System

Abstract

An object of a system according to an embodiment is to eliminate anxiety of a parent about development of a child and to provide appropriate information.SOLUTION: A system includes an interpretation unit, a generation unit, an information providing unit, and a determination unit. The interpretation unit interprets the user's question. The generation unit generates a reassuring word based on the question interpreted by the interpretation unit. The information providing unit returns appropriate information based on the question interpreted by the interpretation unit. The determination unit determines a possible developmental disorder based on the information provided by the information providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for parents to obtain appropriate information to alleviate their concerns about their children's development, which can lead to excessive worry.

[0005] The system according to the embodiment aims to alleviate parents' anxieties about their children's development and provide them with appropriate information. [Means for solving the problem]

[0006] The system according to the embodiment includes an interpretation unit, a generation unit, an information provision unit, and a discrimination unit. The interpretation unit interprets a user's question. The generation unit generates reassuring words based on the question interpreted by the interpretation unit. The information provision unit returns appropriate information based on the question interpreted by the interpretation unit. The discrimination unit discriminates cases in which a developmental disorder is suspected based on the information provided by the information provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can alleviate parents' anxieties about their children's development and provide them with appropriate 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) An AI system according to an embodiment of the present invention provides answers based on accumulated knowledge to parents with concerns about their baby's development. The AI ​​system interprets users' questions, generates reassuring phrases, provides appropriate information, and identifies potential developmental disorders. For example, when a user inputs a question about a baby's development, the AI ​​system interprets the question and generates reassuring phrases. The AI ​​system then provides appropriate information based on online information and expert opinions. Furthermore, the AI ​​system identifies suspected developmental disorders, such as when certain symptoms persist for a certain period of time or when a certain developmental stage has not been reached. This allows the AI ​​system to provide prompt and appropriate answers to parents with concerns about their baby's development. The AI ​​system provides reassurance to parents with concerns about their baby's development and allows them to seek expert advice as needed, enabling appropriate responses.

[0029] The AI ​​system according to the embodiment includes an interpretation unit, a generation unit, an information provision unit, and a discrimination unit. The interpretation unit interprets a user's question. The interpretation unit interprets the user's question using, for example, natural language processing technology. The interpretation unit can also improve the accuracy of the interpretation by referring to the user's past question history. The interpretation unit can also estimate the user's emotions and adjust the way the question is interpreted based on the estimated user's emotions. The generation unit generates reassuring words based on the question interpreted by the interpretation unit. The generation unit can generate reassuring words by referring to, for example, a database of past questions and answers. The generation unit can also estimate the user's emotions and adjust the way the reassuring words are expressed based on the estimated user's emotions. The information provision unit returns optimal information based on the question interpreted by the interpretation unit. The information provision unit can return optimal information by, for example, collecting and analyzing information on the Internet and expert opinions. The information provision unit can also estimate the user's emotions and adjust the way the information is provided based on the estimated user's emotions. The discrimination unit discriminates cases in which a developmental disorder is suspected based on the information provided by the information providing unit. The discrimination unit discriminates cases in which a developmental disorder is suspected, for example, when a specific symptom continues for a certain period of time or when a specific developmental stage has not been reached. The discrimination unit can also estimate the user's emotions and adjust the discrimination criteria based on the estimated user's emotions. This enables the AI ​​system according to the embodiment to appropriately interpret user questions, generate reassuring words, provide optimal information, and discriminate developmental disorders.

[0030] The interpretation unit can interpret the user's 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 interpretation unit uses morphological analysis to break down the user's question into words and analyze the meaning of each word. The interpretation unit can also use grammatical analysis to analyze the grammatical structure of the user's question and understand the meaning of the sentence. The interpretation unit can also use semantic analysis to understand the context of the user's question and interpret the intent of the question. This improves the accuracy of interpreting the user's question using natural language processing technology. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, AI, or may be performed without AI. For example, the interpretation unit can interpret the question using an AI model that receives the user's question as input and outputs an interpretation result.

[0031] The generation unit can generate reassuring words by referring to a database of past questions and answers. The database of past questions and answers includes, but is not limited to, the types of questions and the formats of answers. For example, the generation unit can refer to the database of past questions and answers and generate reassuring words based on past answers to similar questions. The generation unit can also analyze the database of past questions and answers, extract optimal answer patterns, and generate reassuring words. The generation unit can also use the database of past questions and answers to generate appropriate answers to user questions. In this way, referring to the database of past questions and answers improves the accuracy of generating reassuring words. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can generate words using an AI model that inputs the database of past questions and answers and outputs reassuring words.

[0032] The information providing unit can collect and analyze information on the Internet and expert opinions and return appropriate information. Methods for collecting information on the Internet include, but are not limited to, web scraping and API usage. The information providing unit can automatically collect and analyze information on the Internet using, for example, web scraping. The information providing unit can also obtain and analyze information on the Internet using an API. Methods for collecting expert opinions include, but are not limited to, interviews and questionnaires. For example, the information providing unit can interview experts and collect their opinions. The information providing unit can also conduct questionnaires with experts and analyze their responses. By collecting and analyzing information on the Internet and expert opinions, optimal information can be provided. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or without AI. For example, the information providing unit can provide information using an AI model that inputs information on the Internet and expert opinions and outputs appropriate information.

[0033] The discrimination unit can determine whether a developmental disorder is suspected when a specific symptom persists for a certain period of time or when a specific developmental stage has not been reached. Examples of specific symptoms include, but are not limited to, persistent crying and slow development. For example, when a specific symptom persists for one week or more, the discrimination unit can improve the discrimination accuracy for that symptom. Furthermore, when a specific symptom persists for one month or more, the discrimination unit can improve the discrimination accuracy for that symptom. Furthermore, when a specific symptom persists for three months or more, the discrimination unit can improve the discrimination accuracy for that symptom. Examples of specific developmental stages include, but are not limited to, developmental standards by age, behavioral patterns, etc. For example, when a baby has not reached a specific developmental stage, the discrimination unit can improve the discrimination accuracy for that stage. Furthermore, when a baby lacks a specific motor ability, the discrimination unit can improve the discrimination accuracy for that ability. Furthermore, when a baby lacks a specific language ability, the discrimination unit can improve the discrimination accuracy for that ability. This makes it possible to determine whether a developmental disorder is suspected based on specific symptoms or developmental stages. Some or all of the above-mentioned processes in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit may perform discrimination using an AI model that receives data on specific symptoms and developmental stages as input and outputs cases in which developmental disorders are suspected.

[0034] The information providing unit can provide the content of the user's questions to a doctor or specialist to support a smooth hearing. Methods for a smooth hearing include, but are not limited to, for example, the order of questions and the format of the hearing. For example, the information providing unit can provide the content of the user's questions to a doctor or specialist and adjust the order of questions. The information providing unit can also provide the content of the user's questions to a doctor or specialist and adjust the format of the hearing. In this way, providing the content of the user's questions to a doctor or specialist enables a smooth hearing. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI or without AI. For example, the information providing unit can provide information using an AI model that receives the content of the user's questions as input and outputs information to be provided to a doctor or specialist.

[0035] When interpreting a question, the interpretation unit can improve the accuracy of the interpretation by referring to the user's past question history. The user's past question history includes, but is not limited to, for example, the type of question and the method of saving the history. For example, if the user has asked a similar question in the past, the interpretation unit can improve the accuracy of the interpretation by referring to the history. The interpretation unit can also find specific patterns from the user's past question history and reflect them in the interpretation. The interpretation unit can also adjust the direction of the interpretation based on answers the user has received in the past. In this way, the accuracy of the interpretation is improved by referring to the user's past question history. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit can interpret questions using an AI model that inputs the user's past question history and outputs interpretation results.

[0036] When interpreting a question, the interpretation unit can select the optimal interpretation means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs a question by voice, the interpretation unit performs interpretation using voice recognition technology. Furthermore, when the user inputs a question by text, the interpretation unit can also perform interpretation using natural language processing technology. Furthermore, when the user uploads an image, the interpretation unit can also perform interpretation using image analysis technology. This improves the accuracy of interpretation by selecting the optimal interpretation means depending on the user's input method. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit can interpret a question using an AI model that receives user input data as input and outputs an interpretation result.

[0037] The interpretation unit may interpret a question by taking into consideration the user's current situation and background information. Examples of the user's current situation and background information include, but are not limited to, the user's environment and past experiences. For example, if the user inputs a question during a specific time period, the interpretation unit may interpret the question by taking into consideration information related to that time period. Furthermore, if the user inputs a question from a specific location, the interpretation unit may interpret the question by taking into consideration information related to that location. Furthermore, the interpretation unit may interpret the question by taking into consideration the user's background information (e.g., past question history and profile information). This improves the accuracy of the interpretation by taking into consideration the user's current situation and background information. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit may interpret the question using an AI model that receives the user's situation and background information as input and outputs an interpretation result.

[0038] When interpreting a question, the interpretation unit can prioritize a more relevant interpretation by taking into account the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP address. For example, if the user inputs a question from a specific region, the interpretation unit can prioritize interpreting information related to that region. Furthermore, if the user is traveling, the interpretation unit can prioritize interpreting information related to the travel destination. Furthermore, if the user inputs a question from home, the interpretation unit can prioritize interpreting information around the home. This enables a more relevant interpretation by taking the user's geographical location information into account. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit can interpret a question using an AI model that receives the user's geographical location information as input and outputs an interpretation result.

[0039] When interpreting a question, the interpretation unit may analyze the user's social media activity and provide a relevant interpretation. The user's social media activity may include, but is not limited to, the content of posts and the number of followers. The interpretation unit may interpret the question based on, for example, information shared by the user on social media. The interpretation unit may also analyze the user's social media activity history and provide a relevant interpretation. The interpretation unit may also interpret the question by referring to the activities of the user's friends on social media. This enables a relevant interpretation by analyzing the user's social media activity. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit may interpret the question using an AI model that receives the user's social media data as input and outputs an interpretation result.

[0040] When interpreting a question, the interpretation unit can customize the interpretation method by reflecting the user's past feedback. The user's past feedback includes, but is not limited to, evaluation comments, feedback frequency, and the like. The interpretation unit adjusts the interpretation method, for example, based on feedback provided by the user in the past. The interpretation unit can also preferentially use interpretation methods that the user has previously satisfied. The interpretation unit can also avoid interpretation methods that the user has previously dissatisfied with. In this way, the interpretation method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the interpretation unit may be performed, for example, using AI, or may be performed without using AI. For example, the interpretation unit can interpret a question using an AI model that uses the user's past feedback data as input and customizes the interpretation method.

[0041] When generating reassuring words, the generation unit can adjust the level of detail of the generated reassuring words based on the importance of the question. Methods for evaluating the importance of a question include, but are not limited to, the content of the question and urgency. For example, if the question is very important, the generation unit can generate reassuring words that include detailed explanations. Furthermore, if the question is general, the generation unit can generate concise reassuring words. Furthermore, if the question is minor, the generation unit can generate short reassuring words. By adjusting the level of detail of the generated reassuring words based on the importance of the question, more appropriate reassuring words can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate reassuring words using an AI model that uses question importance data as input and adjusts the level of detail of the generated reassuring words.

[0042] When generating reassuring words, the generation unit can apply different generation algorithms depending on the question category. Methods for categorizing questions into categories include, but are not limited to, medical, educational, and technical categories. For example, the generation unit can apply a medical-specialized generation algorithm to a question about health. Furthermore, the generation unit can also apply a development-specialized generation algorithm to a question about development. Furthermore, the generation unit can also apply a childcare-specialized generation algorithm to a question about general childcare. By applying different generation algorithms depending on the question category, more appropriate reassuring words can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate words using an AI model that receives question category data as input and selects a generation algorithm.

[0043] When generating reassuring words, the generation unit can improve the accuracy of the generation by referring to the user's past answer results. The user's past answer results include, for example, but are not limited to, the accuracy of the answer and the user's satisfaction. The generation unit improves the accuracy of generating reassuring words, for example, based on answers the user has received in the past. The generation unit can also analyze the user's past answer history to generate optimal expressions. The generation unit can also generate reassuring words by referring to answers that the user has been satisfied with in the past. This improves the accuracy of generation by referring to the user's past answer results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can generate words using an AI model that improves the accuracy of generation by inputting the user's past answer result data.

[0044] When generating reassuring words, the generation unit can determine the generation priority based on the time when the question was submitted. Methods for evaluating the time when the question was submitted include, but are not limited to, the submission date and time, the submission frequency, and the like. For example, if the question is urgent, the generation unit generates reassuring words as a top priority. Furthermore, if the question is general, the generation unit can generate reassuring words with the same priority as other questions. Furthermore, if the question is minor, the generation unit can generate reassuring words later. Thus, by determining the generation priority based on the time when the question was submitted, reassuring words can be generated at a more appropriate time. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate reassuring words using an AI model that receives data on the time when the question was submitted and determines the generation priority.

[0045] When generating reassuring phrases, the generation unit can adjust the order of generation based on the relevance of the question. Methods for evaluating the relevance of questions include, but are not limited to, similarity in question content, related topics, and the like. For example, if a question is highly relevant, the generation unit generates reassuring phrases as a top priority. Furthermore, if a question is general, the generation unit can generate reassuring phrases in the same order as other questions. Furthermore, if a question is less relevant, the generation unit can generate reassuring phrases later. By adjusting the order of generation based on the relevance of the question, reassuring phrases can be generated in a more appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate phrases using an AI model that receives question relevance data as input and adjusts the order of generation.

[0046] When generating reassuring words, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. Methods for evaluating the user's level of expertise include, but are not limited to, the user's occupation and educational background. For example, if the user has specialized knowledge, the generation unit can generate reassuring words using technical terminology. Furthermore, if the user has general knowledge, the generation unit can generate reassuring words in simple language. Furthermore, if the user is a beginner, the generation unit can generate reassuring words in very simple language. This allows for more appropriate expression by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate reassuring words using an AI model that uses the user's level of expertise as input and adjusts the use of technical terminology.

[0047] When providing information, the information providing unit can adjust the level of detail of the information to be provided based on the importance of the question. Adjustments to the level of detail of the information to be provided include, but are not limited to, the depth and specificity of the information. For example, the information providing unit can provide detailed information if the question is very important. Furthermore, the information providing unit can provide concise information if the question is general. Furthermore, the information providing unit can provide short information if the question is minor. By adjusting the level of detail of the information to be provided based on the importance of the question, more appropriate information can be provided. Some or all of the above-described processing by the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs question importance data and adjusts the level of detail of the information.

[0048] When providing information, the information providing unit can apply different information provision algorithms depending on the category of the question. Types of information provision algorithms include, but are not limited to, recommendation systems and search algorithms. For example, in the case of a question about health, the information providing unit can apply a medical-specialized information provision algorithm. Furthermore, in the case of a question about development, the information providing unit can also apply a development-specialized information provision algorithm. Furthermore, in the case of a question about general childcare, the information providing unit can also apply a childcare-specialized information provision algorithm. In this way, by applying different information provision algorithms depending on the category of the question, more appropriate information can be provided. Some or all of the above-described processing in the information providing unit may be performed, for example, using AI or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs question category data and selects an information provision algorithm.

[0049] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. The user's past information provision results include, but are not limited to, for example, the accuracy of the provided information and the user's satisfaction level. The information providing unit can improve the accuracy of the information provided by, for example, based on the information provision results the user received in the past. The information providing unit can also analyze the user's past information provision history and provide optimal information. The information providing unit can also improve the accuracy of the information provided by referring to information provision results that the user was satisfied with in the past. This improves the accuracy of the information provided by referring to the user's past information provision results. Some or all of the above-described processing by the information providing unit can be performed, for example, using AI or without AI. For example, the information providing unit can provide information using an AI model that uses the user's past information provision result data as input and improves the accuracy of the information provided.

[0050] When providing information, the information providing unit can determine the priority of the information to be provided based on the time when the question was submitted. Methods for evaluating the time when the question was submitted include, but are not limited to, the submission date and time, the submission frequency, etc. For example, if the question is urgent, the information providing unit can provide the information with the highest priority. Furthermore, if the question is general, the information providing unit can also provide the information with the same priority as other questions. Furthermore, if the question is minor, the information providing unit can provide the information later. In this way, by determining the priority of the information to be provided based on the time when the question was submitted, it is possible to provide the information at a more appropriate time. Some or all of the above-mentioned processing by the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide the information using an AI model that inputs data on the time when the question was submitted and determines the priority of the information.

[0051] When providing information, the information providing unit can adjust the order of information to be provided based on the relevance of the question. Adjustments to the order of information to be provided include, but are not limited to, the relevance and importance of the information. For example, if a question is highly relevant, the information providing unit can provide the information with the highest priority. Furthermore, if the question is general, the information providing unit can also provide the information in the same order as other questions. Furthermore, if the question is less relevant, the information providing unit can provide the information later. By adjusting the order of information to be provided based on the relevance of the question, it is possible to provide information in a more appropriate order. Some or all of the above-described processing by the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that uses question relevance data as input and adjusts the order of information.

[0052] When providing information, the information providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise. Adjustment of the use of technical terms includes, but is not limited to, definitions and frequency of use of technical terms. For example, if the user has specialized knowledge, the information providing unit can provide information using technical terms. Furthermore, if the user has general knowledge, the information providing unit can provide information in simple language. Furthermore, if the user is a beginner, the information providing unit can provide information in very simple language. This allows for more appropriate information provision by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without AI. For example, the information providing unit can provide information using an AI model that uses the user's level of expertise data as input and adjusts the use of technical terms.

[0053] The discrimination unit can improve the discrimination accuracy when a specific symptom continues for a certain period of time during discrimination. Criteria for discriminating a specific symptom include, but are not limited to, the type and duration of the symptom. For example, the discrimination unit improves the discrimination accuracy for a specific symptom when the specific symptom continues for one week or more. The discrimination unit can also improve the discrimination accuracy for a specific symptom when the specific symptom continues for one month or more. The discrimination unit can also improve the discrimination accuracy for a specific symptom when the specific symptom continues for three months or more. This improves the discrimination accuracy when the specific symptom continues for a certain period of time, enabling more appropriate discrimination. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input specific symptom data and perform discrimination using an AI model that improves discrimination accuracy.

[0054] The discrimination unit can improve the accuracy of discrimination when a specific developmental stage has not been reached at the time of discrimination. Criteria for discriminating a specific developmental stage include, but are not limited to, developmental standards by age, behavioral patterns, etc. For example, if a baby has not reached a specific developmental stage, the discrimination unit improves the accuracy of discrimination for that stage. Furthermore, if a baby does not have a specific motor ability, the discrimination unit can also improve the accuracy of discrimination for that ability. Furthermore, if a baby does not have a specific language ability, the discrimination unit can also improve the accuracy of discrimination for that ability. By improving the accuracy of discrimination when a specific developmental stage has not been reached, more appropriate discrimination becomes possible. Some or all of the above-described processing in the discrimination unit may be performed, for example, using AI, or may be performed without using AI. For example, the discrimination unit can input specific developmental stage data and perform discrimination using an AI model that improves discrimination accuracy.

[0055] The discrimination unit can improve the accuracy of discrimination by referring to the user's past question history during discrimination. The user's past question history includes, but is not limited to, for example, the type of question and the method of saving the history. For example, if the user has asked a similar question in the past, the discrimination unit can improve the accuracy of discrimination by referring to the history. The discrimination unit can also find specific patterns from the user's past question history and reflect them in the discrimination. The discrimination unit can also adjust the direction of discrimination based on answers the user has received in the past. In this way, the accuracy of discrimination is improved by referring to the user's past question history. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the user's past question history data and perform discrimination using an AI model that improves the accuracy of discrimination.

[0056] When making a determination, the determination unit can prioritize highly relevant determinations by taking into account the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP address. For example, if the user inputs a question from a specific region, the determination unit prioritizes determining information related to that region. Furthermore, if the user is traveling, the determination unit can prioritize determining information related to the travel destination. Furthermore, if the user inputs a question from home, the determination unit can prioritize determining information around the home. This enables highly relevant determinations by taking the user's geographical location information into account. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can perform the determination using an AI model that receives the user's geographical location information as input and outputs a determination result.

[0057] During the determination, the determination unit can analyze the user's social media activity and make a relevant determination. The user's social media activity includes, for example, but is not limited to, the content of posts and the number of followers. The determination unit, for example, determines the question based on information shared by the user on social media. The determination unit can also analyze the user's social media activity history and make a relevant determination. The determination unit can also determine the question with reference to the activities of the user's friends on social media. In this way, relevant determination is possible by analyzing the user's social media activity. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can perform the determination using an AI model that inputs the user's social media data and outputs a determination result.

[0058] The discrimination unit can customize the discrimination method by reflecting the user's past feedback when making a discrimination. The user's past feedback includes, but is not limited to, evaluation comments, feedback frequency, and the like. The discrimination unit adjusts the discrimination method, for example, based on feedback provided by the user in the past. The discrimination unit can also preferentially use discrimination methods that the user has previously satisfied. The discrimination unit can also avoid discrimination methods that the user has previously dissatisfied with. In this way, the discrimination method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can perform discrimination using an AI model that uses the user's past feedback data as input and customizes the discrimination method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The AI ​​system may further include a reliability evaluation unit that evaluates the reliability of answers to user questions. The reliability evaluation unit may evaluate, for example, the reliability of the source of the provided information or the reliability of expert opinions, and provide the user with more reliable information. The reliability evaluation unit may also evaluate the reliability of information on the Internet and eliminate less reliable information. Furthermore, the reliability evaluation unit may adjust the reliability evaluation criteria based on the user's past feedback. This allows the AI ​​system to provide the user with more reliable information.

[0061] When interpreting a user's question, the interpretation unit can improve the accuracy of the interpretation by taking into account the context of the question. For example, the interpretation unit can refer to the context of questions previously asked by the user to more accurately interpret the intention of the current question. The interpretation unit can also prioritize interpretation of related information based on the context of the question. Furthermore, the interpretation unit can adjust the direction of interpretation based on the context of the question. In this way, the interpretation unit can improve the accuracy of interpretation by taking into account the context of the question.

[0062] When providing an answer to a user's question, the information providing unit can adjust the visual representation of the answer. For example, if the user prefers visual information, the information providing unit can provide the information using graphs or charts. If the user prefers text information, the information providing unit can also provide the information in detailed text. Furthermore, if the user prefers videos, the information providing unit can also provide the information in video format. This allows the information providing unit to provide more appropriate information by adjusting the visual representation according to the user's preferences.

[0063] The determination unit can evaluate the reliability of the answer when providing an answer to a user's question. For example, the determination unit evaluates the reliability of the source of the provided information or the opinion of an expert, and provides the user with highly reliable information. The determination unit can also evaluate the reliability of information on the Internet and eliminate unreliable information. Furthermore, the determination unit can adjust the reliability evaluation criteria based on the user's past feedback. This allows the determination unit to provide the user with more reliable information.

[0064] When providing an answer to a user's question, the information providing unit can adjust the visual representation of the answer. For example, if the user prefers visual information, the information providing unit can provide the information using graphs or charts. If the user prefers text information, the information providing unit can also provide the information in detailed text. Furthermore, if the user prefers videos, the information providing unit can also provide the information in video format. This allows the information providing unit to provide more appropriate information by adjusting the visual representation according to the user's preferences.

[0065] The interpretation unit can improve the accuracy of the interpretation by referring to the user's past question history. For example, if the user has asked a similar question in the past, the interpretation unit can improve the accuracy of the interpretation by referring to that history. The interpretation unit can also find specific patterns from the user's past question history and reflect them in the interpretation. Furthermore, the interpretation unit can adjust the direction of the interpretation based on answers the user has received in the past. In this way, the interpretation unit can improve the accuracy of the interpretation by referring to the user's past question history.

[0066] When interpreting a question, the interpretation unit can select the most appropriate interpretation means depending on the user's input method. For example, if the user inputs a question by voice, the interpretation unit performs the interpretation using voice recognition technology. Also, if the user inputs a question in text, the interpretation unit can perform the interpretation using natural language processing technology. Furthermore, if the user uploads an image, the interpretation unit can perform the interpretation using image analysis technology. In this way, the interpretation unit can improve the accuracy of interpretation by selecting the most appropriate interpretation means depending on the user's input method.

[0067] When interpreting a question, the interpretation unit can take into account the user's current situation and background information. For example, if a user inputs a question during a specific time period, the interpretation unit can take into account information related to that time period. Also, if a user inputs a question from a specific location, the interpretation unit can take into account information related to that location. Furthermore, the interpretation unit can take into account the user's background information (e.g., past question history and profile information) when interpreting the question. This allows the interpretation unit to improve the accuracy of interpretation by taking into account the user's current situation and background information.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The interpretation unit interprets the user's question. The interpretation unit may use, for example, natural language processing technology to interpret the user's question. The interpretation unit may also improve the accuracy of the interpretation by referring to the user's past question history. Furthermore, the interpretation unit may estimate the user's emotions and adjust the method of interpreting the question based on the estimated user emotions. Step 2: The generator generates reassuring words based on the question interpreted by the interpreter. For example, the generator generates reassuring words by referring to a database of past questions and answers. The generator can also estimate the user's emotions and adjust the way the reassuring words are expressed based on the estimated user's emotions. Step 3: The information providing unit returns the most appropriate information based on the question interpreted by the interpretation unit. For example, the information providing unit collects and analyzes information on the Internet and expert opinions to return the most appropriate information. The information providing unit can also estimate the user's emotions and adjust the way it provides information based on the estimated user emotions. Step 4: The discrimination unit discriminates cases in which a developmental disorder is suspected based on the information provided by the information providing unit. The discrimination unit discriminates cases in which a developmental disorder is suspected, for example, when a specific symptom continues for a certain period of time or when a specific developmental stage has not been reached. The discrimination unit can also estimate the user's emotions and adjust the criteria for discrimination based on the estimated user's emotions.

[0070] (Example 2) An AI system according to an embodiment of the present invention provides answers based on accumulated knowledge to parents with concerns about their baby's development. The AI ​​system interprets users' questions, generates reassuring phrases, provides appropriate information, and identifies potential developmental disorders. For example, when a user inputs a question about a baby's development, the AI ​​system interprets the question and generates reassuring phrases. The AI ​​system then provides appropriate information based on online information and expert opinions. Furthermore, the AI ​​system identifies suspected developmental disorders, such as when certain symptoms persist for a certain period of time or when a certain developmental stage has not been reached. This allows the AI ​​system to provide prompt and appropriate answers to parents with concerns about their baby's development. The AI ​​system provides reassurance to parents with concerns about their baby's development and allows them to seek expert advice as needed, enabling appropriate responses.

[0071] The AI ​​system according to the embodiment includes an interpretation unit, a generation unit, an information provision unit, and a discrimination unit. The interpretation unit interprets a user's question. The interpretation unit interprets the user's question using, for example, natural language processing technology. The interpretation unit can also improve the accuracy of the interpretation by referring to the user's past question history. The interpretation unit can also estimate the user's emotions and adjust the way the question is interpreted based on the estimated user's emotions. The generation unit generates reassuring words based on the question interpreted by the interpretation unit. The generation unit can generate reassuring words by referring to, for example, a database of past questions and answers. The generation unit can also estimate the user's emotions and adjust the way the reassuring words are expressed based on the estimated user's emotions. The information provision unit returns optimal information based on the question interpreted by the interpretation unit. The information provision unit can return optimal information by, for example, collecting and analyzing information on the Internet and expert opinions. The information provision unit can also estimate the user's emotions and adjust the way the information is provided based on the estimated user's emotions. The discrimination unit discriminates cases in which a developmental disorder is suspected based on the information provided by the information providing unit. The discrimination unit discriminates cases in which a developmental disorder is suspected, for example, when a specific symptom continues for a certain period of time or when a specific developmental stage has not been reached. The discrimination unit can also estimate the user's emotions and adjust the discrimination criteria based on the estimated user's emotions. This enables the AI ​​system according to the embodiment to appropriately interpret user questions, generate reassuring words, provide optimal information, and discriminate developmental disorders.

[0072] The interpretation unit can interpret the user's 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 interpretation unit uses morphological analysis to break down the user's question into words and analyze the meaning of each word. The interpretation unit can also use grammatical analysis to analyze the grammatical structure of the user's question and understand the meaning of the sentence. The interpretation unit can also use semantic analysis to understand the context of the user's question and interpret the intent of the question. This improves the accuracy of interpreting the user's question using natural language processing technology. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, AI, or may be performed without AI. For example, the interpretation unit can interpret the question using an AI model that receives the user's question as input and outputs an interpretation result.

[0073] The generation unit can generate reassuring words by referring to a database of past questions and answers. The database of past questions and answers includes, but is not limited to, the types of questions and the formats of answers. For example, the generation unit can refer to the database of past questions and answers and generate reassuring words based on past answers to similar questions. The generation unit can also analyze the database of past questions and answers, extract optimal answer patterns, and generate reassuring words. The generation unit can also use the database of past questions and answers to generate appropriate answers to user questions. In this way, referring to the database of past questions and answers improves the accuracy of generating reassuring words. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can generate words using an AI model that inputs the database of past questions and answers and outputs reassuring words.

[0074] The information providing unit can collect and analyze information on the Internet and expert opinions and return appropriate information. Methods for collecting information on the Internet include, but are not limited to, web scraping and API usage. The information providing unit can automatically collect and analyze information on the Internet using, for example, web scraping. The information providing unit can also obtain and analyze information on the Internet using an API. Methods for collecting expert opinions include, but are not limited to, interviews and questionnaires. For example, the information providing unit can interview experts and collect their opinions. The information providing unit can also conduct questionnaires with experts and analyze their responses. By collecting and analyzing information on the Internet and expert opinions, optimal information can be provided. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or without AI. For example, the information providing unit can provide information using an AI model that inputs information on the Internet and expert opinions and outputs appropriate information.

[0075] The discrimination unit can determine whether a developmental disorder is suspected when a specific symptom persists for a certain period of time or when a specific developmental stage has not been reached. Examples of specific symptoms include, but are not limited to, persistent crying and slow development. For example, when a specific symptom persists for one week or more, the discrimination unit can improve the discrimination accuracy for that symptom. Furthermore, when a specific symptom persists for one month or more, the discrimination unit can improve the discrimination accuracy for that symptom. Furthermore, when a specific symptom persists for three months or more, the discrimination unit can improve the discrimination accuracy for that symptom. Examples of specific developmental stages include, but are not limited to, developmental standards by age, behavioral patterns, etc. For example, when a baby has not reached a specific developmental stage, the discrimination unit can improve the discrimination accuracy for that stage. Furthermore, when a baby lacks a specific motor ability, the discrimination unit can improve the discrimination accuracy for that ability. Furthermore, when a baby lacks a specific language ability, the discrimination unit can improve the discrimination accuracy for that ability. This makes it possible to determine whether a developmental disorder is suspected based on specific symptoms or developmental stages. Some or all of the above-mentioned processes in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit may perform discrimination using an AI model that receives data on specific symptoms and developmental stages as input and outputs cases in which developmental disorders are suspected.

[0076] The information providing unit can provide the content of the user's questions to a doctor or specialist to support a smooth hearing. Methods for a smooth hearing include, but are not limited to, for example, the order of questions and the format of the hearing. For example, the information providing unit can provide the content of the user's questions to a doctor or specialist and adjust the order of questions. The information providing unit can also provide the content of the user's questions to a doctor or specialist and adjust the format of the hearing. In this way, providing the content of the user's questions to a doctor or specialist enables a smooth hearing. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI or without AI. For example, the information providing unit can provide information using an AI model that receives the content of the user's questions as input and outputs information to be provided to a doctor or specialist.

[0077] The interpretation unit can estimate the user's emotions and adjust the question interpretation method based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, emotion analysis algorithms and facial expression recognition technology. The interpretation unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The interpretation unit can also estimate the user's emotions from the user's facial expressions using facial expression recognition technology. For example, if the user is feeling anxious, the interpretation unit can interpret the question in a gentle tone to provide a sense of security. If the user is feeling impatient, the interpretation unit can quickly interpret the question and generate an answer immediately. If the user is relaxed, the interpretation unit can provide a detailed interpretation and provide more in-depth information. This allows for more appropriate interpretation by adjusting the question interpretation method based on the user's emotions. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or without AI. For example, the interpretation unit can interpret the question using an AI model that inputs user emotion data and adjusts the interpretation method.

[0078] When interpreting a question, the interpretation unit can improve the accuracy of the interpretation by referring to the user's past question history. The user's past question history includes, but is not limited to, for example, the type of question and the method of saving the history. For example, if the user has asked a similar question in the past, the interpretation unit can improve the accuracy of the interpretation by referring to the history. The interpretation unit can also find specific patterns from the user's past question history and reflect them in the interpretation. The interpretation unit can also adjust the direction of the interpretation based on answers the user has received in the past. In this way, the accuracy of the interpretation is improved by referring to the user's past question history. Some or all of the above-mentioned processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit can interpret questions using an AI model that inputs the user's past question history and outputs interpretation results.

[0079] When interpreting a question, the interpretation unit can select the optimal interpretation means depending on the user's input method. User input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs a question by voice, the interpretation unit performs interpretation using voice recognition technology. Furthermore, when the user inputs a question by text, the interpretation unit can also perform interpretation using natural language processing technology. Furthermore, when the user uploads an image, the interpretation unit can also perform interpretation using image analysis technology. This improves the accuracy of interpretation by selecting the optimal interpretation means depending on the user's input method. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit can interpret a question using an AI model that receives user input data as input and outputs an interpretation result.

[0080] The interpretation unit may interpret a question by taking into consideration the user's current situation and background information. Examples of the user's current situation and background information include, but are not limited to, the user's environment and past experiences. For example, if the user inputs a question during a specific time period, the interpretation unit may interpret the question by taking into consideration information related to that time period. Furthermore, if the user inputs a question from a specific location, the interpretation unit may interpret the question by taking into consideration information related to that location. Furthermore, the interpretation unit may interpret the question by taking into consideration the user's background information (e.g., past question history and profile information). This improves the accuracy of the interpretation by taking into consideration the user's current situation and background information. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit may interpret the question using an AI model that receives the user's situation and background information as input and outputs an interpretation result.

[0081] The interpretation unit can estimate the user's emotions and prioritize interpretation results based on the estimated user emotions. Methods for determining the priority of interpretation results include, but are not limited to, the intensity of the emotion and the urgency of the question. For example, if the user is feeling strong anxiety, the interpretation unit can interpret the question with the highest priority and quickly generate an answer. Furthermore, if the user is feeling mild anxiety, the interpretation unit can interpret the question with the same priority as other questions. Furthermore, if the user is relaxed, the interpretation unit can provide a detailed interpretation and postpone it over other questions. This enables more appropriate interpretation by prioritizing interpretation results based on the user's emotions. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or without AI. For example, the interpretation unit can interpret questions using an AI model that inputs user emotion data and prioritizes interpretation results.

[0082] When interpreting a question, the interpretation unit can prioritize a more relevant interpretation by taking into account the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP address. For example, if the user inputs a question from a specific region, the interpretation unit can prioritize interpreting information related to that region. Furthermore, if the user is traveling, the interpretation unit can prioritize interpreting information related to the travel destination. Furthermore, if the user inputs a question from home, the interpretation unit can prioritize interpreting information around the home. This enables a more relevant interpretation by taking the user's geographical location information into account. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit can interpret a question using an AI model that receives the user's geographical location information as input and outputs an interpretation result.

[0083] When interpreting a question, the interpretation unit may analyze the user's social media activity and provide a relevant interpretation. The user's social media activity may include, but is not limited to, the content of posts and the number of followers. The interpretation unit may interpret the question based on, for example, information shared by the user on social media. The interpretation unit may also analyze the user's social media activity history and provide a relevant interpretation. The interpretation unit may also interpret the question by referring to the activities of the user's friends on social media. This enables a relevant interpretation by analyzing the user's social media activity. Some or all of the above-described processing in the interpretation unit may be performed using, for example, AI, or may be performed without using AI. For example, the interpretation unit may interpret the question using an AI model that receives the user's social media data as input and outputs an interpretation result.

[0084] When interpreting a question, the interpretation unit can customize the interpretation method by reflecting the user's past feedback. The user's past feedback includes, but is not limited to, evaluation comments, feedback frequency, and the like. The interpretation unit adjusts the interpretation method, for example, based on feedback provided by the user in the past. The interpretation unit can also preferentially use interpretation methods that the user has previously satisfied. The interpretation unit can also avoid interpretation methods that the user has previously dissatisfied with. In this way, the interpretation method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the interpretation unit may be performed, for example, using AI, or may be performed without using AI. For example, the interpretation unit can interpret a question using an AI model that uses the user's past feedback data as input and customizes the interpretation method.

[0085] The generation unit can estimate the user's emotions and adjust the reassuring words used based on the estimated user's emotions. Examples of adjusting the reassuring words include, but are not limited to, adjusting the wording and writing style. For example, if the user is feeling anxious, the generation unit can generate reassuring words using gentle language. If the user is feeling anxious, the generation unit can also generate reassuring words using quick and concise language. If the user is feeling relaxed, the generation unit can also generate reassuring words using detailed explanations. This allows for more appropriate reassuring expressions by adjusting the reassuring words used based on the user's emotions. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate reassuring words using an AI model that uses user emotion data as input and adjusts the reassuring words used.

[0086] When generating reassuring words, the generation unit can adjust the level of detail of the generated reassuring words based on the importance of the question. Methods for evaluating the importance of a question include, but are not limited to, the content of the question and urgency. For example, if the question is very important, the generation unit can generate reassuring words that include detailed explanations. Furthermore, if the question is general, the generation unit can generate concise reassuring words. Furthermore, if the question is minor, the generation unit can generate short reassuring words. By adjusting the level of detail of the generated reassuring words based on the importance of the question, more appropriate reassuring words can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate reassuring words using an AI model that uses question importance data as input and adjusts the level of detail of the generated reassuring words.

[0087] When generating reassuring words, the generation unit can apply different generation algorithms depending on the question category. Methods for categorizing questions into categories include, but are not limited to, medical, educational, and technical categories. For example, the generation unit can apply a medical-specialized generation algorithm to a question about health. Furthermore, the generation unit can also apply a development-specialized generation algorithm to a question about development. Furthermore, the generation unit can also apply a childcare-specialized generation algorithm to a question about general childcare. By applying different generation algorithms depending on the question category, more appropriate reassuring words can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate words using an AI model that receives question category data as input and selects a generation algorithm.

[0088] When generating reassuring words, the generation unit can improve the accuracy of the generation by referring to the user's past answer results. The user's past answer results include, for example, but are not limited to, the accuracy of the answer and the user's satisfaction. The generation unit improves the accuracy of generating reassuring words, for example, based on answers the user has received in the past. The generation unit can also analyze the user's past answer history to generate optimal expressions. The generation unit can also generate reassuring words by referring to answers that the user has been satisfied with in the past. This improves the accuracy of generation by referring to the user's past answer results. Some or all of the above-described processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can generate words using an AI model that improves the accuracy of generation by inputting the user's past answer result data.

[0089] The generation unit can estimate the user's emotions and adjust the length of the reassuring words based on the estimated user emotions. Examples of adjustments to the length of the reassuring words include, but are not limited to, the length of the sentence and the level of detail of the information. For example, if the user is feeling anxious, the generation unit can generate longer reassuring words. Furthermore, if the user is feeling anxious, the generation unit can generate short, to-the-point reassuring words. Furthermore, if the user is feeling relaxed, the generation unit can generate reassuring words that include detailed explanations. This allows for more appropriate expression by adjusting the length of the reassuring words based on the user's emotions. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate words using an AI model that uses user emotion data as input and adjusts the length of words.

[0090] When generating reassuring words, the generation unit can determine the generation priority based on the time when the question was submitted. Methods for evaluating the time when the question was submitted include, but are not limited to, the submission date and time, the submission frequency, and the like. For example, if the question is urgent, the generation unit generates reassuring words as a top priority. Furthermore, if the question is general, the generation unit can generate reassuring words with the same priority as other questions. Furthermore, if the question is minor, the generation unit can generate reassuring words later. Thus, by determining the generation priority based on the time when the question was submitted, reassuring words can be generated at a more appropriate time. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate reassuring words using an AI model that receives data on the time when the question was submitted and determines the generation priority.

[0091] When generating reassuring phrases, the generation unit can adjust the order of generation based on the relevance of the question. Methods for evaluating the relevance of questions include, but are not limited to, similarity in question content, related topics, and the like. For example, if a question is highly relevant, the generation unit generates reassuring phrases as a top priority. Furthermore, if a question is general, the generation unit can generate reassuring phrases in the same order as other questions. Furthermore, if a question is less relevant, the generation unit can generate reassuring phrases later. By adjusting the order of generation based on the relevance of the question, reassuring phrases can be generated in a more appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate phrases using an AI model that receives question relevance data as input and adjusts the order of generation.

[0092] When generating reassuring words, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. Methods for evaluating the user's level of expertise include, but are not limited to, the user's occupation and educational background. For example, if the user has specialized knowledge, the generation unit can generate reassuring words using technical terminology. Furthermore, if the user has general knowledge, the generation unit can generate reassuring words in simple language. Furthermore, if the user is a beginner, the generation unit can generate reassuring words in very simple language. This allows for more appropriate expression by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can generate reassuring words using an AI model that uses the user's level of expertise as input and adjusts the use of technical terminology.

[0093] The information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. Examples of the information providing method include, but are not limited to, text, audio, and video. For example, if the user is feeling anxious, the information providing unit can provide information in a gentle tone. Furthermore, if the user is feeling anxious, the information providing unit can provide quick and concise information. Furthermore, if the user is feeling relaxed, the information providing unit can provide detailed information. By adjusting the method of providing information based on the user's emotions, more appropriate information can be provided. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs user emotion data and adjusts the method of providing information.

[0094] When providing information, the information providing unit can adjust the level of detail of the information to be provided based on the importance of the question. Adjustments to the level of detail of the information to be provided include, but are not limited to, the depth and specificity of the information. For example, the information providing unit can provide detailed information if the question is very important. Furthermore, the information providing unit can provide concise information if the question is general. Furthermore, the information providing unit can provide short information if the question is minor. By adjusting the level of detail of the information to be provided based on the importance of the question, more appropriate information can be provided. Some or all of the above-described processing by the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs question importance data and adjusts the level of detail of the information.

[0095] When providing information, the information providing unit can apply different information provision algorithms depending on the category of the question. Types of information provision algorithms include, but are not limited to, recommendation systems and search algorithms. For example, in the case of a question about health, the information providing unit can apply a medical-specialized information provision algorithm. Furthermore, in the case of a question about development, the information providing unit can also apply a development-specialized information provision algorithm. Furthermore, in the case of a question about general childcare, the information providing unit can also apply a childcare-specialized information provision algorithm. In this way, by applying different information provision algorithms depending on the category of the question, more appropriate information can be provided. Some or all of the above-described processing in the information providing unit may be performed, for example, using AI or may be performed without using AI. For example, the information providing unit can provide information using an AI model that inputs question category data and selects an information provision algorithm.

[0096] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. The user's past information provision results include, but are not limited to, for example, the accuracy of the provided information and the user's satisfaction level. The information providing unit can improve the accuracy of the information provided by, for example, based on the information provision results the user received in the past. The information providing unit can also analyze the user's past information provision history and provide optimal information. The information providing unit can also improve the accuracy of the information provided by referring to information provision results that the user was satisfied with in the past. This improves the accuracy of the information provided by referring to the user's past information provision results. Some or all of the above-described processing by the information providing unit can be performed, for example, using AI or without AI. For example, the information providing unit can provide information using an AI model that uses the user's past information provision result data as input and improves the accuracy of the information provided.

[0097] The information providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. Methods for determining the priority of information to be provided include, but are not limited to, the importance and urgency of the information. For example, if the user is feeling strong anxiety, the information providing unit can provide information related to that question with the highest priority. Furthermore, if the user is feeling mild anxiety, the information providing unit can provide information with the same priority as other questions. Furthermore, if the user is relaxed, the information providing unit can provide detailed information and postpone other questions. This enables more appropriate information to be provided by determining the priority of information to be provided based on the user's emotions. Some or all of the above-described processing by the information providing unit may be performed using, for example, AI, or may be performed without AI. For example, the information providing unit can provide information using an AI model that receives user emotion data as input and determines the priority of information.

[0098] When providing information, the information providing unit can determine the priority of the information to be provided based on the time when the question was submitted. Methods for evaluating the time when the question was submitted include, but are not limited to, the submission date and time, the submission frequency, etc. For example, if the question is urgent, the information providing unit can provide the information with the highest priority. Furthermore, if the question is general, the information providing unit can also provide the information with the same priority as other questions. Furthermore, if the question is minor, the information providing unit can provide the information later. In this way, by determining the priority of the information to be provided based on the time when the question was submitted, it is possible to provide the information at a more appropriate time. Some or all of the above-mentioned processing by the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide the information using an AI model that inputs data on the time when the question was submitted and determines the priority of the information.

[0099] When providing information, the information providing unit can adjust the order of information to be provided based on the relevance of the question. Adjustments to the order of information to be provided include, but are not limited to, the relevance and importance of the information. For example, if a question is highly relevant, the information providing unit can provide the information with the highest priority. Furthermore, if the question is general, the information providing unit can also provide the information in the same order as other questions. Furthermore, if the question is less relevant, the information providing unit can provide the information later. By adjusting the order of information to be provided based on the relevance of the question, it is possible to provide information in a more appropriate order. Some or all of the above-described processing by the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can provide information using an AI model that uses question relevance data as input and adjusts the order of information.

[0100] When providing information, the information providing unit can adjust the use of technical terms in the information to be provided according to the user's level of expertise. Adjustment of the use of technical terms includes, but is not limited to, definitions and frequency of use of technical terms. For example, if the user has specialized knowledge, the information providing unit can provide information using technical terms. Furthermore, if the user has general knowledge, the information providing unit can provide information in simple language. Furthermore, if the user is a beginner, the information providing unit can provide information in very simple language. This allows for more appropriate information provision by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without AI. For example, the information providing unit can provide information using an AI model that uses the user's level of expertise data as input and adjusts the use of technical terms.

[0101] The discrimination unit can estimate the user's emotions and adjust the discrimination criteria based on the estimated user's emotions. Examples of adjustment of the discrimination criteria include, but are not limited to, the type and duration of symptoms. For example, the discrimination unit can make a discrimination using strict criteria when the user is feeling strong anxiety. Furthermore, the discrimination unit can also make a discrimination using normal criteria when the user is feeling mild anxiety. Furthermore, the discrimination unit can also make a discrimination using flexible criteria when the user is relaxed. This allows for more appropriate discrimination by adjusting the discrimination criteria based on the user's emotions. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can make a discrimination using an AI model that inputs the user's emotion data and adjusts the discrimination criteria.

[0102] The discrimination unit can improve the discrimination accuracy when a specific symptom continues for a certain period of time during discrimination. Criteria for discriminating a specific symptom include, but are not limited to, the type and duration of the symptom. For example, the discrimination unit improves the discrimination accuracy for a specific symptom when the specific symptom continues for one week or more. The discrimination unit can also improve the discrimination accuracy for a specific symptom when the specific symptom continues for one month or more. The discrimination unit can also improve the discrimination accuracy for a specific symptom when the specific symptom continues for three months or more. This improves the discrimination accuracy when the specific symptom continues for a certain period of time, enabling more appropriate discrimination. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input specific symptom data and perform discrimination using an AI model that improves discrimination accuracy.

[0103] The discrimination unit can improve the accuracy of discrimination when a specific developmental stage has not been reached at the time of discrimination. Criteria for discriminating a specific developmental stage include, but are not limited to, developmental standards by age, behavioral patterns, etc. For example, if a baby has not reached a specific developmental stage, the discrimination unit improves the accuracy of discrimination for that stage. Furthermore, if a baby does not have a specific motor ability, the discrimination unit can also improve the accuracy of discrimination for that ability. Furthermore, if a baby does not have a specific language ability, the discrimination unit can also improve the accuracy of discrimination for that ability. By improving the accuracy of discrimination when a specific developmental stage has not been reached, more appropriate discrimination becomes possible. Some or all of the above-described processing in the discrimination unit may be performed, for example, using AI, or may be performed without using AI. For example, the discrimination unit can input specific developmental stage data and perform discrimination using an AI model that improves discrimination accuracy.

[0104] The discrimination unit can improve the accuracy of discrimination by referring to the user's past question history during discrimination. The user's past question history includes, but is not limited to, for example, the type of question and the method of saving the history. For example, if the user has asked a similar question in the past, the discrimination unit can improve the accuracy of discrimination by referring to the history. The discrimination unit can also find specific patterns from the user's past question history and reflect them in the discrimination. The discrimination unit can also adjust the direction of discrimination based on answers the user has received in the past. In this way, the accuracy of discrimination is improved by referring to the user's past question history. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the user's past question history data and perform discrimination using an AI model that improves the accuracy of discrimination.

[0105] The discrimination unit can estimate the user's emotions and determine the priority of the discrimination results based on the estimated user emotions. Methods for determining the priority of the discrimination results include, but are not limited to, the severity and urgency of the symptoms. For example, if the user is feeling strong anxiety, the discrimination unit can provide a discrimination result related to that question with the highest priority. Furthermore, if the user is feeling mild anxiety, the discrimination unit can provide a discrimination result with the same priority as other questions. Furthermore, if the user is relaxed, the discrimination unit can provide a detailed discrimination result and postpone the other questions. Thus, by determining the priority of the discrimination results based on the user's emotions, more appropriate discrimination is possible. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without AI. For example, the discrimination unit can perform discrimination using an AI model that receives user emotion data as input and determines the priority of the discrimination results.

[0106] When making a determination, the determination unit can prioritize highly relevant determinations by taking into account the user's geographical location information. Examples of the user's geographical location information include, but are not limited to, GPS data and IP address. For example, if the user inputs a question from a specific region, the determination unit prioritizes determining information related to that region. Furthermore, if the user is traveling, the determination unit can prioritize determining information related to the travel destination. Furthermore, if the user inputs a question from home, the determination unit can prioritize determining information around the home. This enables highly relevant determinations by taking the user's geographical location information into account. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can perform the determination using an AI model that receives the user's geographical location information as input and outputs a determination result.

[0107] During the determination, the determination unit can analyze the user's social media activity and make a relevant determination. The user's social media activity includes, for example, but is not limited to, the content of posts and the number of followers. The determination unit, for example, determines the question based on information shared by the user on social media. The determination unit can also analyze the user's social media activity history and make a relevant determination. The determination unit can also determine the question with reference to the activities of the user's friends on social media. In this way, relevant determination is possible by analyzing the user's social media activity. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can perform the determination using an AI model that inputs the user's social media data and outputs a determination result.

[0108] The discrimination unit can customize the discrimination method by reflecting the user's past feedback when making a discrimination. The user's past feedback includes, but is not limited to, evaluation comments, feedback frequency, and the like. The discrimination unit adjusts the discrimination method, for example, based on feedback provided by the user in the past. The discrimination unit can also preferentially use discrimination methods that the user has previously satisfied. The discrimination unit can also avoid discrimination methods that the user has previously dissatisfied with. In this way, the discrimination method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can perform discrimination using an AI model that uses the user's past feedback data as input and customizes the discrimination method. === Hard Collateral 1-1 === Each of the multiple elements including the interpretation unit, generation unit, information provision unit, and discrimination unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the interpretation unit is realized by the control unit 46A of the smart device 14 and interprets a user's question. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates reassuring words. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal information. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and discriminates cases in which a developmental disorder is suspected. === Hard Collateral 1-2 === Each of the multiple elements including the interpretation unit, generation unit, information provision unit, and discrimination 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 interpretation unit is realized by the control unit 46A of the smart glasses 214 and interprets a user's question. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates reassuring words. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal information. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and discriminates cases in which a developmental disorder is suspected. === Hard Collateral 1-3 === Each of the multiple elements including the interpretation unit, generation unit, information provision unit, and discrimination 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 interpretation unit is realized by the control unit 46A of the headset type terminal 314 and interprets the user's question. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates reassuring words. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal information. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and discriminates cases in which a developmental disorder is suspected. === Hard Collateral 1-4 === Each of the multiple elements including the interpretation unit, generation unit, information provision unit, and discrimination unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the interpretation unit is realized by the control unit 46A of the robot 414 and interprets the user's question. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates reassuring words. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides optimal information. The discrimination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and discriminates cases in which a developmental disorder is suspected.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The AI ​​system may further include a reliability evaluation unit that evaluates the reliability of answers to user questions. The reliability evaluation unit may evaluate, for example, the reliability of the source of the provided information or the reliability of expert opinions, and provide the user with more reliable information. The reliability evaluation unit may also evaluate the reliability of information on the Internet and eliminate less reliable information. Furthermore, the reliability evaluation unit may adjust the reliability evaluation criteria based on the user's past feedback. This allows the AI ​​system to provide the user with more reliable information.

[0111] When interpreting a user's question, the interpretation unit can improve the accuracy of the interpretation by taking into account the context of the question. For example, the interpretation unit can refer to the context of questions previously asked by the user to more accurately interpret the intention of the current question. The interpretation unit can also prioritize interpretation of related information based on the context of the question. Furthermore, the interpretation unit can adjust the direction of interpretation based on the context of the question. In this way, the interpretation unit can improve the accuracy of interpretation by taking into account the context of the question.

[0112] When generating reassuring words, the generation unit can adjust the way the words are expressed by taking into account the user's cultural background. For example, if the user belongs to a particular cultural sphere, the generation unit can generate reassuring words using expressions appropriate for that culture. The generation unit can also generate reassuring words that include religious expressions by taking into account the user's religious background. Furthermore, the generation unit can generate reassuring words in the user's native language by taking into account the user's linguistic background. In this way, the generation unit can generate more appropriate reassuring words by taking into account the user's cultural background.

[0113] When providing an answer to a user's question, the information providing unit can adjust the visual representation of the answer. For example, if the user prefers visual information, the information providing unit can provide the information using graphs or charts. If the user prefers text information, the information providing unit can also provide the information in detailed text. Furthermore, if the user prefers videos, the information providing unit can also provide the information in video format. This allows the information providing unit to provide more appropriate information by adjusting the visual representation according to the user's preferences.

[0114] The determination unit can evaluate the reliability of the answer when providing an answer to a user's question. For example, the determination unit evaluates the reliability of the source of the provided information or the opinion of an expert, and provides the user with highly reliable information. The determination unit can also evaluate the reliability of information on the Internet and eliminate unreliable information. Furthermore, the determination unit can adjust the reliability evaluation criteria based on the user's past feedback. This allows the determination unit to provide the user with more reliable information.

[0115] When providing an answer to a user's question, the information providing unit can adjust the visual representation of the answer. For example, if the user prefers visual information, the information providing unit can provide the information using graphs or charts. If the user prefers text information, the information providing unit can also provide the information in detailed text. Furthermore, if the user prefers videos, the information providing unit can also provide the information in video format. This allows the information providing unit to provide more appropriate information by adjusting the visual representation according to the user's preferences.

[0116] The interpretation unit can estimate the user's emotions and determine the priority of interpretation results based on the estimated user's emotions. For example, if the user is feeling strong anxiety, the interpretation unit can interpret that question with the highest priority and quickly generate an answer. Also, if the user is feeling mild anxiety, the interpretation unit can interpret the question with the same priority as other questions. Furthermore, if the user is relaxed, the interpretation unit can provide a detailed interpretation and postpone it over other questions. This allows the interpretation unit to determine the priority of interpretation results based on the user's emotions, enabling more appropriate interpretations.

[0117] The interpretation unit can improve the accuracy of the interpretation by referring to the user's past question history. For example, if the user has asked a similar question in the past, the interpretation unit can improve the accuracy of the interpretation by referring to that history. The interpretation unit can also find specific patterns from the user's past question history and reflect them in the interpretation. Furthermore, the interpretation unit can adjust the direction of the interpretation based on answers the user has received in the past. In this way, the interpretation unit can improve the accuracy of the interpretation by referring to the user's past question history.

[0118] When interpreting a question, the interpretation unit can select the most appropriate interpretation means depending on the user's input method. For example, if the user inputs a question by voice, the interpretation unit performs the interpretation using voice recognition technology. Also, if the user inputs a question in text, the interpretation unit can perform the interpretation using natural language processing technology. Furthermore, if the user uploads an image, the interpretation unit can perform the interpretation using image analysis technology. In this way, the interpretation unit can improve the accuracy of interpretation by selecting the most appropriate interpretation means depending on the user's input method.

[0119] When interpreting a question, the interpretation unit can take into account the user's current situation and background information. For example, if a user inputs a question during a specific time period, the interpretation unit can take into account information related to that time period. Also, if a user inputs a question from a specific location, the interpretation unit can take into account information related to that location. Furthermore, the interpretation unit can take into account the user's background information (e.g., past question history and profile information) when interpreting the question. This allows the interpretation unit to improve the accuracy of interpretation by taking into account the user's current situation and background information.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The interpretation unit interprets the user's question. The interpretation unit may use, for example, natural language processing technology to interpret the user's question. The interpretation unit may also improve the accuracy of the interpretation by referring to the user's past question history. Furthermore, the interpretation unit may estimate the user's emotions and adjust the method of interpreting the question based on the estimated user emotions. Step 2: The generator generates reassuring words based on the question interpreted by the interpreter. For example, the generator generates reassuring words by referring to a database of past questions and answers. The generator can also estimate the user's emotions and adjust the way the reassuring words are expressed based on the estimated user's emotions. Step 3: The information providing unit returns the most appropriate information based on the question interpreted by the interpretation unit. For example, the information providing unit collects and analyzes information on the Internet and expert opinions to return the most appropriate information. The information providing unit can also estimate the user's emotions and adjust the way it provides information based on the estimated user emotions. Step 4: The discrimination unit discriminates cases in which a developmental disorder is suspected based on the information provided by the information providing unit. The discrimination unit discriminates cases in which a developmental disorder is suspected, for example, when a specific symptom continues for a certain period of time or when a specific developmental stage has not been reached. The discrimination unit can also estimate the user's emotions and adjust the criteria for discrimination based on the estimated user's emotions.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an interpretation unit for interpreting a user's question; a generator for generating reassuring words based on the question interpreted by the interpreter; an information providing unit that returns appropriate information based on the question interpreted by the interpretation unit; a discrimination unit that discriminates cases in which a developmental disorder is suspected based on the information provided by the information providing unit; Equipped with A system characterized by:

2. The interpretation unit Use natural language processing techniques to interpret user questions 2. The system of claim 1.

3. The generation unit Generate reassuring words by referencing a database of past questions and answers 2. The system of claim 1.

4. The information providing unit Collects and analyzes information from the internet and expert opinions to return relevant information 2. The system of claim 1.

5. The determination unit Identify cases of suspected developmental disorders when certain symptoms persist for a certain period of time or when certain developmental milestones are not reached 2. The system of claim 1.

6. The information providing unit Provides users with their questions to doctors and specialists to facilitate smooth interviews 2. The system of claim 1.

7. The interpretation unit Inferring user sentiment and adjusting how questions are interpreted based on the inferred sentiment 2. The system of claim 1.

8. The interpretation unit When interpreting a question, the accuracy of the interpretation is improved by referring to the user's past question history.

2. The system of claim 1.

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