system

Abstract

The system of the embodiment aims to provide an environment where people can easily consult about concerns about child-rearing and pregnancy, and to eliminate disparities in child-rearing support services among local governments. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, and an API unit. The reception unit receives questions from users. The generation unit analyzes the questions received by the reception unit and generates answers. The provision unit provides the answers generated by the generation unit to users. The API unit converts the services provided by the provision unit into an API and links to child-rearing support sites of each local government.

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 technology did not provide an environment where people could easily seek advice about concerns about childcare or pregnancy, and there were also issues such as disparities in childcare support services offered by each local government.

[0005] The system of the embodiment aims to provide an environment where people can easily consult about concerns about child-rearing and pregnancy, and to eliminate disparities in child-rearing support services among local governments. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and an API unit. The reception unit receives questions from users. The generation unit analyzes the questions received by the reception unit and generates answers. The provision unit provides the answers generated by the generation unit to users. The API unit converts the services provided by the provision unit into APIs and links to child-rearing support sites of each local government. [Effects of the Invention]

[0007] The system according to the embodiment provides an environment where people can easily consult about concerns about child-rearing and pregnancy, and can eliminate disparities in child-rearing support services offered by each local government. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The childcare and pregnancy consultation system according to an embodiment of the present invention is a system that provides a service that allows users to consult about their concerns regarding childcare and pregnancy using a generative AI. In this system, users input questions about childcare and pregnancy through a messaging app (for example, LINE®), the generative AI analyzes the questions, generates appropriate answers, and provides them to the users. Furthermore, by making this service an API and integrating it into the childcare support sites of each local government, disparities in service provision can be eliminated. For example, when a user inputs questions such as "How to deal with a baby that won't stop crying in the middle of the night" or "Advice on diet during pregnancy" through a messaging app, the generative AI analyzes the questions, generates appropriate answers, and provides them to the users. This allows users to easily consult about their concerns regarding childcare and pregnancy regardless of the time. In addition, by making this service an API and integrating it into the childcare support sites of each local government, the same level of support can be provided in any local government. For example, if one local government has extensive support from experts while another local government lacks sufficient support, this API can be used to provide the same level of support in any local government. This will allow the childcare and pregnancy consultation system to allow users to easily consult about childcare and pregnancy-related concerns regardless of the time, and will eliminate disparities in the services provided by childcare support websites in each municipality.

[0029] The childcare and pregnancy consultation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and an API unit. The reception unit receives questions entered by the user through a messaging application. Questions entered by the user include, but are not limited to, examples such as "how to deal with a baby that won't stop crying in the middle of the night" or "advice on diet during pregnancy." The reception unit also accepts questions entered by the user through a messaging application. For example, the reception unit can accept questions entered by the user using voice input. For example, the user can enter a question by voice, and the voice data can be converted into text data and accepted. The generation unit uses a generation AI to analyze the questions received by the reception unit and generate appropriate answers. The generation AI uses, for example, natural language processing techniques such as GPT-3 or BERT to analyze the questions and generates answers based on relevant information. For example, the generation AI proposes several specific solutions for "how to deal with a baby that won't stop crying in the middle of the night." The generation unit can also use the generation AI to apply different generation algorithms depending on the content of the question. For example, a specialized generation algorithm for childcare is applied to questions about childcare, and a specialized generation algorithm for pregnancy is applied to questions about pregnancy. The provider unit provides the user with the answers generated by the generation unit. The provider unit provides the user with the generated answers, for example, through a messaging app. The provider unit can also provide the generated answers in audio format. For example, the generated answers can be converted into audio data and provided to the user. Furthermore, the provider unit can estimate the user's emotional state and adjust the way the answers are provided based on the estimated emotional state. For example, if the user is stressed, the answer will be provided in a concise and easy-to-understand format, and if the user is relaxed, the answer will be provided in a format that includes detailed explanations. The API unit turns the services provided by the provider unit into APIs and links them to each local government's childcare support website. The API unit turns the services into APIs using technologies such as REST APIs and SOAP APIs and integrates them into each local government's childcare support website. This eliminates disparities in the services provided by each local government's childcare support website.For example, if one local government has ample support from experts while another does not, by using this API, it is possible to provide the same level of support in all local governments. As a result, the childcare and pregnancy consultation system according to the embodiment allows users to easily consult about concerns about childcare and pregnancy regardless of the time, and eliminates disparities in the provision of services on childcare support sites by each local government.

[0030] The reception unit can receive questions entered by a user through a messaging app. The reception unit, for example, receives questions entered by a user through a messaging app. Questions entered by a user include, but are not limited to, "What to do if my baby won't stop crying in the middle of the night" and "Dietary advice during pregnancy." The reception unit, for example, receives questions entered by a user through a messaging app. The reception unit can also receive questions entered by a user using voice input. For example, the user can input a question by voice, and the voice data can be converted into text data and accepted. This allows the user to easily enter a question through a messaging app. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input a question entered by a user to a generation AI and have the generation AI analyze the question.

[0031] The generation unit can analyze the input question using the generation AI and generate an appropriate answer. The generation unit can analyze the question received by the reception unit using the generation AI and generate an appropriate answer. The generation AI can analyze the question using natural language processing technology such as GPT-3 or BERT and generate an answer based on related information. For example, the generation AI can suggest several specific solutions to the question, "What to do if your baby won't stop crying in the middle of the night." The generation unit can also use the generation AI to apply different generation algorithms depending on the content of the question. For example, a generation algorithm specialized for childcare can be applied to a question about childcare, and a generation algorithm specialized for pregnancy can be applied to a question about pregnancy. In this way, the generation AI can generate an appropriate answer to the question. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input a question entered by a user into the generation AI and have the generation AI analyze the question and generate an answer.

[0032] The providing unit can provide the generated answer to the user. The providing unit provides the answer generated by the generating unit to the user. The providing unit can provide the generated answer to the user, for example, through a messaging app. The providing unit can also provide the generated answer in audio format. For example, the providing unit can convert the generated answer into audio data and provide it to the user. The providing unit can also estimate the user's emotional state and adjust the method of providing the answer based on the estimated emotional state. For example, if the user is feeling stressed, the providing unit can provide the answer in a concise and easy-to-understand format, and if the user is relaxed, the providing unit can provide the answer in a format including detailed explanations. This allows the user to obtain appropriate information by providing the generated answer to the user. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the answer generated by the generating unit to the generation AI and cause the generation AI to execute the answer providing method.

[0033] The API unit can link with each local government's child-rearing support site to provide services. The API unit converts the services provided by the provider unit into APIs and links them to each local government's child-rearing support site. The API unit converts the services into APIs using technologies such as REST API or SOAP API and integrates them into each local government's child-rearing support site. This eliminates disparities in the services provided by each local government's child-rearing support site. For example, if one local government offers extensive expert support while another does not, using this API can provide the same level of support in all local governments. This eliminates disparities in the services provided by each local government's child-rearing support site. Some or all of the above-mentioned processing in the API unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the API unit can input the services provided by the provider unit into a generation AI and have the generation AI execute the API conversion process.

[0034] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. The reception unit can also predict and suggest question formats to be used in specific time periods based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history into the generation AI and have the generation AI select the optimal reception method.

[0035] When receiving a question, the reception unit can filter the questions based on the user's current living situation or areas of interest. For example, if the user is raising a child, the reception unit can prioritize receiving questions related to childcare. Furthermore, if the user is pregnant, the reception unit can also prioritize receiving questions related to pregnancy. Furthermore, the reception unit can filter and receive related questions based on the user's areas of interest. In this way, by filtering questions based on the user's living situation or areas of interest, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation or areas of interest into the generation AI and have the generation AI perform question filtering.

[0036] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and have the generation AI prioritize highly relevant questions.

[0037] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, if the user posts about childcare on social media, the reception desk will prioritize accepting questions related to childcare. Similarly, if the user posts about pregnancy on social media, the reception desk can prioritize accepting questions related to pregnancy. Furthermore, the reception desk can filter questions related to the user's areas of interest from their social media activity. This allows for the prioritization of relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input data on the user's social media activity into a generative AI and have the generative AI perform the filtering of relevant questions.

[0038] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate a detailed answer for a high-importance question. It can also generate a concise answer for a low-importance question. Furthermore, the generation unit can adjust the level of detail in the answer in stages according to the importance of the question. This allows for the provision of appropriate answers by adjusting the level of detail in the answer according to the importance of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the question into the generation AI and have the generation AI perform the adjustment of the level of detail in the answer.

[0039] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can apply a generation algorithm specialized for childcare to a question about childcare. The generation unit can also apply a generation algorithm specialized for pregnancy to a question about pregnancy. The generation unit can also select an optimal generation algorithm depending on the category of the question and generate an answer. This makes it possible to provide an appropriate answer by applying the optimal generation algorithm depending on the category of the question. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the question category into the generation AI and have the generation AI select the optimal generation algorithm and generate an answer.

[0040] When generating an answer, the generation unit can determine the priority of the answer based on the time the question was submitted. For example, if a question is submitted late at night, the generation unit can generate an answer quickly. Furthermore, if a question is submitted during the daytime on a weekday, the generation unit can generate an answer with normal priority. Furthermore, if a question is submitted on a weekend, the generation unit can generate an answer with weekend priority. In this way, by determining the priority of answers based on the time the question was submitted, answers can be provided at an appropriate time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the time the question was submitted to the generation AI and have the generation AI determine the priority of the answers.

[0041] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, if a question is highly relevant to other questions, the generation unit prioritizes answers to related questions. Furthermore, if questions are independent, the generation unit can also generate answers in a normal order. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, highly relevant answers can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of questions to the generation AI and have the generation AI adjust the order of answers.

[0042] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past question history. For example, the providing unit can preferentially provide a providing method (text, voice, etc.) that the user has used in the past. The providing unit can also predict and suggest a specific providing method from the user's past question history. The providing unit can also analyze the user's past question history and select the optimal providing method. In this way, the optimal providing method can be selected by referring to the user's past question history. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's past question history into the generation AI and cause the generation AI to select the optimal providing method.

[0043] The information provider can customize the means of providing information based on the user's current living situation when providing responses. For example, if the user is raising children, the information provider will prioritize providing information related to childcare. Similarly, if the user is pregnant, the information provider can prioritize providing information related to pregnancy. Furthermore, the information provider can customize the most suitable means of providing information based on the user's living situation. This allows for the provision of appropriate information by customizing the means of providing information according to the user's living situation. Some or all of the above-described processes in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input data on the user's living situation into a generative AI and have the generative AI perform the customization of the means of providing information.

[0044] The information delivery unit can select the optimal delivery method when providing responses, taking into account the user's geographical location information. For example, if the user is in a specific region, the information delivery unit can prioritize providing information related to that region. Furthermore, if the user is traveling, the information delivery unit can prioritize providing information related to their travel destination. Also, if the user is at home, the information delivery unit can prioritize providing information related to their home area. This allows for the provision of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information delivery unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0045] The information provider can analyze the user's social media activity and propose information delivery methods when providing responses. For example, if the user posts about childcare on social media, the information provider will prioritize providing information related to childcare. Similarly, if the user posts about pregnancy on social media, the information provider can prioritize providing information related to pregnancy. Furthermore, the information provider can filter and provide information related to the user's areas of interest from their social media activity. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input data on the user's social media activity into a generative AI and have the generative AI propose information delivery methods.

[0046] The API unit can select the optimal API delivery method by referring to past usage data of each local government when providing APIs. For example, the API unit can select the optimal API delivery method based on past usage data of each local government. The API unit can also predict and propose API delivery methods to be used during specific time periods based on usage data of each local government. Furthermore, the API unit can analyze past usage data of each local government and select the most efficient API delivery method. In this way, the optimal API delivery method can be selected by referring to past usage data of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or without a generative AI. For example, the API unit can input past usage data of each local government into a generative AI and have the generative AI select the optimal API delivery method.

[0047] The API unit can customize the means of providing APIs based on the characteristics of each local government. For example, the API unit can customize the optimal API provision method based on the characteristics of each local government. The API unit can also adjust the API provision method according to the population composition of each local government. Furthermore, the API unit can customize the API provision method based on the geographical characteristics of each local government. In this way, appropriate APIs can be provided by customizing the provision method based on the characteristics of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the API unit can input the characteristics of each local government into a generative AI and have the generative AI perform the customization of the provision method.

[0048] The API unit can select the optimal API delivery method by considering the geographical location information of each local government when providing APIs. For example, the API unit can select the optimal API delivery method based on the geographical location information of each local government. The API unit can also adjust the API delivery method according to the geographical characteristics of each local government. Furthermore, the API unit can analyze the geographical location information of each local government and select the most efficient API delivery method. This allows for the provision of highly relevant APIs by considering the geographical location information of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or without a generative AI. For example, the API unit can input the geographical location information of each local government into a generative AI and have the generative AI select the optimal delivery method.

[0049] When providing the API, the API unit can analyze the social media activity of each local government and propose a means of delivery. For example, the API unit can propose the optimal means of API delivery based on the social media activity of each local government. The API unit can also predict and propose specific means of delivery from the social media activity of each local government. The API unit can also analyze the social media activity of each local government and propose the most efficient means of API delivery. This makes it possible to provide highly relevant APIs by analyzing the social media activity of each local government. Some or all of the above-mentioned processing in the API unit may be performed using, or without, a generation AI. For example, the API unit can input data on the social media activity of each local government into the generation AI and have the generation AI execute the proposal of delivery means.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's past question history and select the optimal reception method. For example, it can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest question formats to be used during specific time periods based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history into the generation AI and have the generation AI select the optimal reception method.

[0052] The service provider can select the optimal delivery method by referring to the user's past question history when providing answers. For example, it may prioritize delivery methods that the user has previously preferred (text, audio, etc.). The service provider can also predict and suggest specific delivery methods based on the user's past question history. Furthermore, the service provider can analyze the user's past question history and select the optimal delivery method. This allows the service provider to select the optimal delivery method by referring to the user's past question history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past question history into a generative AI and have the generative AI select the optimal delivery method.

[0053] The API unit can select the optimal API delivery method by referring to past usage data of each local government when providing APIs. For example, it can select the optimal API delivery method based on past usage data of each local government. The API unit can also predict and propose API delivery methods to be used during specific time periods based on usage data of each local government. Furthermore, the API unit can analyze past usage data of each local government and select the most efficient API delivery method. In this way, the optimal API delivery method can be selected by referring to past usage data of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the API unit can input past usage data of each local government into a generative AI and have the generative AI select the optimal API delivery method.

[0054] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, a detailed answer is generated for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can gradually adjust the level of detail of the answer depending on the importance of the question. This makes it possible to provide an appropriate answer by adjusting the level of detail of the answer depending on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the question to the generation AI and have the generation AI adjust the level of detail of the answer.

[0055] When providing an API, the API unit can customize the delivery means based on the characteristics of each local government. For example, it customizes the optimal API delivery means based on the characteristics of each local government. The API unit can also adjust the API delivery means according to the population composition of each local government. Furthermore, the API unit can also customize the API delivery means based on the geographical characteristics of each local government. This allows for the provision of an appropriate API by customizing the delivery means based on the characteristics of each local government. Some or all of the above-mentioned processing in the API unit may be performed using, or without, a generation AI. For example, the API unit can input the characteristics of each local government into the generation AI and have the generation AI customize the delivery means.

[0056] When receiving a question, the reception unit can filter the questions based on the user's current living situation or areas of interest. For example, if the user is raising a child, the reception unit can prioritize receiving questions related to childcare. Also, if the user is pregnant, the reception unit can prioritize receiving questions related to pregnancy. Furthermore, the reception unit can filter and receive related questions based on the user's areas of interest. In this way, by filtering questions based on the user's living situation or areas of interest, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation or areas of interest into the generation AI and have the generation AI perform question filtering.

[0057] When providing an answer, the providing unit can select the optimal providing method 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 provided preferentially. Furthermore, if the user is traveling, the providing unit can also provide preferentially information related to the travel destination. Furthermore, if the user is at home, the providing unit can also provide preferentially information related to the area around the user's home. In this way, highly relevant information can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal providing method.

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

[0059] Step 1: The reception unit accepts questions entered by the user through a messaging app. Questions entered by the user include "What to do if my baby won't stop crying in the middle of the night" and "Advice on diet during pregnancy." The reception unit can also accept questions via voice input, converting the voice data into text data and accepting it. Step 2: The generation unit uses a generation AI to analyze the question received by the reception unit and generate an appropriate answer. The generation AI analyzes the question using natural language processing techniques such as GPT-3 and BERT, and generates an answer based on related information. Different generation algorithms can be applied depending on the content of the question. Step 3: The providing unit provides the answer generated by the generating unit to the user. The providing unit may provide the generated answer to the user through a messaging app or may provide the generated answer in audio format. Furthermore, the providing unit estimates the user's emotional state and adjusts the method of providing the answer based on the estimated emotional state. Step 4: The API department turns the services provided by the provider department into APIs and connects them to each local government's child-rearing support site. The API department turns the services into APIs using technologies such as REST API and SOAP API, and integrates them into each local government's child-rearing support site. This eliminates disparities in the provision of services on each local government's child-rearing support site.

[0060] (Example 2) A childcare and pregnancy consultation system according to an embodiment of the present invention utilizes a generation AI to provide a service that allows users to seek advice on concerns about childcare and pregnancy. In this system, users input questions about childcare and pregnancy through a messaging app (e.g., LINE), and a generation AI analyzes the questions, generates appropriate answers, and provides them to the user. Furthermore, by converting this service into an API and integrating it into each local government's childcare support website, disparities in service provision can be eliminated. For example, when a user inputs a question such as "What to do if my baby won't stop crying in the middle of the night" or "Advice on diet during pregnancy" through a messaging app, the generation AI analyzes the question, generates appropriate answers, and provides them to the user. This allows users to easily consult about concerns about childcare and pregnancy at any time. Furthermore, by converting this service into an API and integrating it into each local government's childcare support website, the same level of support can be provided across all local governments. For example, if one local government offers extensive expert support while another lacks it, using this API can provide the same level of support across all local governments. This allows users to easily consult about concerns about childcare and pregnancy at any time, eliminating disparities in service provision across local government childcare support websites.

[0061] The childcare and pregnancy consultation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and an API unit. The reception unit receives questions entered by the user through a messaging application. Questions entered by the user include, but are not limited to, examples such as "how to deal with a baby that won't stop crying in the middle of the night" or "advice on diet during pregnancy." The reception unit also accepts questions entered by the user through a messaging application. For example, the reception unit can accept questions entered by the user using voice input. For example, the user can enter a question by voice, and the voice data can be converted into text data and accepted. The generation unit uses a generation AI to analyze the questions received by the reception unit and generate appropriate answers. The generation AI uses, for example, natural language processing techniques such as GPT-3 or BERT to analyze the questions and generates answers based on relevant information. For example, the generation AI proposes several specific solutions for "how to deal with a baby that won't stop crying in the middle of the night." The generation unit can also use the generation AI to apply different generation algorithms depending on the content of the question. For example, a specialized generation algorithm for childcare is applied to questions about childcare, and a specialized generation algorithm for pregnancy is applied to questions about pregnancy. The provider unit provides the user with the answers generated by the generation unit. The provider unit provides the user with the generated answers, for example, through a messaging app. The provider unit can also provide the generated answers in audio format. For example, the generated answers can be converted into audio data and provided to the user. Furthermore, the provider unit can estimate the user's emotional state and adjust the way the answers are provided based on the estimated emotional state. For example, if the user is stressed, the answer will be provided in a concise and easy-to-understand format, and if the user is relaxed, the answer will be provided in a format that includes detailed explanations. The API unit turns the services provided by the provider unit into APIs and links them to each local government's childcare support website. The API unit turns the services into APIs using technologies such as REST APIs and SOAP APIs and integrates them into each local government's childcare support website. This eliminates disparities in the services provided by each local government's childcare support website.For example, if one local government has ample support from experts while another does not, by using this API, it is possible to provide the same level of support in all local governments. As a result, the childcare and pregnancy consultation system according to the embodiment allows users to easily consult about concerns about childcare and pregnancy regardless of the time, and eliminates disparities in the provision of services on childcare support sites by each local government.

[0062] The reception unit can receive questions entered by a user through a messaging app. The reception unit, for example, receives questions entered by a user through a messaging app. Questions entered by a user include, but are not limited to, "What to do if my baby won't stop crying in the middle of the night" and "Dietary advice during pregnancy." The reception unit, for example, receives questions entered by a user through a messaging app. The reception unit can also receive questions entered by a user using voice input. For example, the user can input a question by voice, and the voice data can be converted into text data and accepted. This allows the user to easily enter a question through a messaging app. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input a question entered by a user to a generation AI and have the generation AI analyze the question.

[0063] The generation unit can analyze the input question using the generation AI and generate an appropriate answer. The generation unit can analyze the question received by the reception unit using the generation AI and generate an appropriate answer. The generation AI can analyze the question using natural language processing technology such as GPT-3 or BERT and generate an answer based on related information. For example, the generation AI can suggest several specific solutions to the question, "What to do if your baby won't stop crying in the middle of the night." The generation unit can also use the generation AI to apply different generation algorithms depending on the content of the question. For example, a generation algorithm specialized for childcare can be applied to a question about childcare, and a generation algorithm specialized for pregnancy can be applied to a question about pregnancy. In this way, the generation AI can generate an appropriate answer to the question. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input a question entered by a user into the generation AI and have the generation AI analyze the question and generate an answer.

[0064] The providing unit can provide the generated answer to the user. The providing unit provides the answer generated by the generating unit to the user. The providing unit can provide the generated answer to the user, for example, through a messaging app. The providing unit can also provide the generated answer in audio format. For example, the providing unit can convert the generated answer into audio data and provide it to the user. The providing unit can also estimate the user's emotional state and adjust the method of providing the answer based on the estimated emotional state. For example, if the user is feeling stressed, the providing unit can provide the answer in a concise and easy-to-understand format, and if the user is relaxed, the providing unit can provide the answer in a format including detailed explanations. This allows the user to obtain appropriate information by providing the generated answer to the user. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the answer generated by the generating unit to the generation AI and cause the generation AI to execute the answer providing method.

[0065] The API unit can link with each local government's child-rearing support site to provide services. The API unit converts the services provided by the provider unit into APIs and links them to each local government's child-rearing support site. The API unit converts the services into APIs using technologies such as REST API or SOAP API and integrates them into each local government's child-rearing support site. This eliminates disparities in the services provided by each local government's child-rearing support site. For example, if one local government offers extensive expert support while another does not, using this API can provide the same level of support in all local governments. This eliminates disparities in the services provided by each local government's child-rearing support site. Some or all of the above-mentioned processing in the API unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the API unit can input the services provided by the provider unit into a generation AI and have the generation AI execute the API conversion process.

[0066] The reception unit can estimate the user's emotional state and adjust the timing of question reception based on the estimated emotional state. For example, if the user is feeling stressed, the reception unit can immediately accept the question and provide a prompt response. Furthermore, if the user is relaxed, the reception unit can slightly delay the reception of the question to allow the user time to enter detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simple question input interface and quickly accept the question. This allows the timing of question reception to be adjusted according to the user's emotions, thereby allowing questions to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's emotional state into the generation AI and have the generation AI perform emotion estimation and adjust the timing of question reception.

[0067] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. The reception unit can also predict and suggest question formats to be used in specific time periods based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history into the generation AI and have the generation AI select the optimal reception method.

[0068] When receiving a question, the reception unit can filter the questions based on the user's current living situation or areas of interest. For example, if the user is raising a child, the reception unit can prioritize receiving questions related to childcare. Furthermore, if the user is pregnant, the reception unit can also prioritize receiving questions related to pregnancy. Furthermore, the reception unit can filter and receive related questions based on the user's areas of interest. In this way, by filtering questions based on the user's living situation or areas of interest, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation or areas of interest into the generation AI and have the generation AI perform question filtering.

[0069] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize urgent questions. Furthermore, when the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize concise questions. By determining the priority of questions according to the user's emotions, more appropriate questions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotional state into the generation AI and have the generation AI perform emotion estimation and question priority determination.

[0070] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions related to the area around the user's home. In this way, by taking the user's geographical location information into consideration, highly relevant questions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and have the generation AI prioritize highly relevant questions.

[0071] The reception desk can analyze the user's social media activity when receiving questions and accept relevant questions. For example, if the user posts about childcare on social media, the reception desk will prioritize accepting questions related to childcare. Similarly, if the user posts about pregnancy on social media, the reception desk can prioritize accepting questions related to pregnancy. Furthermore, the reception desk can filter questions related to the user's areas of interest from their social media activity. This allows for the prioritization of relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without one. For example, the reception desk can input data on the user's social media activity into a generative AI and have the generative AI perform the filtering of relevant questions.

[0072] The generation unit can estimate the user's emotional state and adjust the way the response is expressed based on the estimated emotional state. For example, if the user is stressed, the generation unit will generate a concise and easy-to-understand response. If the user is relaxed, the generation unit can also generate a response that includes detailed explanations. If the user is in a hurry, the generation unit can also generate a concise and quick response. This allows for the provision of more appropriate responses by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input the user's emotional state into a generative AI and have the generative AI perform emotion estimation and adjustment of the response expression.

[0073] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit can generate a detailed answer for a high-importance question. It can also generate a concise answer for a low-importance question. Furthermore, the generation unit can adjust the level of detail in the answer in stages according to the importance of the question. This allows for the provision of appropriate answers by adjusting the level of detail in the answer according to the importance of the question. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the question into the generation AI and have the generation AI perform the adjustment of the level of detail in the answer.

[0074] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can apply a generation algorithm specialized for childcare to a question about childcare. The generation unit can also apply a generation algorithm specialized for pregnancy to a question about pregnancy. The generation unit can also select an optimal generation algorithm depending on the category of the question and generate an answer. This makes it possible to provide an appropriate answer by applying the optimal generation algorithm depending on the category of the question. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the question category into the generation AI and have the generation AI select the optimal generation algorithm and generate an answer.

[0075] The generation unit can estimate the user's emotional state and adjust the length of the response based on the estimated emotional state. For example, if the user is stressed, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can also generate a longer response with more detailed explanations. If the user is in a hurry, the generation unit can generate a brief and quick response. This allows for the provision of appropriate responses by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input the user's emotional state into a generative AI and have the generative AI perform emotion estimation and response length adjustment.

[0076] When generating an answer, the generation unit can determine the priority of the answer based on the time the question was submitted. For example, if a question is submitted late at night, the generation unit can generate an answer quickly. Furthermore, if a question is submitted during the daytime on a weekday, the generation unit can generate an answer with normal priority. Furthermore, if a question is submitted on a weekend, the generation unit can generate an answer with weekend priority. In this way, by determining the priority of answers based on the time the question was submitted, answers can be provided at an appropriate time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the time the question was submitted to the generation AI and have the generation AI determine the priority of the answers.

[0077] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, if a question is highly relevant to other questions, the generation unit prioritizes answers to related questions. Furthermore, if questions are independent, the generation unit can also generate answers in a normal order. Furthermore, the generation unit can dynamically adjust the order of answers according to the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, highly relevant answers can be provided preferentially. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the relevance of questions to the generation AI and have the generation AI adjust the order of answers.

[0078] The service provider can estimate the user's emotional state and adjust the method of providing the response based on the estimated emotional state. For example, if the user is stressed, the service provider can provide a concise and easy-to-understand response. If the user is relaxed, the service provider can also provide a response that includes a detailed explanation. If the user is in a hurry, the service provider can also provide a quick response. In this way, by adjusting the method of providing the response according to the user's emotions, the service provider can provide a response in an appropriate format. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input the user's emotional state into a generative AI and have the generative AI perform emotion estimation and adjustment of the response method.

[0079] When providing an answer, the providing unit can select the optimal providing method by referring to the user's past question history. For example, the providing unit can preferentially provide a providing method (text, voice, etc.) that the user has used in the past. The providing unit can also predict and suggest a specific providing method from the user's past question history. The providing unit can also analyze the user's past question history and select the optimal providing method. In this way, the optimal providing method can be selected by referring to the user's past question history. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's past question history into the generation AI and cause the generation AI to select the optimal providing method.

[0080] The information provider can customize the means of providing information based on the user's current living situation when providing responses. For example, if the user is raising children, the information provider will prioritize providing information related to childcare. Similarly, if the user is pregnant, the information provider can prioritize providing information related to pregnancy. Furthermore, the information provider can customize the most suitable means of providing information based on the user's living situation. This allows for the provision of appropriate information by customizing the means of providing information according to the user's living situation. Some or all of the above-described processes in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input data on the user's living situation into a generative AI and have the generative AI perform the customization of the means of providing information.

[0081] The service provider can estimate the user's emotional state and determine the priority of the answers to be provided based on the estimated emotional state. For example, if the user is stressed, the service provider will prioritize providing answers that are of high urgency. If the user is relaxed, the service provider may also prioritize providing detailed answers. If the user is in a hurry, the service provider may also prioritize providing concise answers. In this way, by prioritizing answers according to the user's emotions, appropriate answers can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input the user's emotional state into a generative AI and have the generative AI perform emotion estimation and determine the priority of answers.

[0082] The information delivery unit can select the optimal delivery method when providing responses, taking into account the user's geographical location information. For example, if the user is in a specific region, the information delivery unit can prioritize providing information related to that region. Furthermore, if the user is traveling, the information delivery unit can prioritize providing information related to their travel destination. Also, if the user is at home, the information delivery unit can prioritize providing information related to their home area. This allows for the provision of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the information delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the information delivery unit can input the user's geographical location information into a generative AI and have the generative AI select the optimal delivery method.

[0083] The information provider can analyze the user's social media activity and propose information delivery methods when providing responses. For example, if the user posts about childcare on social media, the information provider will prioritize providing information related to childcare. Similarly, if the user posts about pregnancy on social media, the information provider can prioritize providing information related to pregnancy. Furthermore, the information provider can filter and provide information related to the user's areas of interest from their social media activity. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider can input data on the user's social media activity into a generative AI and have the generative AI propose information delivery methods.

[0084] The API unit can estimate the user's emotional state and adjust the API provision method based on the estimated user's emotional state. For example, if the user is feeling stressed, the API unit can provide the API in a concise and easy-to-understand format. Furthermore, if the user is relaxed, the API unit can provide the API in a format including detailed explanations. Furthermore, if the user is in a hurry, the API unit can provide the API quickly. This allows the API to be provided in an appropriate format by adjusting the API provision method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the API unit can be performed using, for example, the generation AI. For example, the API unit can input the user's emotional state into the generation AI and have the generation AI estimate the emotion and adjust the API provision method.

[0085] The API unit can select the optimal API delivery method by referring to past usage data of each local government when providing APIs. For example, the API unit can select the optimal API delivery method based on past usage data of each local government. The API unit can also predict and propose API delivery methods to be used during specific time periods based on usage data of each local government. Furthermore, the API unit can analyze past usage data of each local government and select the most efficient API delivery method. In this way, the optimal API delivery method can be selected by referring to past usage data of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or without a generative AI. For example, the API unit can input past usage data of each local government into a generative AI and have the generative AI select the optimal API delivery method.

[0086] The API unit can customize the means of providing APIs based on the characteristics of each local government. For example, the API unit can customize the optimal API provision method based on the characteristics of each local government. The API unit can also adjust the API provision method according to the population composition of each local government. Furthermore, the API unit can customize the API provision method based on the geographical characteristics of each local government. In this way, appropriate APIs can be provided by customizing the provision method based on the characteristics of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the API unit can input the characteristics of each local government into a generative AI and have the generative AI perform the customization of the provision method.

[0087] The API unit can estimate the user's emotional state and determine the priority of APIs based on the estimated emotional state of the user. For example, if the user is stressed, the API unit can prioritize providing APIs with high urgency. Furthermore, if the user is relaxed, the API unit can prioritize providing detailed APIs. Furthermore, if the user is in a hurry, the API unit can prioritize providing concise APIs. This allows the API priority to be determined according to the user's emotions, thereby providing appropriate APIs with priority. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the API unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the API unit can input the user's emotional state into the generation AI and have the generation AI perform emotion estimation and API priority determination.

[0088] The API unit can select the optimal API delivery method by considering the geographical location information of each local government when providing APIs. For example, the API unit can select the optimal API delivery method based on the geographical location information of each local government. The API unit can also adjust the API delivery method according to the geographical characteristics of each local government. Furthermore, the API unit can analyze the geographical location information of each local government and select the most efficient API delivery method. This allows for the provision of highly relevant APIs by considering the geographical location information of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or without a generative AI. For example, the API unit can input the geographical location information of each local government into a generative AI and have the generative AI select the optimal delivery method.

[0089] When providing the API, the API unit can analyze the social media activity of each local government and propose a means of delivery. For example, the API unit can propose the optimal means of API delivery based on the social media activity of each local government. The API unit can also predict and propose specific means of delivery from the social media activity of each local government. The API unit can also analyze the social media activity of each local government and propose the most efficient means of API delivery. This makes it possible to provide highly relevant APIs by analyzing the social media activity of each local government. Some or all of the above-mentioned processing in the API unit may be performed using, or without, a generation AI. For example, the API unit can input data on the social media activity of each local government into the generation AI and have the generation AI execute the proposal of delivery means. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and API unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives questions entered by the user through a messaging application. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the questions using generation AI to generate appropriate answers. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated answers to the user. The API unit is implemented by the communication I / F 26 of the data processing unit 12 and turns the provided services into APIs and links them to childcare support websites of each local government. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and API unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives questions entered by the user through a messaging application. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the question using generation AI to generate an appropriate answer. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides the generated answer to the user. The API unit is implemented by the communication I / F 26 of the data processing unit 12 and turns the provided service into an API and links it to childcare support sites of each local government. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and API unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives questions entered by the user through a messaging application. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the question using a generation AI to generate an appropriate answer. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the generated answer to the user. The API unit is implemented by, for example, the communication I / F 26 of the data processing unit 12 and turns the provided service into an API and links it to childcare support sites of each local government. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and API unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives questions entered by the user through a messaging application. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the question using a generation AI to generate an appropriate answer. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated answer to the user. The API unit is implemented by, for example, the communication I / F 26 of the data processing unit 12 and turns the provided service into an API and links it to childcare support sites of each local government.

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

[0091] The reception unit can analyze the user's past question history and select the optimal reception method. For example, it can automatically display questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest question formats to be used during specific time periods based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past question history into the generation AI and have the generation AI select the optimal reception method.

[0092] The generation unit can estimate the user's emotional state and adjust the way an answer is expressed based on the estimated emotional state. For example, if the user is stressed, the generation unit can generate a concise and easy-to-understand answer. Furthermore, if the user is relaxed, the generation unit can generate an answer that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can generate a quick answer that focuses on the main points. This allows for providing a more appropriate answer by adjusting the way an answer is expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotional state into the generation AI and have the generation AI estimate the emotion and adjust the way the answer is expressed.

[0093] The service provider can select the optimal delivery method by referring to the user's past question history when providing answers. For example, it may prioritize delivery methods that the user has previously preferred (text, audio, etc.). The service provider can also predict and suggest specific delivery methods based on the user's past question history. Furthermore, the service provider can analyze the user's past question history and select the optimal delivery method. This allows the service provider to select the optimal delivery method by referring to the user's past question history. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past question history into a generative AI and have the generative AI select the optimal delivery method.

[0094] The API unit can select the optimal API delivery method by referring to past usage data of each local government when providing APIs. For example, it can select the optimal API delivery method based on past usage data of each local government. The API unit can also predict and propose API delivery methods to be used during specific time periods based on usage data of each local government. Furthermore, the API unit can analyze past usage data of each local government and select the most efficient API delivery method. In this way, the optimal API delivery method can be selected by referring to past usage data of each local government. Some or all of the above processing in the API unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the API unit can input past usage data of each local government into a generative AI and have the generative AI select the optimal API delivery method.

[0095] The reception unit can estimate the user's emotional state and adjust the timing of question reception based on the estimated emotional state. For example, if the user is feeling stressed, the reception unit can immediately accept the question and provide a prompt response. Furthermore, if the user is relaxed, the reception unit can slightly delay the reception of the question to allow the user time to enter detailed information. Furthermore, if the user is in a hurry, the reception unit can provide a simple question input interface and quickly accept the question. This allows the timing of question reception to be adjusted according to the user's emotions, thereby allowing questions to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's emotional state into the generation AI and have the generation AI perform emotion estimation and adjust the timing of question reception.

[0096] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. For example, a detailed answer is generated for a question of high importance. The generation unit can also generate a concise answer for a question of low importance. Furthermore, the generation unit can gradually adjust the level of detail of the answer depending on the importance of the question. This makes it possible to provide an appropriate answer by adjusting the level of detail of the answer depending on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the question to the generation AI and have the generation AI adjust the level of detail of the answer.

[0097] The service provider can estimate the user's emotional state and determine the priority of the answers to provide based on the estimated emotional state. For example, if the user is stressed, it can prioritize providing urgent answers. The service provider can also prioritize providing detailed answers if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize providing concise answers. This ensures that appropriate answers are provided by prioritizing answers according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input the user's emotional state into a generative AI and have the generative AI perform emotion estimation and determine the priority of answers.

[0098] When providing an API, the API unit can customize the delivery means based on the characteristics of each local government. For example, it customizes the optimal API delivery means based on the characteristics of each local government. The API unit can also adjust the API delivery means according to the population composition of each local government. Furthermore, the API unit can also customize the API delivery means based on the geographical characteristics of each local government. This allows for the provision of an appropriate API by customizing the delivery means based on the characteristics of each local government. Some or all of the above-mentioned processing in the API unit may be performed using, or without, a generation AI. For example, the API unit can input the characteristics of each local government into the generation AI and have the generation AI customize the delivery means.

[0099] When receiving a question, the reception unit can filter the questions based on the user's current living situation or areas of interest. For example, if the user is raising a child, the reception unit can prioritize receiving questions related to childcare. Also, if the user is pregnant, the reception unit can prioritize receiving questions related to pregnancy. Furthermore, the reception unit can filter and receive related questions based on the user's areas of interest. In this way, by filtering questions based on the user's living situation or areas of interest, more relevant questions can be received. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input data on the user's living situation or areas of interest into the generation AI and have the generation AI perform question filtering.

[0100] When providing an answer, the providing unit can select the optimal providing method 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 provided preferentially. Furthermore, if the user is traveling, the providing unit can also provide preferentially information related to the travel destination. Furthermore, if the user is at home, the providing unit can also provide preferentially information related to the area around the user's home. In this way, highly relevant information can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal providing method.

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

[0102] Step 1: The reception unit accepts questions entered by the user through a messaging app. Questions entered by the user include "What to do if my baby won't stop crying in the middle of the night" and "Advice on diet during pregnancy." The reception unit can also accept questions via voice input, converting the voice data into text data and accepting it. Step 2: The generation unit uses a generation AI to analyze the question received by the reception unit and generate an appropriate answer. The generation AI analyzes the question using natural language processing techniques such as GPT-3 and BERT, and generates an answer based on related information. Different generation algorithms can be applied depending on the content of the question. Step 3: The providing unit provides the answer generated by the generating unit to the user. The providing unit may provide the generated answer to the user through a messaging app or may provide the generated answer in audio format. Furthermore, the providing unit estimates the user's emotional state and adjusts the method of providing the answer based on the estimated emotional state. Step 4: The API department turns the services provided by the provider department into APIs and connects them to each local government's child-rearing support site. The API department turns the services into APIs using technologies such as REST API and SOAP API, and integrates them into each local government's child-rearing support site. This eliminates disparities in the provision of services on each local government's child-rearing support site.

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

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

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

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

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

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

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

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

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

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

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

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

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

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

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

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

[0140] 7, 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 a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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 desk that handles questions from users, A generation unit analyzes the questions received by the reception unit and generates answers, A providing unit that provides the answer generated by the generation unit to the user, The API unit comprises a service provider unit that converts the services provided by the service provider unit into an API and links it to childcare support websites of each local government. A system characterized by:

2. The reception unit The system accepts questions entered by users via messaging apps. The system of claim 1 .

3. The generation unit The AI ​​generates responses by analyzing the entered questions and producing appropriate answers. The system of claim 1 .

4. The providing unit Providing the generated answer to the user The system of claim 1 .

5. The API unit We collaborate with local government childcare support websites to provide services. The system of claim 1 .

6. The reception unit The system estimates the user's emotional state and adjusts the timing of question submissions based on that estimate. The system of claim 1 .

7. The reception unit Analyze the user's past question history and select the appropriate method for receiving inquiries. The system of claim 1 .

8. The reception unit When receiving questions, filtering is performed based on the user's current life situation or areas of interest. The system of claim 1 .

9. The reception unit The system estimates the user's emotions and prioritizes the questions to be asked based on those estimated emotions. The system of claim 1 .

10. The reception unit When receiving questions, the system prioritizes questions that are highly relevant based on the user's geographical location. The system of claim 1 .

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