time of submission or the elapsed time since submission. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the information providing unit can input data on the time of submission of the search results to the generation AI and cause the generation AI to set the priority for providing the information.

[0053] The information providing unit can change the order of information provision based on the relevance of search results when providing information. For example, the information providing unit can prioritize providing the most relevant information based on the relevance of the search results. The information providing unit can also adjust the order of information provision based on the relevance of the search results. Furthermore, the information providing unit can also provide appropriate information based on the relevance of the search results. This enables responding to user needs by providing an order of information provision based on the relevance of the search results. The relevance of search results is evaluated based on criteria such as the similarity of the search query or related topics. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input relevance data of search results to a generation AI and cause the generation AI to adjust the order of information provision.

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

[0055] The virtual assistant system can further include a health management unit that monitors the user's health status. The health management unit can acquire the user's health data (e.g., heart rate, blood pressure, sleep patterns, etc.) and provide appropriate advice based on this. For example, if the user's heart rate is high, it can suggest breathing techniques to help them relax. Also, if the user's sleep pattern is disrupted, it can provide advice to improve the quality of their sleep. Furthermore, the health management unit can analyze the user's health data and set reminders for regular health checks. This allows the user's health status to be continuously monitored and supports health maintenance.

[0056] The virtual assistant system can further include a purchase recommendation unit that analyzes the user's purchasing history and recommends appropriate products. The purchase recommendation unit recommends related products based on products the user has purchased in the past, for example. It can also analyze the user's purchasing patterns and recommend products tailored to specific seasons or events. Furthermore, it can recommend products based on specific brands or categories from the user's purchasing history. This enables appropriate product recommendations that take the user's purchasing history into consideration.

[0057] The virtual assistant system can further include a learning recommendation unit that analyzes the user's learning history and recommends appropriate learning content. The learning recommendation unit recommends related learning content based on, for example, what the user has learned in the past. It can also analyze the user's learning progress and suggest what content to study next. It can also suggest the optimal learning method based on the user's learning style. This makes it possible to recommend appropriate learning content that takes the user's learning history into consideration.

[0058] The virtual assistant system can also include a travel recommendation unit that analyzes the user's travel history and recommends appropriate destinations. For example, the travel recommendation unit can recommend relevant destinations based on places the user has visited in the past. It can also analyze the user's travel patterns and recommend destinations suited to specific seasons or events. Furthermore, it can recommend destinations based on specific interests and preferences derived from the user's travel history. This enables the recommendation of appropriate destinations that take the user's travel history into consideration.

[0059] The virtual assistant system can also include an event recommendation unit that analyzes the user's hobbies and interests and recommends appropriate events and activities. For example, the event recommendation unit can recommend relevant events based on events the user has previously attended. It can also analyze the user's interests and recommend events tailored to specific seasons or regions. Furthermore, it can suggest appropriate activities based on the user's hobbies. This enables the recommendation of appropriate events and activities that take the user's hobbies and interests into consideration.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The question reception section receives user questions. User questions include weather information, schedule management, reminder setting, information retrieval, etc. The question reception section receives questions through a character that appears when the user opens the application. Questions can also be received via voice input or text input. Step 2: The answering unit immediately answers questions received by the question receiving unit. The answering unit accesses a weather database to provide weather information and retrieves the latest weather data. It also accesses a calendar application for schedule management and reminder setting, allowing users to add schedules and set reminders. Step 3: The task management department accepts user task requests. For example, if a user asks, "Can you recommend a nearby restaurant?", the department accepts that task. Step 4: The Reminders Department adds tasks received by the Task Management Department to the schedule and sets reminders. For example, if a user requests "Remind me of tomorrow's meeting," the department adds the meeting to the schedule and sets a reminder based on that request. Step 5: The information retrieval unit receives the user's information retrieval request. For example, if the user asks, "Tell me about nearby restaurants," the unit receives that information retrieval request. Step 6: The Information Provision Department provides information based on the information retrieval request received by the Information Retrieval Department. For example, to provide information on nearby restaurants, it accesses a restaurant database and retrieves the latest restaurant information.

[0062] (Example 2) A virtual assistant system according to an embodiment of the present invention is a system in which a robot or character acts as a virtual assistant as an AI chatbot. When a user opens an application, this virtual assistant system displays a character and accepts the user's questions and requests. For example, when a user asks, "What's the weather like tomorrow?", the AI ​​chatbot instantly provides weather information. When a user requests, "Please remind me about tomorrow's meeting," the AI ​​chatbot adds the meeting to the schedule and sets a reminder. Furthermore, the system also has an information search function. When a user asks, "What restaurants are nearby?", the AI ​​chatbot provides information about nearby restaurants. In this way, by responding to various user needs, convenience is improved. This service allows users to easily obtain information and manage tasks through the app, thereby improving the efficiency of daily life. For example, when a user opens an application, a character appears and accepts the user's questions and requests. For example, when a user asks, "What's the weather like tomorrow?", the AI ​​chatbot instantly provides weather information. When a user requests, "Please remind me about tomorrow's meeting," the AI ​​chatbot adds the meeting to the schedule and sets a reminder. Furthermore, the system also has an information search function. When a user requests, "What restaurants are nearby?", the AI ​​chatbot provides information about nearby restaurants. In this way, responding to various user needs improves convenience. This service allows users to easily obtain information and manage tasks through the app, thereby improving the efficiency of daily life. As a result, the virtual assistant system can immediately respond to users' questions, task requests, and information search requests, improving convenience.

[0063] The virtual assistant system according to this embodiment comprises a question receiving unit, an answering unit, a task management unit, a reminder unit, an information retrieval unit, and an information provision unit. The question receiving unit receives questions from the user. User questions include, but are not limited to, weather information, schedule management, reminder setting, and information retrieval. The question receiving unit receives questions, for example, through a character displayed when the user opens the application. The question receiving unit can also receive questions through voice input or text input. For example, if the user asks, "What's the weather like tomorrow?", the question receiving unit receives the question. The answering unit immediately answers the question received by the question receiving unit. For example, the answering unit accesses a weather database to obtain the latest weather information in order to provide weather information. The answering unit can also access a calendar application to add schedules and set reminders for schedule management and reminder setting. For example, if the user requests, "Remind me of tomorrow's meeting," the answering unit adds the meeting to the schedule and sets a reminder based on the request. The Task Management Unit accepts task requests from users. For example, if a user asks, "Tell me about nearby restaurants," the Task Management Unit accepts the task. The Reminder Unit adds the task accepted by the Task Management Unit to the schedule and sets a reminder. For example, if a user requests, "Remind me about tomorrow's meeting," the Reminder Unit adds the meeting to the schedule and sets a reminder based on the request. The Information Retrieval Unit accepts information retrieval requests from users. For example, if a user asks, "Tell me about nearby restaurants," the Information Retrieval Unit accepts the information retrieval request. The Information Provision Unit provides information based on the information retrieval requests accepted by the Information Retrieval Unit. For example, to provide information on nearby restaurants, the Information Provision Unit accesses a restaurant database and obtains the latest restaurant information. As a result, the virtual assistant system according to this embodiment can respond immediately to user questions, task requests, and information retrieval requests, improving convenience.

[0064] The question reception unit can estimate the user's emotions and change the way it receives questions based on the estimated emotions. For example, if the user is stressed, the question reception unit can provide a simple interface and minimize the input steps. If the user is relaxed, the question reception unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the question reception unit can prioritize voice input to quickly receive questions. This improves the user experience by providing a question reception method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] When accepting a question, the question acceptance unit can analyze the user's past question history and select an appropriate acceptance method. For example, the question acceptance unit can automatically display questions that the user has frequently asked in the past as candidates. The question acceptance unit can also preferentially suggest question methods (voice, text, etc.) that the user has used in the past. Furthermore, the question acceptance unit can predict and suggest question content to be used in a specific time period based on the user's past question history. This makes it possible to provide a more appropriate question acceptance method by taking the user's past question history into consideration. Some or all of the above-mentioned processing in the question acceptance unit may be performed using, or without, AI. For example, the question acceptance unit can input the user's past question history data into a generation AI and have the generation AI select the optimal acceptance method.

[0066] The question receiving unit can perform filtering based on the user's current situation or area of ​​interest when receiving a question. For example, the question receiving unit preferentially receives questions related to the user's current location. The question receiving unit can also automatically filter related questions based on the user's area of ​​interest. Furthermore, the question receiving unit can also suggest appropriate questions based on the user's current situation (time of day, weather, etc.). This makes it possible to receive appropriate questions based on the user's current situation and area of ​​interest. Some or all of the above-described processing in the question receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the question receiving unit can input the user's location information data to the generation AI and cause the generation AI to filter related questions.

[0067] The question reception unit can estimate the user's emotions and prioritize questions based on those emotions. For example, if the user has an urgent question, the question reception unit will prioritize it and respond quickly. If the user is relaxed, the question reception unit can also prioritize questions with normal priority. Furthermore, if the user is stressed, the question reception unit can prioritize high-priority questions. This allows for quick and appropriate responses by determining question priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question reception unit may be performed using AI or not. For example, the question reception unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0068] When receiving a question, the question receiving unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, when the user is in a specific area, the question receiving unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the question receiving unit can also prioritize receiving questions related to the travel destination. Furthermore, when the user is at home, the question receiving unit can also prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the question receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the question receiving unit can input the user's location information data to the generation AI and have the generation AI execute the prioritization of related questions.

[0069] The question reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the question reception unit can prioritize receiving questions related to topics the user is discussing on social media. The question reception unit can also analyze the content of the user's social media posts and suggest related questions. Furthermore, the question reception unit can also receive questions related to topics that the user's social media followers and friends are interested in. In this way, by analyzing the user's social media activity, related questions can be appropriately received. Some or all of the above-described processing in the question reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the question reception unit can input the user's social media data into a generation AI and cause the generation AI to receive related questions.

[0070] The answering unit can estimate the user's emotions and change the way the answer is expressed based on the estimated user's emotions. For example, if the user is nervous, the answering unit can provide an answer in a calm tone. Furthermore, if the user is relaxed, the answering unit can also provide an answer in a friendly tone. Furthermore, if the user is in a hurry, the answering unit can provide a concise and quick answer. This improves the user experience by providing an answer expression method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the answering unit can be performed using, for example, AI, or without AI. For example, the answering unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0071] The answering unit can adjust the level of detail in its answers based on the importance of the question. For example, it can provide detailed answers to high-importance questions, and concise answers to low-importance questions. Furthermore, it can provide necessary information appropriately according to the importance of the question. This allows for a response that meets the user's needs by providing detailed answers according to the importance of the question. The importance of a question is evaluated based on criteria such as urgency and impact. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input question importance data into a generating AI and have the generating AI adjust the level of detail in the answers.

[0072] The answering unit can use different answering algorithms depending on the category of the question when providing an answer. For example, the answering unit can apply a specialized answering algorithm to technical questions. It can also apply a concise answering algorithm to general questions. Furthermore, it can apply a customer support-specific answering algorithm to questions related to customer support. This allows for more accurate answers by applying the appropriate answering algorithm according to the category of the question. Question categories are identified by criteria such as technical questions or business-related questions. Some or all of the above processing in the answering unit may be performed using AI, for example, or without AI. For example, the answering unit can input question category data into a generating AI and have the generating AI apply different answering algorithms.

[0073] The response unit can estimate the user's emotions and adjust the length of its response based on the estimated emotions. For example, if the user is in a hurry, the response unit can provide a short, concise response. If the user is relaxed, it can provide a longer response with more detailed explanations. Furthermore, if the user is excited, it can provide a response with visually stimulating effects. This improves the user experience by providing response lengths that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] When answering, the answering unit can set the priority of answers based on the time of submission of the question. The answering unit can determine the priority based on, for example, the time period when the question was submitted. The answering unit can also determine the priority based on the date when the question was submitted. Furthermore, the answering unit can quickly provide answers based on the timing when the question was submitted. This enables a prompt and appropriate response by determining the priority based on the time when the question was submitted. The time when the question was submitted is identified based on, for example, the date and time of submission or the elapsed time since submission. Some or all of the above-mentioned processing in the answering unit can be performed using, for example, AI, or can be performed without using AI. For example, the answering unit can input question submission time data into the generating AI and have the generating AI set the priority of the answers.

[0075] The answering unit can change the order of answers based on the relevance of the question when answering. For example, the answering unit can prioritize providing the most relevant answer based on the relevance of the question. The answering unit can also adjust the order of answers based on the relevance of the question. Furthermore, the answering unit can also provide appropriate answers based on the relevance of the question. This enables responses that meet the needs of users by providing an order of answers based on the relevance of the question. The relevance of a question is evaluated based on criteria such as the similarity of the question content or related topics. Some or all of the above-mentioned processing in the answering unit may be performed using, for example, AI, or may be performed without using AI. For example, the answering unit can input question relevance data to a generation AI and have the generation AI adjust the order of the answers.

[0076] The task management unit can estimate the user's emotions and change the task acceptance method based on the estimated user emotions. For example, if the user is feeling stressed, the task management unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the task management unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the task management unit can prioritize voice input and enable quick task acceptance. This improves the user experience by providing a task acceptance method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the task management unit can be performed using AI, for example, or without AI. For example, the task management unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] The task management unit can analyze the user's past task history and select an appropriate task management method during task management. For example, the task management unit can automatically display tasks that the user has frequently requested in the past as candidates. The task management unit can also prioritize and suggest task management methods (such as voice and text) that the user has used in the past. Furthermore, the task management unit can predict and suggest tasks to be used during a specific time period based on the user's past task history. This makes it possible to provide a more appropriate task management method by taking the user's past task history into consideration. Some or all of the above-described processing in the task management unit can be performed using, for example, AI, or without AI. For example, the task management unit can input the user's past task history data into a generation AI and have the generation AI select the optimal task management method.

[0078] The task management unit can perform filtering based on the user's current situation or areas of interest when managing tasks. For example, the task management unit prioritizes managing tasks related to the user's current location. The task management unit can also automatically filter related tasks based on the user's areas of interest. Furthermore, the task management unit can also suggest appropriate tasks based on the user's current situation (time of day, weather, etc.). This makes it possible to manage appropriate tasks based on the user's current situation and areas of interest. Some or all of the above-described processing in the task management unit may be performed using AI, for example, or may be performed without using AI. For example, the task management unit can input the user's location information data into the generation AI and have the generation AI filter related tasks.

[0079] The task management unit can estimate the user's emotions and set task priorities based on the estimated user emotions. For example, if the user requests an urgent task, the task management unit prioritizes the task and responds quickly. The task management unit can also manage tasks with normal priority when the user is relaxed. Furthermore, if the user is feeling stressed, the task management unit can prioritize tasks of high importance. This enables a prompt and appropriate response by determining task priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the task management unit can be performed using, for example, an AI, or without an AI. For example, the task management unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] During task management, the task management unit can prioritize highly relevant tasks based on the user's geographical location information. For example, when the user is in a specific area, the task management unit prioritizes tasks related to that area. Furthermore, when the user is traveling, the task management unit can prioritize tasks related to the travel destination. Furthermore, when the user is at home, the task management unit can prioritize tasks related to the area around the user's home. This allows highly relevant tasks to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the task management unit may be performed using, or without, AI. For example, the task management unit can input the user's location information data into a generation AI and have the generation AI prioritize related tasks.

[0081] During task management, the task management unit can analyze the user's social media activities and manage related tasks. For example, the task management unit prioritizes managing tasks related to topics the user is discussing on social media. The task management unit can also analyze the content of the user's social media posts and suggest related tasks. Furthermore, the task management unit can manage tasks related to topics that the user's social media followers and friends are interested in. This allows for appropriate management of related tasks by analyzing the user's social media activities. Some or all of the above-described processing in the task management unit may be performed using, for example, AI, or may be performed without using AI. For example, the task management unit can input the user's social media data into a generation AI and have the generation AI manage related tasks.

[0082] The reminder unit can estimate the user's emotions and change the way reminders are set based on the estimated emotions. For example, if the user is stressed, the reminder unit can provide a simple interface and minimize the input steps. If the user is relaxed, the reminder unit can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reminder unit can prioritize voice input to allow for quick reminder setting. This improves the user experience by providing a reminder setting method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] When setting a reminder, the reminder unit can analyze the user's past reminder history and select an appropriate setting method. For example, the reminder unit can automatically display reminders that the user has frequently set in the past as candidates. The reminder unit can also prioritize and suggest reminder setting methods (voice, text, etc.) that the user has used in the past. Furthermore, the reminder unit can predict and suggest reminders to be used in specific time periods based on the user's past reminder history. This makes it possible to provide a more appropriate reminder setting method by taking the user's past reminder history into consideration. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input the user's past reminder history data into a generation AI and have the generation AI select the optimal setting method.

[0084] When setting a reminder, the reminder unit can filter reminders based on the user's current situation or areas of interest. For example, the reminder unit can prioritize setting reminders related to the user's current location. The reminder unit can also automatically filter relevant reminders based on the user's areas of interest. Furthermore, the reminder unit can also suggest appropriate reminders based on the user's current situation (time of day, weather, etc.). This makes it possible to set appropriate reminders based on the user's current situation and areas of interest. Some or all of the above-mentioned processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit can input the user's location information data into the generation AI and have the generation AI filter relevant reminders.

[0085] The reminder unit can estimate the user's emotions and prioritize reminders based on the estimated user emotions. For example, if the user has set an urgent reminder, the reminder unit prioritizes it and responds promptly. The reminder unit can also manage reminders with normal priority when the user is relaxed. Furthermore, if the user is feeling stressed, the reminder unit can prioritize important reminders. This enables prompt and appropriate responses by prioritizing reminders according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminder unit can be performed using, for example, an AI. For example, the reminder unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0086] When setting a reminder, the reminder unit can prioritize highly relevant reminders based on the user's geographical location information. For example, if the user is in a specific area, the reminder unit can prioritize reminders related to that area. Furthermore, if the user is traveling, the reminder unit can prioritize reminders related to the travel destination. Furthermore, if the user is at home, the reminder unit can prioritize reminders related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant reminders can be prioritized. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's location information data into the generation AI and cause the generation AI to prioritize relevant reminders.

[0087] When setting a reminder, the reminder unit can analyze the user's social media activity and set a relevant reminder. For example, the reminder unit can prioritize setting reminders related to topics the user is talking about on social media. The reminder unit can also analyze the content of the user's social media posts and suggest relevant reminders. Furthermore, the reminder unit can set reminders related to topics that the user's social media followers and friends are interested in. This allows relevant reminders to be appropriately set by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reminder unit may be performed using, or without, AI, for example. For example, the reminder unit can input the user's social media data into a generation AI and have the generation AI set relevant reminders.

[0088] The information search unit can estimate the user's emotions and change the information search acceptance method based on the estimated user emotions. For example, if the user is feeling stressed, the information search unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the information search unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the information search unit can prioritize voice input and quickly accept information searches. This improves the user experience by providing an information search acceptance method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information search unit may be performed using AI, or without AI. For example, the information search unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The information retrieval unit can analyze the user's past search history and select an appropriate search method when an information retrieval is performed. For example, the information retrieval unit can automatically display as suggestions what the user has frequently searched for in the past. The information retrieval unit can also prioritize suggesting search methods (voice, text, etc.) that the user has used in the past. Furthermore, the information retrieval unit can predict and suggest search content that the user will use at a specific time of day based on their past search history. This allows for the provision of a more appropriate search method by considering the user's past search history. Some or all of the above processing in the information retrieval unit may be performed using AI, for example, or without AI. For example, the information retrieval unit can input the user's past search history data into a generating AI and have the generating AI select the optimal search method.

[0090] The information retrieval unit can filter information based on the user's current situation or areas of interest during an information retrieval. For example, the information retrieval unit may prioritize searching for information related to the user's current location. The information retrieval unit can also automatically filter relevant information based on the user's areas of interest. Furthermore, the information retrieval unit can suggest appropriate information based on the user's current situation (time of day, weather, etc.). This allows the user to search for appropriate information based on their current situation and areas of interest. Some or all of the above processing in the information retrieval unit may be performed using AI, for example, or without AI. For example, the information retrieval unit can input the user's location data into a generating AI and have the generating AI perform the filtering of relevant information.

[0091] The information search unit can estimate the user's emotions and set information search priorities based on the estimated user emotions. For example, when the user is searching for urgent information, the information search unit prioritizes the search and responds quickly. Furthermore, when the user is relaxed, the information search unit can also search for information with normal priority. Furthermore, when the user is feeling stressed, the information search unit can prioritize searching for information of high importance. This enables a quick and appropriate response by determining the information search priority according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information search unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] When searching for information, the information search unit can prioritize searching for highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, the information search unit can prioritize searching for information related to that area. Furthermore, if the user is traveling, the information search unit can also prioritize searching for information related to the travel destination. Furthermore, if the user is at home, the information search unit can also prioritize searching for information related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant information can be prioritized. Some or all of the above-described processing in the information search unit may be performed using AI, for example, or may be performed without using AI. For example, the information search unit can input the user's location information data into the generation AI and have the generation AI prioritize relevant information.

[0093] When searching for information, the information search unit can analyze the user's social media activity and search for related information. For example, the information search unit can prioritize searching for information related to topics the user is talking about on social media. The information search unit can also analyze the content of the user's social media posts and suggest related information. Furthermore, the information search unit can search for information related to topics that the user's social media followers and friends are interested in. This makes it possible to appropriately search for related information by analyzing the user's social media activity. Some or all of the above-mentioned processing in the information search unit may be performed using, or without, AI. For example, the information search unit can input the user's social media data into the generation AI and cause the generation AI to search for related information.

[0094] The information providing unit can estimate the user's emotions and change the way information is presented based on the estimated user's emotions. For example, if the user is nervous, the information providing unit can provide information in a calm tone. Furthermore, if the user is relaxed, the information providing unit can also provide information in a friendly tone. Furthermore, if the user is in a hurry, the information providing unit can provide concise and quick information. This improves the user experience by providing an information presentation method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0095] The information providing unit can change the level of detail of the information provided based on the importance of the search results when providing the information. For example, the information providing unit provides detailed information for information with high importance. The information providing unit can also provide concise information for information with low importance. Furthermore, the information providing unit can appropriately provide necessary information according to the importance of the search results. This makes it possible to respond to user needs by providing detailed information according to the importance of the search results. The importance of the search results is evaluated based on criteria such as urgency and impact. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input importance data of the search results to the generation AI and cause the generation AI to adjust the level of detail of the information provided.

[0096] When providing information, the information providing unit can use different provision algorithms depending on the category of the search result. For example, the information providing unit can apply a specialized provision algorithm to technical information. The information providing unit can also apply a simple provision algorithm to general information. Furthermore, the information providing unit can apply a provision algorithm specialized for customer support to information related to customer support. This allows more accurate information to be provided by applying an appropriate provision algorithm depending on the category of the search result. The category of the search result is identified based on criteria such as technical information or business information. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input category data of the search result to the generation AI and cause the generation AI to apply different provision algorithms.

[0097] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is in a hurry, the information provider can provide short, concise information. If the user is relaxed, the information provider can also provide longer information with detailed explanations. Furthermore, if the user is excited, the information provider can provide information with visually stimulating effects. This improves the user experience by providing information of appropriate length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] When providing information, the information providing unit can set a priority for providing the information based on the time of submission of the search results. The information providing unit can determine the priority based on, for example, the time period in which the search results were submitted. The information providing unit can also determine the priority based on the date the search results were submitted. Furthermore, the information providing unit can quickly provide information based on the timing of submission of the search results. This enables a prompt and appropriate response by determining the priority based on the time of submission of the search results. The time of submission of the search results is specified based on, for example, the date and time of submission or the elapsed time since submission. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the information providing unit can input data on the time of submission of the search results to the generation AI and cause the generation AI to set the priority for providing the information.

[0099] The information providing unit can change the order of information provision based on the relevance of search results when providing information. For example, the information providing unit can prioritize providing the most relevant information based on the relevance of the search results. The information providing unit can also adjust the order of information provision based on the relevance of the search results. Furthermore, the information providing unit can also provide appropriate information based on the relevance of the search results. This enables responding to user needs by providing an order of information provision based on the relevance of the search results. The relevance of search results is evaluated based on criteria such as the similarity of the search query or related topics. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input relevance data of search results to a generation AI and cause the generation AI to adjust the order of information provision. === Hard Collateral 1-1 === Each of the multiple elements, including the question receiving unit, answering unit, task management unit, reminder unit, information search unit, and information providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the question receiving unit is realized by the control unit 46A of the smart device 14 and receives a user's question. The answering unit is realized by the specific processing unit 290 of the data processing device 12 and provides an immediate answer to the question. The task management unit is realized by the control unit 46A of the smart device 14 and receives a user's task request. The reminder unit is realized by the specific processing unit 290 of the data processing device 12 and adds a task to a schedule and sets a reminder. The information search unit is realized by the control unit 46A of the smart device 14 and receives a user's information search request. The information providing unit is realized by the specific processing unit 290 of the data processing device 12 and provides information based on the information search request. === Hard Collateral 1-2 === Each of the multiple elements, including the question receiving unit, answering unit, task management unit, reminder unit, information search unit, and information providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the question receiving unit is realized by the control unit 46A of the smart glasses 214 and receives a user's question. The answering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an immediate answer to the question. The task management unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives a user's task request. The reminder unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and adds a task to a schedule and sets a reminder. The information search unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives a user's information search request. The information providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information based on the information search request. === Hard Collateral 1-3 === Each of the multiple elements described above, including the question receiving unit, answering unit, task management unit, reminder unit, information retrieval unit, and information provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the question receiving unit is implemented by the control unit 46A of the headset terminal 314 and receives questions from the user. The answering unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides immediate answers to questions. The task management unit is implemented by the control unit 46A of the headset terminal 314 and receives task requests from the user. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and adds tasks to the schedule and sets reminders. The information retrieval unit is implemented by the control unit 46A of the headset terminal 314 and receives information retrieval requests from the user. The information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information based on information retrieval requests. === Hard Collateral 1-4 === Each of the multiple elements described above, including the question receiving unit, answering unit, task management unit, reminder unit, information retrieval unit, and information provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the question receiving unit is implemented by the control unit 46A of the robot 414 and receives questions from the user. The answering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides immediate answers to questions. The task management unit is implemented, for example, by the control unit 46A of the robot 414 and receives task requests from the user. The reminder unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adds tasks to the schedule and sets reminders. The information retrieval unit is implemented, for example, by the control unit 46A of the robot 414 and receives information retrieval requests from the user. The information provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides information based on the information retrieval request.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The virtual assistant system can further include a health management unit that monitors the user's health status. The health management unit can acquire the user's health data (e.g., heart rate, blood pressure, sleep patterns, etc.) and provide appropriate advice based on this. For example, if the user's heart rate is high, it can suggest breathing techniques to help them relax. Also, if the user's sleep pattern is disrupted, it can provide advice to improve the quality of their sleep. Furthermore, the health management unit can analyze the user's health data and set reminders for regular health checks. This allows the user's health status to be continuously monitored and supports health maintenance.

[0102] The virtual assistant system can also include a music recommendation unit that estimates the user's emotions and recommends music based on those emotions. For example, if the user is feeling stressed, the music recommendation unit might recommend relaxing music. If the user is happy, it could recommend energetic music. Furthermore, if the user is sad, it could recommend mood-soothing music. This allows the system to improve the user's mood by providing music that matches their emotions.

[0103] The virtual assistant system can further include a purchase recommendation unit that analyzes the user's purchasing history and recommends appropriate products. The purchase recommendation unit recommends related products based on products the user has purchased in the past, for example. It can also analyze the user's purchasing patterns and recommend products tailored to specific seasons or events. Furthermore, it can recommend products based on specific brands or categories from the user's purchasing history. This enables appropriate product recommendations that take the user's purchasing history into consideration.

[0104] The virtual assistant system can further include an exercise suggestion unit that estimates the user's emotions and suggests an exercise program based on the estimated emotions. For example, if the user is feeling stressed, the exercise suggestion unit can suggest relaxing yoga or stretching. If the user is feeling energetic, the exercise suggestion unit can also suggest running or high-intensity interval training (HIIT). Furthermore, if the user is tired, the exercise suggestion unit can suggest light walking or relaxation exercises. This allows the system to support health maintenance by providing an exercise program that matches the user's emotions.

[0105] The virtual assistant system can further include a learning recommendation unit that analyzes the user's learning history and recommends appropriate learning content. The learning recommendation unit recommends related learning content based on, for example, what the user has learned in the past. It can also analyze the user's learning progress and suggest what content to study next. It can also suggest the optimal learning method based on the user's learning style. This makes it possible to recommend appropriate learning content that takes the user's learning history into consideration.

[0106] The virtual assistant system can further include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the relaxation suggestion unit can suggest meditation or deep breathing methods. Also, if the user is tired, it can suggest relaxing aromatherapy or massage methods. Furthermore, if the user is feeling anxious, it can provide relaxing music or natural sounds. In this way, by providing relaxation methods according to the user's emotions, it is possible to reduce the user's stress.

[0107] The virtual assistant system can also include a travel recommendation unit that analyzes the user's travel history and recommends appropriate destinations. For example, the travel recommendation unit can recommend relevant destinations based on places the user has visited in the past. It can also analyze the user's travel patterns and recommend destinations suited to specific seasons or events. Furthermore, it can recommend destinations based on specific interests and preferences derived from the user's travel history. This enables the recommendation of appropriate destinations that take the user's travel history into consideration.

[0108] The virtual assistant system can further include a meal suggestion unit that estimates the user's emotions and proposes a meal plan based on the estimated emotions. For example, if the user is feeling stressed, the meal suggestion unit can suggest recipes using relaxing ingredients. If the user is feeling energetic, the unit can also suggest nutritious meals. Furthermore, if the user is tired, the unit can suggest easy-to-prepare healthy meals. This can support the user's health by providing a meal plan that matches their emotions.

[0109] The virtual assistant system can also include an event recommendation unit that analyzes the user's hobbies and interests and recommends appropriate events and activities. For example, the event recommendation unit can recommend relevant events based on events the user has previously attended. It can also analyze the user's interests and recommend events tailored to specific seasons or regions. Furthermore, it can suggest appropriate activities based on the user's hobbies. This enables the recommendation of appropriate events and activities that take the user's hobbies and interests into consideration.

[0110] The virtual assistant system can further include a communication suggestion unit that estimates the user's emotions and suggests a communication method based on the estimated emotions. For example, if the user is feeling stressed, the communication suggestion unit can suggest a relaxing communication method. Also, if the user is happy, it can suggest an energetic communication method. Furthermore, if the user is sad, it can suggest a communication method that will soothe the user's mood. This allows the user's mood to be improved by providing a communication method that corresponds to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The question reception section receives user questions. User questions include weather information, schedule management, reminder setting, information retrieval, etc. The question reception section receives questions through a character that appears when the user opens the application. Questions can also be received via voice input or text input. Step 2: The answering unit immediately answers questions received by the question receiving unit. The answering unit accesses a weather database to provide weather information and retrieves the latest weather data. It also accesses a calendar application for schedule management and reminder setting, allowing users to add schedules and set reminders. Step 3: The task management department accepts user task requests. For example, if a user asks, "Can you recommend a nearby restaurant?", the department accepts that task. Step 4: The Reminders Department adds tasks received by the Task Management Department to the schedule and sets reminders. For example, if a user requests "Remind me of tomorrow's meeting," the department adds the meeting to the schedule and sets a reminder based on that request. Step 5: The information retrieval unit receives the user's information retrieval request. For example, if the user asks, "Tell me about nearby restaurants," the unit receives that information retrieval request. Step 6: The Information Provision Department provides information based on the information retrieval request received by the Information Retrieval Department. For example, to provide information on nearby restaurants, it accesses a restaurant database and retrieves the latest restaurant information.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question receiving unit that receives questions from users; a replying unit that promptly replies to the questions received by the question receiving unit; a task management unit that accepts task requests from users; a reminder unit that adds the task accepted by the task management unit to a schedule and sets a reminder; an information search unit that accepts information search requests from users; an information providing unit that provides information based on the information search request received by the information searching unit; A system characterized by:

2. The question receiving unit Estimate the user's emotions and change the way questions are accepted based on the estimated user emotions.

2. The system of claim 1.

3. The question receiving unit When accepting a question, analyze the user's past question history and select the appropriate method of acceptance.

2. The system of claim 1.

4. The question receiving unit Filter questions based on the user's current situation or interests 2. The system of claim 1.

5. The question receiving unit Infer user sentiment and prioritize questions based on the estimated sentiment 2. The system of claim 1.

6. The question receiving unit When accepting questions, prioritize relevant questions based on the user's geographic location information.

2. The system of claim 1.

7. The question receiving unit When a question is received, the system analyzes the user's social media activity and receives related questions.

2. The system of claim 1.

8. The answering section Inferring user sentiment and changing the way answers are expressed based on the estimated sentiment 2. The system of claim 1.

9. The answering section When answering, change the detail of your answer based on the importance of the question 2. The system of claim 1.

10. The answering section When answering, use different answering algorithms depending on the question category 2. The system of claim 1.

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