system

Abstract

An object of a system according to an embodiment is to easily search for and consult about worries about pregnancy and childcare and provide highly reliable information.SOLUTION: A system includes a reception unit, an analysis unit, a search unit, an evaluation unit, a provision unit, and an API unit. The reception unit receives a question from a user. The analysis unit analyzes the question received by the reception unit. The retrieval unit retrieves related information on the basis of the question analyzed by the analysis unit. The evaluation unit evaluates reliability of the information retrieved by the retrieval unit. The provision unit provides an answer based on the information evaluated by the evaluation unit. The API unit converts the platform provided by the providing unit into an API.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to easily search for and seek advice on concerns about pregnancy and child-rearing, and to obtain reliable information.

[0005] The system according to the embodiment aims to provide highly reliable information by allowing users to easily search for and seek advice on concerns about pregnancy and child-rearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a search unit, an evaluation unit, a provision unit, and an API unit. The reception unit receives questions from users. The analysis unit analyzes the questions received by the reception unit. The search unit searches for related information based on the questions analyzed by the analysis unit. The evaluation unit evaluates the reliability of the information searched by the search unit. The provision unit provides answers based on the information evaluated by the evaluation unit. The API unit converts the platform provided by the provision unit into an API. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily search for and seek advice on concerns about pregnancy and child-rearing, and provides reliable information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A platform according to an embodiment of the present invention is a system that allows users to easily search for and seek advice on pregnancy and child-rearing-related concerns. In this system, users input their pregnancy and child-rearing concerns into a chatbot, which analyzes the concerns, searches for relevant information, and displays only information that has been evaluated for reliability. Users can also receive answers to their inquiries. Furthermore, this platform is an API, making it possible to incorporate it into municipal services. For example, if a user inputs a question into the chatbot, such as "Please tell me about diet during pregnancy," the chatbot analyzes the question using natural language processing technology, searches for and displays reliable information on "diet during pregnancy." This allows users to obtain reliable information. Furthermore, by incorporating this platform into the official website or app of a municipality, residents can easily seek advice on pregnancy and child-rearing-related concerns. This allows users with pregnancy and child-rearing concerns to easily obtain reliable information, making it suitable for use as a municipal service.

[0029] The platform according to the embodiment includes a reception unit, an analysis unit, a search unit, an evaluation unit, a provision unit, and an API unit. The reception unit receives questions from users. Questions from users may be in text format, audio format, image format, or the like, but are not limited to these examples. The reception unit receives text questions through a chat interface, for example. The reception unit can also receive audio questions using voice recognition technology. The reception unit can also receive image questions using image analysis technology. The analysis unit analyzes the questions received by the reception unit using natural language processing technology. The analysis unit analyzes the content of the questions using, for example, morphological analysis. The analysis unit can also analyze the structure of the questions using grammatical analysis. The analysis unit can also understand the meaning of the questions using semantic analysis. The search unit searches for related information based on the questions analyzed by the analysis unit. The search unit searches a database for information related to pregnancy and child-rearing, for example. The search unit can also search for information from reliable sources on the Internet. The evaluation unit evaluates the reliability of the information searched by the search unit. The evaluation unit, for example, prioritizes displaying information provided by medical institutions or experts. The evaluation unit can also evaluate the reliability of information sources and prioritize displaying highly reliable information. The provision unit provides answers to users based on the information evaluated by the evaluation unit. The provision unit, for example, displays highly reliable information to users. The provision unit can also notify users of highly reliable information. The API unit converts the platform provided by the provision unit into an API and incorporates it into the local government's official website or app. The API unit, for example, provides an API endpoint and defines a data format and authentication method. The API unit can also provide guidelines for integrating the platform into local government services. This enables the platform according to the embodiment to efficiently accept, analyze, search, evaluate, provide, and convert user questions into an API.

[0030] The analysis unit can analyze the question using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit analyzes the content of the question using morphological analysis, for example. Morphological analysis is a technology that divides a sentence into words and identifies the part of speech of each word. The analysis unit can also analyze the structure of the question using grammatical analysis. Grammatical analysis is a technology that analyzes the grammatical structure of a sentence and identifies relationships such as between a subject, predicate, and object. The analysis unit can also understand the meaning of the question using semantic analysis. Semantic analysis is a technology that analyzes the meaning of a sentence and performs an appropriate interpretation based on the context. As a result, the use of natural language processing technology improves the accuracy of question analysis.

[0031] The evaluation unit can prioritize displaying information provided by medical institutions or experts. Examples of medical institutions or experts include, but are not limited to, certified medical institutions and individuals with professional qualifications. The evaluation unit can prioritize displaying information provided by certified medical institutions, for example. Certified medical institutions are institutions that provide highly reliable medical information, and the information they provide has a high level of reliability. The evaluation unit can also prioritize displaying information provided by individuals with professional qualifications. Individuals with professional qualifications are individuals who have specialized knowledge and experience in a particular field, and the information they provide has a high level of reliability. This allows the user to use the information with peace of mind by preferentially displaying highly reliable information.

[0032] The providing unit can provide highly reliable information to the user. Examples of highly reliable information include, but are not limited to, information provided by medical institutions or experts, and information obtained from highly reliable information sources. The providing unit can, for example, display information provided by medical institutions or experts to the user. The providing unit can also notify the user of information obtained from highly reliable information sources. Examples of highly reliable information sources include government agencies, academic institutions, and professional organizations. By providing highly reliable information, the user can use the information with peace of mind.

[0033] The API department can incorporate the platform into the local government's official website or app. Examples of the local government's official website or app include, but are not limited to, the official website or official app of a specific local government. The API department can, for example, provide API endpoints and define data formats and authentication methods. The API department can also provide guidelines for integrating the platform into local government services, allowing the platform to be used as a local government service.

[0034] The search unit can search for information related to pregnancy and child-rearing. Information related to pregnancy and child-rearing includes, but is not limited to, medical information, child-rearing methods, and support services. For example, the search unit searches a database for medical information related to pregnancy. The search unit can also search for child-rearing methods from reliable information sources on the Internet. The search unit can also search for information on support services from official local government websites. This allows for efficient searching of information related to pregnancy and child-rearing.

[0035] 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 (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the question format to be used in a specific time period based on the user's past question history. In this way, by analyzing the past question history, the optimal reception method can be provided to the user.

[0036] When receiving a question, the reception unit can filter the questions based on the user's current situation or area of ​​interest. For example, if the user is pregnant, the reception unit can preferentially receive questions about pregnancy. Also, if the user is raising a child, the reception unit can preferentially receive questions about child-rearing. Also, the reception unit can filter relevant questions based on the user's current situation (e.g., early pregnancy, raising a child). In this way, by filtering questions based on the user's situation or area of ​​interest, more relevant questions can be received.

[0037] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question using text, the acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the acceptance unit can also accept the question using image analysis technology. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0038] When accepting questions, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user lives in a specific area, the acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the acceptance unit can also prioritize accepting questions related to the user's travel destination. Furthermore, the acceptance unit can filter highly relevant questions based on the user's geographical location information. This allows for more appropriate responses by preferentially accepting highly relevant questions based on the user's geographical location information.

[0039] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit can receive related questions based on, for example, information shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related questions can be received efficiently.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and select the optimal reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis for a question with a high level of importance. The analysis unit can also perform a concise analysis for a question with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the question. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the question.

[0042] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit applies a pregnancy-related analysis algorithm to a question about pregnancy. The analysis unit can also apply a child-rearing-related analysis algorithm to a question about child-rearing. The analysis unit can also select the optimal analysis algorithm depending on the category of the question. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the question.

[0043] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the current question based on the analysis results of questions previously asked by the user. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0044] When analyzing a question, the analysis unit can determine the priority of the analysis based on when the question was submitted. For example, the analysis unit determines the priority of the analysis based on when the question was submitted. The analysis unit can also lower the priority of a question that was submitted earlier. The analysis unit can also raise the priority of a question that was submitted more recently. In this way, determining the priority of the analysis based on when the question was submitted enables efficient analysis.

[0045] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if the relevance of a question is high, the analysis unit prioritizes the analysis. Also, if the relevance of a question is low, the analysis unit can postpone the analysis. Also, the analysis unit can dynamically adjust the order of analysis based on the relevance of the questions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the questions.

[0046] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0047] The search unit can improve search accuracy by taking into account the interrelationships between pieces of information during a search. For example, the search unit can improve search accuracy by linking related pieces of information to each other. The search unit can also analyze the interrelationships between pieces of information to provide optimal search results. The search unit can also adjust the priority of search results based on the interrelationships between pieces of information. In this way, the search accuracy is improved by taking into account the interrelationships between pieces of information.

[0048] The search unit can conduct a search while taking into consideration the attribute information of the information provider. For example, the search unit preferentially searches for information provided by medical institutions or experts. The search unit can also evaluate the reliability of the information provider and preferentially search for highly reliable information. The search unit can also search for related information based on the information provider's field of expertise. This allows highly reliable information to be preferentially searched for by taking into consideration the attribute information of the information provider.

[0049] The search unit can weight the search results based on the frequency of information provided during the search. For example, the search unit can prioritize information provided more frequently in the search results. The search unit can also postpone information provided less frequently. The search unit can also dynamically adjust the weighting of the search results based on the frequency of information provided. This allows for efficient searches by weighting the search based on the frequency of information provided.

[0050] The search unit can perform a search while taking into account the geographical distribution of information. For example, the search unit prioritizes searching for information close to the user's current location. The search unit can also display geographically related information in the search results. The search unit can also adjust the priority of search results based on the geographical distribution of information. This allows highly relevant information to be searched for preferentially by taking into account the geographical distribution of information.

[0051] The search unit can improve the accuracy of the search by referring to literature related to the information during the search. For example, the search unit can improve the accuracy of the search results by referring to related literature. The search unit can also provide optimal search results based on literature related to the information. The search unit can also adjust the priority of the search results based on the related literature. In this way, by referring to literature related to the information, the accuracy of the search is improved.

[0052] The search unit can perform a search while taking into consideration the market value of the information. For example, the search unit prioritizes searches for information with a high market value. The search unit can also postpone searches for information with a low market value. The search unit can also adjust the priority of search results based on the market value of the information. This allows high-value information to be searched for preferentially by taking into consideration the market value of the information.

[0053] The evaluation unit can predict the current evaluation by referring to past evaluation data during evaluation. The evaluation unit predicts the current evaluation based on, for example, past evaluation data. The evaluation unit can also extract specific patterns from the past evaluation data and reflect them in the current evaluation. The evaluation unit can also select the optimal evaluation method by referring to the past evaluation data. In this way, the accuracy of the current evaluation is improved by referring to the past evaluation data.

[0054] The evaluation unit can apply different evaluation methods to each information category during evaluation. For example, the evaluation unit applies a pregnancy-related evaluation method to information about pregnancy. The evaluation unit can also apply a child-rearing-related evaluation method to information about child-rearing. The evaluation unit can also select the most appropriate evaluation method depending on the information category. This improves the accuracy of the evaluation by applying the most appropriate evaluation method to each information category.

[0055] The evaluation unit can make an evaluation taking into consideration attribute information of the information provider. For example, the evaluation unit prioritizes evaluation of information provided by medical institutions or experts. The evaluation unit can also evaluate the reliability of the information provider and prioritize evaluation of highly reliable information. The evaluation unit can also evaluate related information based on the information provider's field of expertise. This allows for a highly reliable evaluation by taking into consideration attribute information of the information provider.

[0056] The evaluation unit can analyze changes in evaluation based on the time when the information was provided at the time of evaluation. For example, the evaluation unit analyzes changes in evaluation when the information was provided recently. The evaluation unit can also analyze changes in evaluation when the information was provided old. The evaluation unit can also dynamically analyze changes in evaluation based on the time when the information was provided. This improves the accuracy of the evaluation by analyzing changes in evaluation based on the time when the information was provided.

[0057] The evaluation unit can analyze the evaluation by referring to market data related to the information when evaluating. For example, the evaluation unit can improve the accuracy of the evaluation by referring to the related market data. The evaluation unit can also provide an optimal evaluation based on the market data related to the information. The evaluation unit can also adjust the priority of the evaluation based on the related market data. In this way, the accuracy of the evaluation is improved by referring to the market data related to the information.

[0058] The evaluation unit can analyze the evaluation taking into account the technical maturity of the information when evaluating. For example, the evaluation unit prioritizes evaluation of information with a high level of technical maturity. The evaluation unit can also postpone evaluation of information with a low level of technical maturity. The evaluation unit can also adjust the priority of evaluation based on the technical maturity of the information. In this way, the accuracy of the evaluation is improved by taking into account the technical maturity of the information.

[0059] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between pieces of information when providing the information. For example, the providing unit can improve the accuracy of the information provided by linking related pieces of information to each other. The providing unit can also analyze the interrelationships between pieces of information and provide optimal information. The providing unit can also adjust the priority of the information to be provided based on the interrelationships between pieces of information. In this way, the accuracy of the information provided is improved by taking into account the interrelationships between pieces of information.

[0060] The providing unit can provide information while taking into consideration attribute information of the information provider. The providing unit can provide information provided by, for example, medical institutions or experts with priority. The providing unit can also evaluate the reliability of the information provider and provide highly reliable information with priority. The providing unit can also provide related information based on the specialty of the information provider. In this way, highly reliable information can be provided with priority by taking into consideration attribute information of the information provider.

[0061] The providing unit can weight the information provided based on the frequency of information provided at the time of providing the information. For example, the providing unit provides information with a high frequency of provision preferentially. The providing unit can also postpone information with a low frequency of provision. The providing unit can also dynamically adjust the weighting of the information to be provided based on the frequency of information provided. As a result, by weighting the information provided based on the frequency of information provided, efficient information provision is possible.

[0062] The providing unit can provide information taking into consideration the geographical distribution of the information. For example, the providing unit can provide information that is close to the user's current location with priority. The providing unit can also provide geographically related information. The providing unit can also adjust the priority of the information to be provided based on the geographical distribution of the information. In this way, by taking the geographical distribution of the information into consideration, highly related information can be provided with priority.

[0063] The providing unit can improve the accuracy of the information provided by referring to literature related to the information when providing the information. For example, the providing unit improves the accuracy of the information to be provided by referring to related literature. The providing unit can also provide optimal information based on literature related to the information. The providing unit can also adjust the priority of the information to be provided based on related literature. In this way, the accuracy of the information provided is improved by referring to literature related to the information.

[0064] The providing unit can provide information taking into consideration the market value of the information when providing the information. For example, the providing unit can provide information with a high market value preferentially. The providing unit can also postpone information with a low market value. The providing unit can also adjust the priority of the information to be provided based on the market value of the information. In this way, by taking the market value of the information into consideration, it is possible to provide information with a high value preferentially.

[0065] When using an API, the API unit can refer to past usage data to select the optimal usage method. For example, the API unit can propose the optimal API usage method based on past usage data. The API unit can also extract specific patterns from past usage data and select the optimal usage method. The API unit can also refer to past usage data to select the optimal API usage method. In this way, by referring to past usage data, it is possible to provide the optimal API usage method.

[0066] The API unit can improve API functions by reflecting user feedback when the API is used. For example, the API unit improves API functions based on user feedback. The API unit can also extract specific improvements from user feedback and improve API functions. The API unit can also refer to user feedback to select the optimal method for improving the API. In this way, the API functions are improved by reflecting user feedback.

[0067] When using the API, the API unit can integrate information from different data sources to expand the functionality of the API. For example, the API unit integrates information from different data sources to expand the functionality of the API. The API unit can also provide optimal information taking into account the diversity of data sources. The API unit can also improve the functionality of the API based on information from different data sources. In this way, the functionality of the API is expanded by integrating information from different data sources.

[0068] When using the API, the API unit can integrate information from different data sources to expand the functionality of the API. For example, the API unit integrates information from different data sources to expand the functionality of the API. The API unit can also provide optimal information taking into account the diversity of data sources. The API unit can also improve the functionality of the API based on information from different data sources. In this way, the functionality of the API is expanded by integrating information from different data sources.

[0069] When using an API, the API unit can select the optimal usage method by taking into account the user's device information. For example, if the user is using a smartphone, the API unit can provide an API usage method that suits the screen size. Furthermore, if the user is using a tablet, the API unit can also provide an API usage method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the API unit can also provide a simple and highly visible API usage method. In this way, the optimal API usage method can be provided by taking into account the user's device information.

[0070] The API unit can improve API functions by reflecting user feedback when the API is used. For example, the API unit improves API functions based on user feedback. The API unit can also extract specific improvements from user feedback and improve API functions. The API unit can also refer to user feedback to select the optimal method for improving the API. In this way, the API functions are improved by reflecting user feedback.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] 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. It can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the question format to be used during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, it is possible to provide the optimal reception method for the user.

[0073] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, a detailed analysis can be performed for a question with a high level of importance. On the other hand, a simple analysis can be performed for a question with a low level of importance. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the question. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the question.

[0074] The search unit can improve search accuracy by taking into account the interrelationships of information during a search. For example, related information can be linked to each other to improve search accuracy. The search unit can also analyze the interrelationships of information to provide optimal search results. Furthermore, the search unit can adjust the priority of search results based on the interrelationships of information. In this way, the search accuracy is improved by taking into account the interrelationships of information.

[0075] The evaluation unit can take into consideration the attribute information of the information provider when making the evaluation. For example, it can prioritize the evaluation of information provided by medical institutions or experts. It can also evaluate the reliability of the information provider and prioritize the evaluation of highly reliable information. Furthermore, it can also evaluate related information based on the information provider's field of expertise. In this way, by taking into consideration the attribute information of the information provider, a highly reliable evaluation is possible.

[0076] The providing unit can weight the information provided based on the frequency of information provided at the time of providing the information. For example, information provided more frequently can be provided preferentially. Information provided less frequently can also be postponed. Furthermore, the weighting of the information to be provided can be dynamically adjusted based on the frequency of information provided. Thus, by weighting the information provided based on the frequency of information provided, efficient information provision is possible.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives questions from users. Questions from users may be in text format, voice format, image format, etc. For example, text format questions are received through a chat interface, voice format questions are received using voice recognition technology, and image format questions are received using image analysis technology. Step 2: The analysis unit uses natural language processing technology to analyze the question received by the reception unit. For example, it analyzes the content of the question using morphological analysis, analyzes the structure of the question using grammatical analysis, and understands the meaning of the question using semantic analysis. Step 3: The search unit searches for relevant information based on the query analyzed by the analysis unit, for example, searching for information on pregnancy and child-rearing from a database and retrieving information from reliable sources on the Internet. Step 4: The evaluation unit evaluates the reliability of the information retrieved by the search unit. For example, it may prioritize displaying information provided by medical institutions or experts, and it may evaluate the reliability of the information source and prioritize displaying highly reliable information. Step 5: The providing unit provides an answer to the user based on the information evaluated by the evaluating unit, for example, by displaying highly reliable information to the user and notifying the user of the highly reliable information. Step 6: The API Department converts the platform provided by the Provider Department into an API and incorporates it into the local government's official website or app. For example, they provide API endpoints, define data formats and authentication methods, and provide guidelines for integrating the platform into local government services.

[0079] (Example 2) A platform according to an embodiment of the present invention is a system that allows users to easily search for and seek advice on pregnancy and child-rearing-related concerns. In this system, users input their pregnancy and child-rearing concerns into a chatbot, which analyzes the concerns, searches for relevant information, and displays only information that has been evaluated for reliability. Users can also receive answers to their inquiries. Furthermore, this platform is an API, making it possible to incorporate it into municipal services. For example, if a user inputs a question into the chatbot, such as "Please tell me about diet during pregnancy," the chatbot analyzes the question using natural language processing technology, searches for and displays reliable information on "diet during pregnancy." This allows users to obtain reliable information. Furthermore, by incorporating this platform into the official website or app of a municipality, residents can easily seek advice on pregnancy and child-rearing-related concerns. This allows users with pregnancy and child-rearing concerns to easily obtain reliable information, making it suitable for use as a municipal service.

[0080] The platform according to the embodiment includes a reception unit, an analysis unit, a search unit, an evaluation unit, a provision unit, and an API unit. The reception unit receives questions from users. Questions from users may be in text format, audio format, image format, or the like, but are not limited to these examples. The reception unit receives text questions through a chat interface, for example. The reception unit can also receive audio questions using voice recognition technology. The reception unit can also receive image questions using image analysis technology. The analysis unit analyzes the questions received by the reception unit using natural language processing technology. The analysis unit analyzes the content of the questions using, for example, morphological analysis. The analysis unit can also analyze the structure of the questions using grammatical analysis. The analysis unit can also understand the meaning of the questions using semantic analysis. The search unit searches for related information based on the questions analyzed by the analysis unit. The search unit searches a database for information related to pregnancy and child-rearing, for example. The search unit can also search for information from reliable sources on the Internet. The evaluation unit evaluates the reliability of the information searched by the search unit. The evaluation unit, for example, prioritizes displaying information provided by medical institutions or experts. The evaluation unit can also evaluate the reliability of information sources and prioritize displaying highly reliable information. The provision unit provides answers to users based on the information evaluated by the evaluation unit. The provision unit, for example, displays highly reliable information to users. The provision unit can also notify users of highly reliable information. The API unit converts the platform provided by the provision unit into an API and incorporates it into the local government's official website or app. The API unit, for example, provides an API endpoint and defines a data format and authentication method. The API unit can also provide guidelines for integrating the platform into local government services. This enables the platform according to the embodiment to efficiently accept, analyze, search, evaluate, provide, and convert user questions into an API.

[0081] The analysis unit can analyze the question using natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis, for example. The analysis unit analyzes the content of the question using morphological analysis, for example. Morphological analysis is a technology that divides a sentence into words and identifies the part of speech of each word. The analysis unit can also analyze the structure of the question using grammatical analysis. Grammatical analysis is a technology that analyzes the grammatical structure of a sentence and identifies relationships such as between a subject, predicate, and object. The analysis unit can also understand the meaning of the question using semantic analysis. Semantic analysis is a technology that analyzes the meaning of a sentence and performs an appropriate interpretation based on the context. As a result, the use of natural language processing technology improves the accuracy of question analysis.

[0082] The evaluation unit can prioritize displaying information provided by medical institutions or experts. Examples of medical institutions or experts include, but are not limited to, certified medical institutions and individuals with professional qualifications. The evaluation unit can prioritize displaying information provided by certified medical institutions, for example. Certified medical institutions are institutions that provide highly reliable medical information, and the information they provide has a high level of reliability. The evaluation unit can also prioritize displaying information provided by individuals with professional qualifications. Individuals with professional qualifications are individuals who have specialized knowledge and experience in a particular field, and the information they provide has a high level of reliability. This allows the user to use the information with peace of mind by preferentially displaying highly reliable information.

[0083] The providing unit can provide highly reliable information to the user. Examples of highly reliable information include, but are not limited to, information provided by medical institutions or experts, and information obtained from highly reliable information sources. The providing unit can, for example, display information provided by medical institutions or experts to the user. The providing unit can also notify the user of information obtained from highly reliable information sources. Examples of highly reliable information sources include government agencies, academic institutions, and professional organizations. By providing highly reliable information, the user can use the information with peace of mind.

[0084] The API department can incorporate the platform into the local government's official website or app. Examples of the local government's official website or app include, but are not limited to, the official website or official app of a specific local government. The API department can, for example, provide API endpoints and define data formats and authentication methods. The API department can also provide guidelines for integrating the platform into local government services, allowing the platform to be used as a local government service.

[0085] The search unit can search for information related to pregnancy and child-rearing. Information related to pregnancy and child-rearing includes, but is not limited to, medical information, child-rearing methods, and support services. For example, the search unit searches a database for medical information related to pregnancy. The search unit can also search for child-rearing methods from reliable information sources on the Internet. The search unit can also search for information on support services from official local government websites. This allows for efficient searching of information related to pregnancy and child-rearing.

[0086] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive questions and respond immediately. Furthermore, if the user is relaxed, the reception unit can take its time to respond to detailed questions. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving brief questions. This allows for more appropriate responses by adjusting the timing of receiving questions according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] 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 (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the question format to be used in a specific time period based on the user's past question history. In this way, by analyzing the past question history, the optimal reception method can be provided to the user.

[0088] When receiving a question, the reception unit can filter the questions based on the user's current situation or area of ​​interest. For example, if the user is pregnant, the reception unit can preferentially receive questions about pregnancy. Also, if the user is raising a child, the reception unit can preferentially receive questions about child-rearing. Also, the reception unit can filter relevant questions based on the user's current situation (e.g., early pregnancy, raising a child). In this way, by filtering questions based on the user's situation or area of ​​interest, more relevant questions can be received.

[0089] When accepting a question, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, when the user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when the user inputs a question using text, the acceptance unit can also accept the question using text analysis technology. Furthermore, when the user inputs a question using an image, the acceptance unit can also accept the question using image analysis technology. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0090] 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, if the user is feeling anxious, the reception unit can prioritize urgent questions. Furthermore, if the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, if the user is in a hurry, the reception unit can prioritize concise questions. This allows for more appropriate responses by determining the priority of questions according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When accepting questions, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, if the user lives in a specific area, the acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the acceptance unit can also prioritize accepting questions related to the user's travel destination. Furthermore, the acceptance unit can filter highly relevant questions based on the user's geographical location information. This allows for more appropriate responses by preferentially accepting highly relevant questions based on the user's geographical location information.

[0092] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit can receive related questions based on, for example, information shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related questions can be received efficiently.

[0093] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit can, for example, suggest the optimal reception method based on the user's past feedback. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and select the optimal reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit uses a concise and reassuring presentation method. If the user is relaxed, the analysis unit can also use a presentation method that includes detailed information. If the user is in a hurry, the analysis unit can also use a concise presentation method that focuses on the main points. This allows the analysis presentation method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. The estimation of the user's emotions is realized using an emotion estimation function, for example, using 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.

[0095] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis for a question with a high level of importance. The analysis unit can also perform a concise analysis for a question with a low level of importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the question. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the question.

[0096] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit applies a pregnancy-related analysis algorithm to a question about pregnancy. The analysis unit can also apply a child-rearing-related analysis algorithm to a question about child-rearing. The analysis unit can also select the optimal analysis algorithm depending on the category of the question. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category of the question.

[0097] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit analyzes the current question based on the analysis results of questions previously asked by the user. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0098] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can perform a short, to-the-point analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is in a hurry, the analysis unit can also perform a concise analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When analyzing a question, the analysis unit can determine the priority of the analysis based on when the question was submitted. For example, the analysis unit determines the priority of the analysis based on when the question was submitted. The analysis unit can also lower the priority of a question that was submitted earlier. The analysis unit can also raise the priority of a question that was submitted more recently. In this way, determining the priority of the analysis based on when the question was submitted enables efficient analysis.

[0100] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if the relevance of a question is high, the analysis unit prioritizes the analysis. Also, if the relevance of a question is low, the analysis unit can postpone the analysis. Also, the analysis unit can dynamically adjust the order of analysis based on the relevance of the questions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the questions.

[0101] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can avoid technical terms. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0102] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is feeling anxious, the search unit prioritizes searching for reliable information. The search unit can also search for a wide range of information if the user is relaxed. The search unit can also prioritize searching for concise, to-the-point information if the user is in a hurry. This allows for adjusting the search criteria according to the user's emotions to provide more appropriate search results. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The search unit can improve search accuracy by taking into account the interrelationships between pieces of information during a search. For example, the search unit can improve search accuracy by linking related pieces of information to each other. The search unit can also analyze the interrelationships between pieces of information to provide optimal search results. The search unit can also adjust the priority of search results based on the interrelationships between pieces of information. In this way, the search accuracy is improved by taking into account the interrelationships between pieces of information.

[0104] The search unit can conduct a search while taking into consideration the attribute information of the information provider. For example, the search unit preferentially searches for information provided by medical institutions or experts. The search unit can also evaluate the reliability of the information provider and preferentially search for highly reliable information. The search unit can also search for related information based on the information provider's field of expertise. This allows highly reliable information to be preferentially searched for by taking into consideration the attribute information of the information provider.

[0105] The search unit can weight the search results based on the frequency of information provided during the search. For example, the search unit can prioritize information provided more frequently in the search results. The search unit can also postpone information provided less frequently. The search unit can also dynamically adjust the weighting of the search results based on the frequency of information provided. This allows for efficient searches by weighting the search based on the frequency of information provided.

[0106] The search unit can estimate the user's emotions and adjust the order in which search results are displayed based on the estimated user emotions. For example, if the user is feeling anxious, the search unit can display reliable information first. The search unit can also display a wide range of information if the user is relaxed. The search unit can also display concise, to-the-point information first if the user is in a hurry. This allows for more appropriate search results to be provided by adjusting the display order of search results according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] The search unit can perform a search while taking into account the geographical distribution of information. For example, the search unit prioritizes searching for information close to the user's current location. The search unit can also display geographically related information in the search results. The search unit can also adjust the priority of search results based on the geographical distribution of information. This allows highly relevant information to be searched for preferentially by taking into account the geographical distribution of information.

[0108] The search unit can improve the accuracy of the search by referring to literature related to the information during the search. For example, the search unit can improve the accuracy of the search results by referring to related literature. The search unit can also provide optimal search results based on literature related to the information. The search unit can also adjust the priority of the search results based on the related literature. In this way, by referring to literature related to the information, the accuracy of the search is improved.

[0109] The search unit can perform a search while taking into consideration the market value of the information. For example, the search unit prioritizes searches for information with a high market value. The search unit can also postpone searches for information with a low market value. The search unit can also adjust the priority of search results based on the market value of the information. This allows high-value information to be searched for preferentially by taking into consideration the market value of the information.

[0110] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation based on the estimated user's emotions. For example, if the user is feeling anxious, the evaluation unit uses a simple display method that gives a sense of security. If the user is relaxed, the evaluation unit can also use a display method that includes detailed information. If the user is in a hurry, the evaluation unit can also use a concise display method that focuses on the main points. This allows the evaluation display method to be adjusted according to the user's emotions, thereby providing more appropriate evaluation results. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The evaluation unit can predict the current evaluation by referring to past evaluation data during evaluation. The evaluation unit predicts the current evaluation based on, for example, past evaluation data. The evaluation unit can also extract specific patterns from the past evaluation data and reflect them in the current evaluation. The evaluation unit can also select the optimal evaluation method by referring to the past evaluation data. In this way, the accuracy of the current evaluation is improved by referring to the past evaluation data.

[0112] The evaluation unit can apply different evaluation methods to each information category during evaluation. For example, the evaluation unit applies a pregnancy-related evaluation method to information about pregnancy. The evaluation unit can also apply a child-rearing-related evaluation method to information about child-rearing. The evaluation unit can also select the most appropriate evaluation method depending on the information category. This improves the accuracy of the evaluation by applying the most appropriate evaluation method to each information category.

[0113] The evaluation unit can make an evaluation taking into consideration attribute information of the information provider. For example, the evaluation unit prioritizes evaluation of information provided by medical institutions or experts. The evaluation unit can also evaluate the reliability of the information provider and prioritize evaluation of highly reliable information. The evaluation unit can also evaluate related information based on the information provider's field of expertise. This allows for a highly reliable evaluation by taking into consideration attribute information of the information provider.

[0114] The evaluation unit can estimate the user's emotions and adjust the importance of the evaluations based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can prioritize displaying evaluations with high importance. Furthermore, if the user is relaxed, the evaluation unit can also display a wide range of evaluations. Furthermore, if the user is in a hurry, the evaluation unit can prioritize displaying concise and to-the-point evaluations. This allows for adjusting the importance of the evaluations according to the user's emotions, thereby providing more appropriate evaluation results. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The evaluation unit can analyze changes in evaluation based on the time when the information was provided at the time of evaluation. For example, the evaluation unit analyzes changes in evaluation when the information was provided recently. The evaluation unit can also analyze changes in evaluation when the information was provided old. The evaluation unit can also dynamically analyze changes in evaluation based on the time when the information was provided. This improves the accuracy of the evaluation by analyzing changes in evaluation based on the time when the information was provided.

[0116] The evaluation unit can analyze the evaluation by referring to market data related to the information when evaluating. For example, the evaluation unit can improve the accuracy of the evaluation by referring to the related market data. The evaluation unit can also provide an optimal evaluation based on the market data related to the information. The evaluation unit can also adjust the priority of the evaluation based on the related market data. In this way, the accuracy of the evaluation is improved by referring to the market data related to the information.

[0117] The evaluation unit can analyze the evaluation taking into account the technical maturity of the information when evaluating. For example, the evaluation unit prioritizes evaluation of information with a high level of technical maturity. The evaluation unit can also postpone evaluation of information with a low level of technical maturity. The evaluation unit can also adjust the priority of evaluation based on the technical maturity of the information. In this way, the accuracy of the evaluation is improved by taking into account the technical maturity of the information.

[0118] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing highly reliable information. The providing unit can also provide a wide range of information if the user is relaxed. The providing unit can also prioritize providing concise, to-the-point information if the user is in a hurry. This allows more appropriate information to be provided by determining the priority of information to be provided according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] The providing unit can improve the accuracy of the information provided by taking into account the interrelationships between pieces of information when providing the information. For example, the providing unit can improve the accuracy of the information provided by linking related pieces of information to each other. The providing unit can also analyze the interrelationships between pieces of information and provide optimal information. The providing unit can also adjust the priority of the information to be provided based on the interrelationships between pieces of information. In this way, the accuracy of the information provided is improved by taking into account the interrelationships between pieces of information.

[0120] The providing unit can provide information while taking into consideration attribute information of the information provider. The providing unit can provide information provided by, for example, medical institutions or experts with priority. The providing unit can also evaluate the reliability of the information provider and provide highly reliable information with priority. The providing unit can also provide related information based on the specialty of the information provider. In this way, highly reliable information can be provided with priority by taking into consideration attribute information of the information provider.

[0121] The providing unit can weight the information provided based on the frequency of information provided at the time of providing the information. For example, the providing unit provides information with a high frequency of provision preferentially. The providing unit can also postpone information with a low frequency of provision. The providing unit can also dynamically adjust the weighting of the information to be provided based on the frequency of information provided. As a result, by weighting the information provided based on the frequency of information provided, efficient information provision is possible.

[0122] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit uses a simple display method that gives a sense of security. Furthermore, if the user is relaxed, the providing unit can use a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can use a concise display method that focuses on the main points. In this way, by adjusting the information display method according to the user's emotions, more appropriate information can be provided. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0123] The providing unit can provide information taking into consideration the geographical distribution of the information. For example, the providing unit can provide information that is close to the user's current location with priority. The providing unit can also provide geographically related information. The providing unit can also adjust the priority of the information to be provided based on the geographical distribution of the information. In this way, by taking the geographical distribution of the information into consideration, highly related information can be provided with priority.

[0124] The providing unit can improve the accuracy of the information provided by referring to literature related to the information when providing the information. For example, the providing unit improves the accuracy of the information to be provided by referring to related literature. The providing unit can also provide optimal information based on literature related to the information. The providing unit can also adjust the priority of the information to be provided based on related literature. In this way, the accuracy of the information provided is improved by referring to literature related to the information.

[0125] The providing unit can provide information taking into consideration the market value of the information when providing the information. For example, the providing unit can provide information with a high market value preferentially. The providing unit can also postpone information with a low market value. The providing unit can also adjust the priority of the information to be provided based on the market value of the information. In this way, by taking the market value of the information into consideration, it is possible to provide information with a high value preferentially.

[0126] The API unit can estimate the user's emotions and adjust the API usage method based on the estimated user emotions. For example, if the user is feeling anxious, the API unit can provide a concise and reassuring API usage method. If the user is relaxed, the API unit can also provide an API usage method that includes detailed information. If the user is in a hurry, the API unit can also provide a concise API usage method that focuses on the main points. This allows for more appropriate use by adjusting the API usage method according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0127] When using an API, the API unit can refer to past usage data to select the optimal usage method. For example, the API unit can propose the optimal API usage method based on past usage data. The API unit can also extract specific patterns from past usage data and select the optimal usage method. The API unit can also refer to past usage data to select the optimal API usage method. In this way, by referring to past usage data, it is possible to provide the optimal API usage method.

[0128] The API unit can improve API functions by reflecting user feedback when the API is used. For example, the API unit improves API functions based on user feedback. The API unit can also extract specific improvements from user feedback and improve API functions. The API unit can also refer to user feedback to select the optimal method for improving the API. In this way, the API functions are improved by reflecting user feedback.

[0129] When using the API, the API unit can integrate information from different data sources to expand the functionality of the API. For example, the API unit integrates information from different data sources to expand the functionality of the API. The API unit can also provide optimal information taking into account the diversity of data sources. The API unit can also improve the functionality of the API based on information from different data sources. In this way, the functionality of the API is expanded by integrating information from different data sources.

[0130] The API unit can estimate the user's emotions and adjust the frequency of API usage based on the estimated user's emotions. For example, if the user is feeling anxious, the API unit can increase the frequency of API usage. The API unit can also adjust the frequency of API usage if the user is relaxed. The API unit can also optimize the frequency of API usage if the user is in a hurry. This allows for more appropriate usage by adjusting the frequency of API usage according to the user's emotions. The estimation of the user's emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0131] When using the API, the API unit can integrate information from different data sources to expand the functionality of the API. For example, the API unit integrates information from different data sources to expand the functionality of the API. The API unit can also provide optimal information taking into account the diversity of data sources. The API unit can also improve the functionality of the API based on information from different data sources. In this way, the functionality of the API is expanded by integrating information from different data sources.

[0132] When using an API, the API unit can select the optimal usage method by taking into account the user's device information. For example, if the user is using a smartphone, the API unit can provide an API usage method that suits the screen size. Furthermore, if the user is using a tablet, the API unit can also provide an API usage method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the API unit can also provide a simple and highly visible API usage method. In this way, the optimal API usage method can be provided by taking into account the user's device information.

[0133] The API unit can improve API functions by reflecting user feedback when the API is used. For example, the API unit improves API functions based on user feedback. The API unit can also extract specific improvements from user feedback and improve API functions. The API unit can also refer to user feedback to select the optimal method for improving the API. In this way, the API functions are improved by reflecting user feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, search unit, evaluation unit, provision unit, and API unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives questions from users. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the questions using natural language processing technology. The search unit is implemented by the specific processing unit 290 of the data processing device 12 and searches for related information. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the searched information. The provision unit is implemented by the output device 40 of the smart device 14 and provides the evaluated information to the user. The API unit is implemented by the specific processing unit 290 of the data processing device 12 and converts the platform into an API and integrates it into local government services. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, search unit, evaluation unit, provision unit, and API unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a question from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question using natural language processing technology. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the searched information. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the evaluated information to the user. The API unit is realized by the specific processing unit 290 of the data processing device 12 and converts the platform into an API and integrates it into local government services. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, search unit, evaluation unit, provision unit, and API unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a question from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question using natural language processing technology. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the searched information. The provision unit is realized by the display 343 of the headset-type terminal 314 and provides the evaluated information to the user. The API unit is realized by the specific processing unit 290 of the data processing device 12 and converts the platform into an API and integrates it into local government services. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, search unit, evaluation unit, provision unit, and API unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using natural language processing technology. The search unit is realized by the specific processing unit 290 of the data processing device 12 and searches for related information. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the searched information. The provision unit is realized by the speaker 240 of the robot 414 and provides the evaluated information to the user. The API unit is realized by the specific processing unit 290 of the data processing device 12 and converts the platform into an API and integrates it into local government services.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive questions and respond immediately. If the user is relaxed, the reception unit can take time to respond to detailed questions. Furthermore, if the user is in a hurry, the reception unit can prioritize brief questions. This allows for more appropriate responses by adjusting the timing of receiving questions according to the user's emotions.

[0136] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, a concise and reassuring presentation can be used. If the user is relaxed, a presentation that includes detailed information can be used. Furthermore, if the user is in a hurry, a concise presentation that focuses on the main points can be used. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0137] The search unit can estimate the user's emotions and adjust search criteria based on the estimated user emotions. For example, if the user is feeling anxious, it can prioritize searching for highly reliable information. If the user is relaxed, it can also search for a wide range of information. Furthermore, if the user is in a hurry, it can prioritize searching for concise, to-the-point information. In this way, by adjusting the search criteria according to the user's emotions, it is possible to provide more appropriate search results.

[0138] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation based on the estimated user's emotions. For example, if the user is feeling anxious, a simple display method that gives a sense of security can be used. If the user is relaxed, a display method that includes detailed information can be used. Furthermore, if the user is in a hurry, a simple display method that focuses on the main points can be used. In this way, by adjusting the display method of the evaluation according to the user's emotions, more appropriate evaluation results can be provided.

[0139] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling anxious, highly reliable information can be provided preferentially. Also, if the user is relaxed, a wide range of information can be provided preferentially. Furthermore, if the user is in a hurry, concise information that covers the main points can be provided preferentially. In this way, by determining the priority of information to be provided according to the user's emotions, more appropriate information can be provided.

[0140] 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. It can also preferentially suggest question formats (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the question format to be used during a specific time period based on the user's past question history. In this way, by analyzing the user's past question history, it is possible to provide the optimal reception method for the user.

[0141] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. For example, a detailed analysis can be performed for a question with a high level of importance. On the other hand, a simple analysis can be performed for a question with a low level of importance. Furthermore, the level of detail of the analysis can be dynamically adjusted according to the importance of the question. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the question.

[0142] The search unit can improve search accuracy by taking into account the interrelationships of information during a search. For example, related information can be linked to each other to improve search accuracy. The search unit can also analyze the interrelationships of information to provide optimal search results. Furthermore, the search unit can adjust the priority of search results based on the interrelationships of information. In this way, the search accuracy is improved by taking into account the interrelationships of information.

[0143] The evaluation unit can take into consideration the attribute information of the information provider when making the evaluation. For example, it can prioritize the evaluation of information provided by medical institutions or experts. It can also evaluate the reliability of the information provider and prioritize the evaluation of highly reliable information. Furthermore, it can also evaluate related information based on the information provider's field of expertise. In this way, by taking into consideration the attribute information of the information provider, a highly reliable evaluation is possible.

[0144] The providing unit can weight the information provided based on the frequency of information provided at the time of providing the information. For example, information provided more frequently can be provided preferentially. Information provided less frequently can also be postponed. Furthermore, the weighting of the information to be provided can be dynamically adjusted based on the frequency of information provided. Thus, by weighting the information provided based on the frequency of information provided, efficient information provision is possible.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit receives questions from users. Questions from users may be in text format, voice format, image format, etc. For example, text format questions are received through a chat interface, voice format questions are received using voice recognition technology, and image format questions are received using image analysis technology. Step 2: The analysis unit uses natural language processing technology to analyze the question received by the reception unit. For example, it analyzes the content of the question using morphological analysis, analyzes the structure of the question using grammatical analysis, and understands the meaning of the question using semantic analysis. Step 3: The search unit searches for relevant information based on the query analyzed by the analysis unit, for example, searching for information on pregnancy and child-rearing from a database and retrieving information from reliable sources on the Internet. Step 4: The evaluation unit evaluates the reliability of the information retrieved by the search unit. For example, it may prioritize displaying information provided by medical institutions or experts, and it may evaluate the reliability of the information source and prioritize displaying highly reliable information. Step 5: The providing unit provides an answer to the user based on the information evaluated by the evaluating unit, for example, by displaying highly reliable information to the user and notifying the user of the highly reliable information. Step 6: The API Department converts the platform provided by the Provider Department into an API and incorporates it into the local government's official website or app. For example, they provide API endpoints, define data formats and authentication methods, and provide guidelines for integrating the platform into local government services.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] 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.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0205] 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."

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; a search unit that searches for related information based on the question analyzed by the analysis unit; an evaluation unit that evaluates the reliability of the information searched by the search unit; a providing unit that provides an answer based on the information evaluated by the evaluating unit; and an API unit that converts the platform provided by the providing unit into an API.

2. The analysis unit Analyzing questions using natural language processing technology 2. The system of claim 1.

3. The evaluation unit Prioritize information provided by medical institutions or experts 2. The system of claim 1.

4. The providing unit Providing reliable information to users 2. The system of claim 1.

5. The API unit Embed the platform into your city's official website or app 2. The system of claim 1.

6. The search unit Search for information about pregnancy and child-rearing 2. The system of claim 1.

7. The reception unit A system that estimates a user's emotions and adjusts the timing of accepting questions based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the optimal reception method 2. The system of claim 1.

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