System

Abstract

An object of the system according to the embodiment is to promptly and accurately provide specific advice requested by a user.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives difficulty from the user, physical information of the child, and daily life information. The generation unit analyzes the information received by the reception unit and generates an answer requested by the user. The providing unit provides advice to the user based on the answer generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately provide specific advice that users desire.

[0005] The system according to the embodiment aims to provide specific advice that a user desires quickly and accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from a user about difficulties, physical information about the child, and daily life information. The generation unit analyzes the information received by the reception unit and generates an answer that the user is looking for. The provision unit provides advice to the user based on the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately provide specific advice that the user desires. [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 system according to an embodiment of the present invention allows a user to input their concerns, their child's physical information, and daily life information into a generation AI, and provides the user with the answer they are looking for each time they converse with the generation AI. This system allows users to input their concerns, their child's physical information, and daily life information, and the generation AI analyzes the input information, generates the answer the user is looking for, and provides appropriate advice. The system also functions as a subscription service, introducing products to improve the user's concerns. This allows users who are raising children to have someone they can easily consult with at any time and receive appropriate advice. Furthermore, the subscription service allows users to easily obtain products to improve their concerns, thereby reducing the burden of raising children.

[0029] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from the user about their concerns, their child's physical information, and daily life information. For example, information about their child's fever, dietary concerns, sleep problems, etc. can be input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate the answer the user is looking for. For example, the generation AI generates advice on how to deal with a child's fever, how to improve their diet, and how to improve sleep quality. The provision unit provides appropriate advice to the user based on the answer generated by the generation unit. For example, the provision unit presents the answer generated by the generation AI to the user and suggests specific ways to deal with or improve the situation. This allows the system according to the embodiment to provide appropriate advice to the user about their concerns.

[0030] The generation unit can use the generation AI to analyze the information input by the user and generate an answer based on past data and examples. The generation unit, for example, uses the generation AI to analyze the information input by the user. The generation AI generates the optimal answer based on past data and examples. For example, the generation AI references past user data and existing case studies to generate the optimal answer to the user's problem. This allows for more accurate advice to be provided by generating the optimal answer based on past data and examples.

[0031] The providing unit can present the answer generated by the generation AI to the user and suggest ways to deal with or improve the problem. For example, the providing unit presents the answer generated by the generation AI to the user. The generation AI suggests specific ways to deal with or improve the problem. For example, the generation AI provides specific advice on how to deal with a child's fever, how to improve diet, or how to improve sleep quality. By suggesting specific ways to deal with or improve the problem, the user can more easily find a solution to their problem.

[0032] The provision unit can introduce products to improve the user's difficulties as a subscription service. The provision unit, for example, introduces products to improve the user's difficulties as a subscription service. The generation AI suggests appropriate products based on the user's concerns and encourages them to purchase them. For example, the generation AI suggests health foods, childcare products, lifestyle improvement goods, etc. This allows the user to easily obtain appropriate products by introducing products to improve the user's concerns.

[0033] The provision unit allows the generation AI to suggest products based on the user's concerns and encourage the purchase. For example, the provision unit allows the generation AI to suggest products based on the user's concerns. The generation AI suggests appropriate products based on the user's concerns and encourages the purchase. For example, the generation AI suggests health foods, childcare products, lifestyle improvement goods, etc. This allows the user to easily obtain products that will improve their concerns by suggesting appropriate products based on their concerns.

[0034] The reception unit can analyze the user's past input history and select the reception method. The reception unit, for example, analyzes the user's past input history. The generation AI selects the optimal reception method based on the user's past input history. For example, it prioritizes and suggests input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze patterns of information that the user has entered in the past and automatically select the optimal reception method. Furthermore, it can also suggest the optimal reception method for a specific time period based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history.

[0035] The reception unit can filter information based on the user's living situation and areas of interest when receiving the information. For example, the reception unit filters information based on the user's living situation and areas of interest when receiving the information. The generation AI accepts only relevant information based on the user's living situation and areas of interest. For example, if the user inputs their current living situation, only relevant information is accepted based on that information. It is also possible to set the user's areas of interest in advance and prioritize accepting only information related to those areas. Furthermore, it is also possible to filter unnecessary information and accept only necessary information based on the user's living situation and areas of interest. In this way, by filtering information based on the user's living situation and areas of interest, it is possible to accept only necessary information.

[0036] The reception unit can select a reception means according to the user's input method when receiving information. For example, the reception unit selects a reception means according to the user's input method when receiving information. The generation AI selects the optimal reception means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the generation AI can accept the information using voice recognition technology. Also, if the user selects text input, the generation AI can also accept the information using text analysis technology. Furthermore, if the user selects image input, the generation AI can also accept the information using image analysis technology. This allows the optimal reception means to be selected according to the user's input method, allowing for smooth information reception.

[0037] The reception unit can prioritize receiving relevant information in consideration of the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving relevant information in consideration of the user's geographical location information when receiving information. The generation AI prioritizes receiving highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, it can prioritize receiving information related to that area. Also, if the user is traveling, it can prioritize receiving related information based on the user's current location. Furthermore, if the user is in a specific location, it can prioritize receiving information related to that location. In this way, it is possible to prioritize receiving highly relevant information by considering the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when receiving information and receive related information. For example, the reception unit analyzes the user's social media activity when receiving information. The generation AI preferentially receives related information based on the user's social media activity. For example, the generation AI preferentially receives related information based on information shared by the user on social media. The generation AI can also analyze the user's social media activity history and receive related information. Furthermore, the generation AI can also receive related information based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related information.

[0039] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving information. For example, the reception unit reflects the user's past feedback when receiving information. The generation AI proposes the optimal reception method based on the user's past feedback. For example, the generation AI proposes the optimal reception method based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and customize the reception method. Furthermore, the reception method can be continuously improved based on the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0040] The generation unit can adjust the details of the answer based on the importance of the input information when generating an answer. For example, the generation unit adjusts the details of the answer based on the importance of the input information when generating an answer. The generation AI generates a detailed answer for information of high importance, and generates a concise answer for information of low importance. For example, the generation AI generates an answer including a detailed explanation for information of high importance, and generates a concise answer for information of low importance. In this way, by adjusting the detail of the answer based on the importance of the input information, it is possible to provide an answer with an appropriate level of detail.

[0041] The generation unit can apply different generation algorithms depending on the category of input information when generating an answer. For example, the generation unit applies different generation algorithms depending on the category of input information when generating an answer. The generation AI refers to a medical database for health information, and refers to an education database for education information. For example, the generation AI generates an answer by referring to a medical database for health information, and refers to an education database for education information. In this way, by applying different generation algorithms depending on the category of input information, more appropriate answers can be provided.

[0042] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit, for example, refers to the user's past answer results. The generation AI improves the accuracy of the answer based on the user's past answer results. For example, the generation AI improves the accuracy of the answer based on answers the user has received in the past. It can also analyze the user's past answer results and generate an optimal answer. Furthermore, the generation AI can improve the accuracy of the answer by referring to the user's past feedback. In this way, the accuracy of the answer can be improved by referring to the user's past answer results.

[0043] The generation unit can determine the order of answers based on the submission time of the input information when generating answers. For example, the generation unit determines the order of answers based on the submission time of the input information when generating answers. The generation AI generates answers with priority for highly urgent information and generates answers with priority for information that was submitted earlier. For example, the generation AI generates answers with priority for highly urgent information and generates answers with priority for information that was submitted earlier. In this way, by determining the priority of answers based on the submission time of the input information, it is possible to respond quickly to highly urgent information.

[0044] The generation unit can adjust the order of answers based on the relevance of the input information when generating an answer. For example, the generation unit adjusts the order of answers based on the relevance of the input information when generating an answer. The generation AI generates answers preferentially for highly relevant information and generates answers later for less relevant information. For example, the generation AI generates answers preferentially for highly relevant information and generates answers later for less relevant information. In this way, by adjusting the order of answers based on the relevance of the input information, highly relevant information can be addressed preferentially.

[0045] The generation unit can change the use of technical terminology in the answer according to the user's level of expertise when generating an answer. For example, the generation unit adjusts the use of technical terminology in the answer according to the user's level of expertise when generating an answer. If the user has technical expertise, the generation AI generates an answer that uses a lot of technical terminology, and if the user does not have technical expertise, the generation AI generates an answer that explains in simple terms. For example, if the user has technical expertise, the generation AI generates an answer that uses a lot of technical terminology, and if the user does not have technical expertise, the generation AI generates an answer that explains in simple terms. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide an answer that is easy for the user to understand.

[0046] The providing unit can select an advice method by analyzing the user's past behavioral history when providing advice. For example, the providing unit analyzes the user's past behavioral history when providing advice. The generation AI selects the optimal advice method based on the user's past behavioral history. For example, the generation AI selects the optimal advice method based on advice the user has received in the past. The generation AI can also analyze the user's past behavioral history and select the optimal advice method. Furthermore, the generation AI can select the optimal advice method by referring to the user's past feedback. In this way, the optimal advice method can be selected by analyzing the user's past behavioral history.

[0047] The providing unit can customize the means of advice based on the user's current living situation when providing advice. For example, the providing unit customizes the means of advice based on the user's current living situation when providing advice. The generation AI suggests the optimal means of advice according to the user's living situation. For example, if the user inputs their current living situation, the generation AI customizes the means of advice based on that information. The generation AI can also suggest the optimal means of advice according to the user's living situation. Furthermore, the generation AI can continuously improve the means of advice based on the user's living situation. In this way, more appropriate advice can be provided by customizing the means of advice based on the user's living situation.

[0048] The providing unit can improve the method of advice by reflecting user feedback when providing advice. For example, the providing unit reflects user feedback when providing advice. The generating AI improves the method of advice based on user feedback. For example, the generating AI improves the method of advice based on feedback provided by the user. The generating AI can also analyze user feedback and suggest the optimal method of advice. Furthermore, the generating AI can continuously improve the method of advice based on user feedback. In this way, the method of advice can be continuously improved by reflecting user feedback.

[0049] The providing unit can select an advice method taking into consideration the user's geographical location information when providing advice. For example, the providing unit considers the user's geographical location information when providing advice. The generation AI selects the optimal advice method based on the user's geographical location information. For example, if the user is in a specific area, advice related to that area can be provided preferentially. Also, if the user is moving, relevant advice can be provided preferentially based on the user's current location. Furthermore, if the user is in a specific location, advice related to that location can be provided preferentially. In this way, highly relevant advice can be provided by taking into consideration the user's geographical location information.

[0050] The providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the providing unit analyzes the user's social media activity when providing advice. The generation AI provides relevant advice based on the user's social media activity. For example, it prioritizes providing relevant advice based on information shared by the user on social media. It can also analyze the user's social media activity history and provide relevant advice. It can also provide relevant advice by referring to the activity of the user's friends on social media. In this way, it is possible to provide relevant advice by analyzing the user's social media activity.

[0051] The providing unit can customize the method of advice by reflecting the user's past feedback when providing advice. For example, the providing unit reflects the user's past feedback when providing advice. The generation AI customizes the method of advice based on the user's past feedback. For example, the generation AI customizes the method of advice based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and suggest the optimal method of advice. Furthermore, the generation AI can continuously improve the method of advice based on the user's feedback. In this way, the optimal method of advice can be provided by reflecting the user's past feedback.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When accepting information input by a user, the acceptance unit can suggest the optimal acceptance method based on the user's past behavior history. For example, if the user has frequently used voice input in the past, the acceptance unit can preferentially suggest voice input. Also, if the user has previously input information during a specific time period, the acceptance method can be adjusted to suit that time period. Furthermore, specific patterns can be analyzed from the user's past behavior history to automatically select the optimal acceptance method. This makes it possible to improve user convenience by suggesting the optimal acceptance method based on the user's past behavior history.

[0054] When presenting an answer generated by the generation AI to a user, the providing unit can prioritize providing relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, information related to that area can be prioritized. Also, if the user is on the move, relevant information can be prioritized based on the user's current location. Furthermore, if the user is in a specific location, information related to that location can be prioritized. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0055] When presenting the answer generated by the generation AI to the user, the providing unit can analyze the user's social media activity and provide related information. For example, it can prioritize providing related information based on information shared by the user on social media. It can also analyze the user's social media activity history and provide related information. It can also provide related information by referring to the activities of the user's friends on social media. In this way, it is possible to provide related information by analyzing the user's social media activity.

[0056] When presenting an answer generated by the generation AI to a user, the providing unit can customize the answering method by reflecting the user's past feedback. For example, the generation AI customizes the answering method based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and suggest the optimal answering method. Furthermore, the generation AI can continuously improve the answering method based on the user's feedback. In this way, the optimal answering method can be provided by reflecting the user's past feedback.

[0057] When presenting the answer generated by the generation AI to the user, the providing unit can customize the means of advice based on the user's current living situation. For example, if the user inputs their current living situation, the generation AI customizes the means of advice based on that information. The generation AI can also suggest the optimal means of advice depending on the user's living situation. Furthermore, the generation AI can continuously improve the means of advice based on the user's living situation. In this way, by customizing the means of advice based on the user's living situation, more appropriate advice can be provided.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives information from the user about their concerns, their child's physical condition, and daily life information. For example, they can input information about their child's fever, dietary concerns, sleep problems, etc. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and generate the answer the user is looking for. For example, the generation AI may generate advice on how to deal with a child's fever, how to improve diet, or how to improve sleep quality. Step 3: The provider provides appropriate advice to the user based on the answer generated by the generator. For example, the provider presents the answer generated by the AI ​​to the user and suggests specific solutions or ways to improve the situation.

[0060] (Example 2) A system according to an embodiment of the present invention allows a user to input their concerns, their child's physical information, and daily life information into a generation AI, and provides the user with the answer they are looking for each time they converse with the generation AI. This system allows users to input their concerns, their child's physical information, and daily life information, and the generation AI analyzes the input information, generates the answer the user is looking for, and provides appropriate advice. The system also functions as a subscription service, introducing products to improve the user's concerns. This allows users who are raising children to have someone they can easily consult with at any time and receive appropriate advice. Furthermore, the subscription service allows users to easily obtain products to improve their concerns, thereby reducing the burden of raising children.

[0061] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives information from the user about their concerns, their child's physical information, and daily life information. For example, information about their child's fever, dietary concerns, sleep problems, etc. can be input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate the answer the user is looking for. For example, the generation AI generates advice on how to deal with a child's fever, how to improve their diet, and how to improve sleep quality. The provision unit provides appropriate advice to the user based on the answer generated by the generation unit. For example, the provision unit presents the answer generated by the generation AI to the user and suggests specific ways to deal with or improve the situation. This allows the system according to the embodiment to provide appropriate advice to the user about their concerns.

[0062] The generation unit can use the generation AI to analyze the information input by the user and generate an answer based on past data and examples. The generation unit, for example, uses the generation AI to analyze the information input by the user. The generation AI generates the optimal answer based on past data and examples. For example, the generation AI references past user data and existing case studies to generate the optimal answer to the user's problem. This allows for more accurate advice to be provided by generating the optimal answer based on past data and examples.

[0063] The providing unit can present the answer generated by the generation AI to the user and suggest ways to deal with or improve the problem. For example, the providing unit presents the answer generated by the generation AI to the user. The generation AI suggests specific ways to deal with or improve the problem. For example, the generation AI provides specific advice on how to deal with a child's fever, how to improve diet, or how to improve sleep quality. By suggesting specific ways to deal with or improve the problem, the user can more easily find a solution to their problem.

[0064] The provision unit can introduce products to improve the user's difficulties as a subscription service. The provision unit, for example, introduces products to improve the user's difficulties as a subscription service. The generation AI suggests appropriate products based on the user's concerns and encourages them to purchase them. For example, the generation AI suggests health foods, childcare products, lifestyle improvement goods, etc. This allows the user to easily obtain appropriate products by introducing products to improve the user's concerns.

[0065] The provision unit allows the generation AI to suggest products based on the user's concerns and encourage the purchase. For example, the provision unit allows the generation AI to suggest products based on the user's concerns. The generation AI suggests appropriate products based on the user's concerns and encourages the purchase. For example, the generation AI suggests health foods, childcare products, lifestyle improvement goods, etc. This allows the user to easily obtain products that will improve their concerns by suggesting appropriate products based on their concerns.

[0066] The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. The generation AI estimates the user's emotions and adjusts the timing of receiving information based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI automatically delays the timing of receiving information and waits until the user is relaxed. Also, if the user is relaxed, the generation AI can immediately accept information and quickly start analyzing it. Furthermore, if the user is in a hurry, the generation AI can prioritize accepting information and quickly process it. In this way, by adjusting the timing of receiving information according to the user's emotions, information can be received at a more appropriate time.

[0067] The reception unit can analyze the user's past input history and select the reception method. The reception unit, for example, analyzes the user's past input history. The generation AI selects the optimal reception method based on the user's past input history. For example, it prioritizes and suggests input methods (voice, text, etc.) that the user has frequently used in the past. It can also analyze patterns of information that the user has entered in the past and automatically select the optimal reception method. Furthermore, it can also suggest the optimal reception method for a specific time period based on the user's past input history. In this way, the optimal reception method can be selected by analyzing the user's past input history.

[0068] The reception unit can filter information based on the user's living situation and areas of interest when receiving the information. For example, the reception unit filters information based on the user's living situation and areas of interest when receiving the information. The generation AI accepts only relevant information based on the user's living situation and areas of interest. For example, if the user inputs their current living situation, only relevant information is accepted based on that information. It is also possible to set the user's areas of interest in advance and prioritize accepting only information related to those areas. Furthermore, it is also possible to filter unnecessary information and accept only necessary information based on the user's living situation and areas of interest. In this way, by filtering information based on the user's living situation and areas of interest, it is possible to accept only necessary information.

[0069] The reception unit can select a reception means according to the user's input method when receiving information. For example, the reception unit selects a reception means according to the user's input method when receiving information. The generation AI selects the optimal reception means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the generation AI can accept the information using voice recognition technology. Also, if the user selects text input, the generation AI can also accept the information using text analysis technology. Furthermore, if the user selects image input, the generation AI can also accept the information using image analysis technology. This allows the optimal reception means to be selected according to the user's input method, allowing for smooth information reception.

[0070] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. The generation AI estimates the user's emotions and determines the priority of information to be received based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can prioritize receiving information of high importance. Also, if the user is relaxed, the generation AI can also accept all information equally. Furthermore, if the user is in a hurry, the generation AI can prioritize receiving information with high urgency. In this way, by determining the priority of information according to the user's emotions, important information can be received preferentially.

[0071] The reception unit can prioritize receiving relevant information in consideration of the user's geographical location information when receiving information. For example, the reception unit prioritizes receiving relevant information in consideration of the user's geographical location information when receiving information. The generation AI prioritizes receiving highly relevant information based on the user's geographical location information. For example, if the user is in a specific area, it can prioritize receiving information related to that area. Also, if the user is traveling, it can prioritize receiving related information based on the user's current location. Furthermore, if the user is in a specific location, it can prioritize receiving information related to that location. In this way, it is possible to prioritize receiving highly relevant information by considering the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity when receiving information and receive related information. For example, the reception unit analyzes the user's social media activity when receiving information. The generation AI preferentially receives related information based on the user's social media activity. For example, the generation AI preferentially receives related information based on information shared by the user on social media. The generation AI can also analyze the user's social media activity history and receive related information. Furthermore, the generation AI can also receive related information based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related information.

[0073] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving information. For example, the reception unit reflects the user's past feedback when receiving information. The generation AI proposes the optimal reception method based on the user's past feedback. For example, the generation AI proposes the optimal reception method based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and customize the reception method. Furthermore, the reception method can be continuously improved based on the user's feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0074] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions. The generation AI estimates the user's emotions and adjusts the way the answer is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can generate a concise and easy-to-understand answer. Also, if the user is relaxed, the generation AI can generate an answer that includes detailed explanations. Furthermore, if the user is in a hurry, the generation AI can generate an answer that is quickly understandable. This makes it possible to provide a more appropriate answer by adjusting the way the answer is expressed according to the user's emotions.

[0075] The generation unit can adjust the details of the answer based on the importance of the input information when generating an answer. For example, the generation unit adjusts the details of the answer based on the importance of the input information when generating an answer. The generation AI generates a detailed answer for information of high importance, and generates a concise answer for information of low importance. For example, the generation AI generates an answer including a detailed explanation for information of high importance, and generates a concise answer for information of low importance. In this way, by adjusting the detail of the answer based on the importance of the input information, it is possible to provide an answer with an appropriate level of detail.

[0076] The generation unit can apply different generation algorithms depending on the category of input information when generating an answer. For example, the generation unit applies different generation algorithms depending on the category of input information when generating an answer. The generation AI refers to a medical database for health information, and refers to an education database for education information. For example, the generation AI generates an answer by referring to a medical database for health information, and refers to an education database for education information. In this way, by applying different generation algorithms depending on the category of input information, more appropriate answers can be provided.

[0077] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit, for example, refers to the user's past answer results. The generation AI improves the accuracy of the answer based on the user's past answer results. For example, the generation AI improves the accuracy of the answer based on answers the user has received in the past. It can also analyze the user's past answer results and generate an optimal answer. Furthermore, the generation AI can improve the accuracy of the answer by referring to the user's past feedback. In this way, the accuracy of the answer can be improved by referring to the user's past answer results.

[0078] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. The generation unit, for example, estimates the user's emotions. The generation AI estimates the user's emotions and adjusts the length of the answer based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can generate a short, to-the-point answer. Also, if the user is relaxed, the generation AI can generate a longer answer that includes detailed explanations. Furthermore, if the user is in a hurry, the generation AI can generate a short answer that can be quickly understood. This makes it possible to provide more appropriate answers by adjusting the length of the answer according to the user's emotions.

[0079] The generation unit can determine the order of answers based on the submission time of the input information when generating answers. For example, the generation unit determines the order of answers based on the submission time of the input information when generating answers. The generation AI generates answers with priority for highly urgent information and generates answers with priority for information that was submitted earlier. For example, the generation AI generates answers with priority for highly urgent information and generates answers with priority for information that was submitted earlier. In this way, by determining the priority of answers based on the submission time of the input information, it is possible to respond quickly to highly urgent information.

[0080] The generation unit can adjust the order of answers based on the relevance of the input information when generating an answer. For example, the generation unit adjusts the order of answers based on the relevance of the input information when generating an answer. The generation AI generates answers preferentially for highly relevant information and generates answers later for less relevant information. For example, the generation AI generates answers preferentially for highly relevant information and generates answers later for less relevant information. In this way, by adjusting the order of answers based on the relevance of the input information, highly relevant information can be addressed preferentially.

[0081] The generation unit can change the use of technical terminology in the answer according to the user's level of expertise when generating an answer. For example, the generation unit adjusts the use of technical terminology in the answer according to the user's level of expertise when generating an answer. If the user has technical expertise, the generation AI generates an answer that uses a lot of technical terminology, and if the user does not have technical expertise, the generation AI generates an answer that explains in simple terms. For example, if the user has technical expertise, the generation AI generates an answer that uses a lot of technical terminology, and if the user does not have technical expertise, the generation AI generates an answer that explains in simple terms. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide an answer that is easy for the user to understand.

[0082] The providing unit can estimate the user's emotions and adjust the method of advice based on the estimated user emotions. The providing unit, for example, estimates the user's emotions. The generating AI estimates the user's emotions and adjusts the method of advice based on the estimated user emotions. For example, if the user is feeling stressed, the generating AI can provide concise and easy-to-understand advice. Also, if the user is relaxed, the generating AI can provide advice that includes detailed explanations. Furthermore, if the user is in a hurry, the generating AI can provide advice that is quickly understandable. In this way, more appropriate advice can be provided by adjusting the method of advice according to the user's emotions.

[0083] The providing unit can select an advice method by analyzing the user's past behavioral history when providing advice. For example, the providing unit analyzes the user's past behavioral history when providing advice. The generation AI selects the optimal advice method based on the user's past behavioral history. For example, the generation AI selects the optimal advice method based on advice the user has received in the past. The generation AI can also analyze the user's past behavioral history and select the optimal advice method. Furthermore, the generation AI can select the optimal advice method by referring to the user's past feedback. In this way, the optimal advice method can be selected by analyzing the user's past behavioral history.

[0084] The providing unit can customize the means of advice based on the user's current living situation when providing advice. For example, the providing unit customizes the means of advice based on the user's current living situation when providing advice. The generation AI suggests the optimal means of advice according to the user's living situation. For example, if the user inputs their current living situation, the generation AI customizes the means of advice based on that information. The generation AI can also suggest the optimal means of advice according to the user's living situation. Furthermore, the generation AI can continuously improve the means of advice based on the user's living situation. In this way, more appropriate advice can be provided by customizing the means of advice based on the user's living situation.

[0085] The providing unit can improve the method of advice by reflecting user feedback when providing advice. For example, the providing unit reflects user feedback when providing advice. The generating AI improves the method of advice based on user feedback. For example, the generating AI improves the method of advice based on feedback provided by the user. The generating AI can also analyze user feedback and suggest the optimal method of advice. Furthermore, the generating AI can continuously improve the method of advice based on user feedback. In this way, the method of advice can be continuously improved by reflecting user feedback.

[0086] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. The generation AI estimates the user's emotions and determines the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can prioritize providing advice with high importance. Also, if the user is relaxed, the generation AI can provide all advice equally. Furthermore, if the user is in a hurry, the generation AI can prioritize providing advice with high urgency. In this way, by determining the priority of advice according to the user's emotions, important advice can be provided preferentially.

[0087] The providing unit can select an advice method taking into consideration the user's geographical location information when providing advice. For example, the providing unit considers the user's geographical location information when providing advice. The generation AI selects the optimal advice method based on the user's geographical location information. For example, if the user is in a specific area, advice related to that area can be provided preferentially. Also, if the user is moving, relevant advice can be provided preferentially based on the user's current location. Furthermore, if the user is in a specific location, advice related to that location can be provided preferentially. In this way, highly relevant advice can be provided by taking into consideration the user's geographical location information.

[0088] The providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the providing unit analyzes the user's social media activity when providing advice. The generation AI provides relevant advice based on the user's social media activity. For example, it prioritizes providing relevant advice based on information shared by the user on social media. It can also analyze the user's social media activity history and provide relevant advice. It can also provide relevant advice by referring to the activity of the user's friends on social media. In this way, it is possible to provide relevant advice by analyzing the user's social media activity.

[0089] The providing unit can customize the method of advice by reflecting the user's past feedback when providing advice. For example, the providing unit reflects the user's past feedback when providing advice. The generation AI customizes the method of advice based on the user's past feedback. For example, the generation AI customizes the method of advice based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and suggest the optimal method of advice. Furthermore, the generation AI can continuously improve the method of advice based on the user's feedback. In this way, the optimal method of advice can be provided by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives from the user information about the user's problems, physical information about the child, and daily life information using the touch panel 38A or microphone 38B of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using a generation AI to generate the answer the user is looking for. The provision unit presents the generated answer to the user using, for example, the display 40A or speaker 40B of the smart device 14, and suggests specific solutions or improvement methods. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives from the user information about the user's problems, physical information about the child, and daily life information using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using a generation AI to generate the answer the user is looking for. The provision unit presents the generated answer to the user using, for example, the speaker 240 of the smart glasses 214, and suggests specific solutions or improvement methods. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives from the user information about the user's problems, physical information about the child, and daily life information using the microphone 238 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using a generation AI to generate the answer the user is looking for. The provision unit presents the generated answer to the user using, for example, the speaker 240 of the headset-type terminal 314, and suggests specific solutions and improvement methods. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives from the user information about the user's problems, physical information about the child, and daily life information using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using a generation AI to generate an answer that the user is looking for. The provision unit presents the generated answer to the user using, for example, the speaker 240 of the robot 414, and suggests specific solutions and improvement methods.

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

[0091] When accepting information input by a user, the acceptance unit can suggest the optimal acceptance method based on the user's past behavior history. For example, if the user has frequently used voice input in the past, the acceptance unit can preferentially suggest voice input. Also, if the user has previously input information during a specific time period, the acceptance method can be adjusted to suit that time period. Furthermore, specific patterns can be analyzed from the user's past behavior history to automatically select the optimal acceptance method. This makes it possible to improve user convenience by suggesting the optimal acceptance method based on the user's past behavior history.

[0092] When analyzing the user's input information, the generation unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling stressed, the generation AI will increase the accuracy of the analysis and quickly generate an answer. Also, if the user is relaxed, the generation AI can perform a detailed analysis and generate a more accurate answer. Furthermore, if the user is in a hurry, the generation AI can perform a concise and quick analysis and generate an answer in a short amount of time. This allows the system to provide more appropriate answers by adjusting the accuracy of the analysis according to the user's emotions.

[0093] When presenting an answer generated by the generation AI to a user, the providing unit can prioritize providing relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, information related to that area can be prioritized. Also, if the user is on the move, relevant information can be prioritized based on the user's current location. Furthermore, if the user is in a specific location, information related to that location can be prioritized. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0094] When presenting the answer generated by the generation AI to the user, the providing unit can analyze the user's social media activity and provide related information. For example, it can prioritize providing related information based on information shared by the user on social media. It can also analyze the user's social media activity history and provide related information. It can also provide related information by referring to the activities of the user's friends on social media. In this way, it is possible to provide related information by analyzing the user's social media activity.

[0095] When presenting an answer generated by the generation AI to a user, the providing unit can customize the answering method by reflecting the user's past feedback. For example, the generation AI customizes the answering method based on feedback provided by the user in the past. The generation AI can also analyze the user's past feedback and suggest the optimal answering method. Furthermore, the generation AI can continuously improve the answering method based on the user's feedback. In this way, the optimal answering method can be provided by reflecting the user's past feedback.

[0096] The reception unit can estimate the user's emotions and adjust the timing of receiving information based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can automatically delay the timing of receiving information and wait until the user is relaxed. Also, if the user is relaxed, the generation AI can immediately receive information and begin analyzing it quickly. Furthermore, if the user is in a hurry, the generation AI can prioritize receiving information and process it quickly. In this way, by adjusting the timing of receiving information according to the user's emotions, information can be received at a more appropriate time.

[0097] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated emotions. For example, if the user is feeling stressed, the generation AI can generate a concise and easy-to-understand answer. Alternatively, if the user is relaxed, the generation AI can generate an answer that includes detailed explanations. Furthermore, if the user is in a hurry, the generation AI can generate an answer that is quickly understandable. This allows the system to provide more appropriate answers by adjusting the way the answer is expressed according to the user's emotions.

[0098] The providing unit can estimate the user's emotions and adjust the method of advice based on the estimated emotions. For example, if the user is feeling stressed, the generating AI can provide concise and easy-to-understand advice. Alternatively, if the user is relaxed, the generating AI can provide advice that includes detailed explanations. Furthermore, if the user is in a hurry, the generating AI can provide advice that can be quickly understood. This allows the providing of more appropriate advice by adjusting the method of advice according to the user's emotions.

[0099] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the generating AI can prioritize providing advice with a high level of importance. Also, if the user is relaxed, the generating AI can provide all advice equally. Furthermore, if the user is in a hurry, the generating AI can prioritize providing advice with a high level of urgency. In this way, by determining the priority of advice according to the user's emotions, important advice can be provided preferentially.

[0100] When presenting the answer generated by the generation AI to the user, the providing unit can customize the means of advice based on the user's current living situation. For example, if the user inputs their current living situation, the generation AI customizes the means of advice based on that information. The generation AI can also suggest the optimal means of advice depending on the user's living situation. Furthermore, the generation AI can continuously improve the means of advice based on the user's living situation. In this way, by customizing the means of advice based on the user's living situation, more appropriate advice can be provided.

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

[0102] Step 1: The reception unit receives information from the user about their concerns, their child's physical condition, and daily life information. For example, they can input information about their child's fever, dietary concerns, sleep problems, etc. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and generate the answer the user is looking for. For example, the generation AI may generate advice on how to deal with a child's fever, how to improve diet, or how to improve sleep quality. Step 3: The provider provides appropriate advice to the user based on the answer generated by the generator. For example, the provider presents the answer generated by the AI ​​to the user and suggests specific solutions or ways to improve the situation.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

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

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

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

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

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

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

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

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about difficulties, physical conditions of the child, and daily life information from the user; a generating unit that analyzes the information received by the receiving unit and generates an answer that the user is looking for; a providing unit that provides advice to the user based on the answer generated by the generating unit. A system characterized by:

2. The generation unit Generative AI analyzes user input and generates answers based on past data and case studies.

2. The system of claim 1.

3. The providing unit The AI ​​generates answers that are presented to the user, suggesting solutions and ways to improve the situation.

2. The system of claim 1.

4. The providing unit As a subscription service, it introduces products to improve users' difficulties.

2. The system of claim 1.

5. The providing unit Generative AI suggests products based on the user's concerns and encourages purchases 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of information reception based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the reception method 2. The system of claim 1.

8. The reception unit When receiving information, filter it based on the user's lifestyle and interests.

2. The system of claim 1.

Images