system

Abstract

The system according to the embodiment aims to provide an appropriate support program for children with developmental disabilities and children in the gray zone, thereby reducing the psychological burden on parents. [Solution] A system according to an embodiment includes a collection unit, a provision unit, a reception unit, and an advice provision unit. The collection unit collects the diagnosis or age of the child and the concerns of the parents. The provision unit provides a support program based on the information collected by the collection unit. The reception unit receives consultation details from the user. The advice provision unit provides advice based on the consultation details received by the reception unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of lacking effective means to provide appropriate support programs for children with developmental disorders and those in the gray zone, as well as to reduce the psychological burden on parents.

[0005] The system according to the embodiment aims to provide an appropriate support program for children with developmental disabilities and children in the gray zone, thereby reducing the psychological burden on parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a provision unit, a reception unit, and an advice provision unit. The collection unit collects the diagnosis or age of the child and the concerns of the parents. The provision unit provides a support program based on the information collected by the collection unit. The reception unit receives consultation details from the user. The advice provision unit provides advice based on the consultation details received by the reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate support program for children with developmental disabilities or children in the gray zone, thereby reducing the psychological burden on parents. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A support system according to an embodiment of the present invention uses AI to provide future support for children with developmental disabilities or those in the gray zone, while also reducing the psychological and physical burden on their parents. This support system provides specialized support programs and selects the most appropriate program based on each child's diagnosis, age, and parental concerns. It then provides a chat function for both the child and their parent, allowing them to consult with an AI character to receive advice and vent about things they would otherwise shy away from. This reduces the psychological burden. This system uses AI to provide optimal support tailored to each child's characteristics and parental concerns, thereby reducing the psychological and physical burden on both parents and children. For example, a user inputs the child's diagnosis, age, and parental concerns. The AI ​​then analyzes the input information and provides the optimal support program. Furthermore, the user can consult with the AI ​​character to receive appropriate advice. This mechanism reduces the psychological and physical burden on both parents and children. This allows the support system to provide future support for children with developmental disabilities or those in the gray zone, while also reducing the psychological and physical burden on their parents.

[0029] A support system according to an embodiment includes a collection unit, a provision unit, a reception unit, and an advice provision unit. The collection unit collects information about a child's diagnosis or age and parental concerns. For example, the collection unit can collect information about diagnoses such as medical diagnoses, psychological diagnoses, and developmental diagnoses. The collection unit can also collect information based on age categories such as 0-3 years old and 4-6 years old. The collection unit can also collect information about parental concerns such as child-rearing concerns, education concerns, and health concerns. The provision unit provides a support program based on the information collected by the collection unit. For example, the provision unit can provide support programs such as counseling, educational programs, and medical support. The provision unit analyzes the collected information and selects an optimal support program using AI. The reception unit accepts consultation requests from users. For example, the reception unit can accept consultation requests via online chat, telephone, face-to-face consultation, or other methods. The advice provision unit provides advice based on the consultation requests accepted by the reception unit. For example, the advice provision unit can provide advice using an AI character. The advice provision unit analyzes the consultation requests and generates appropriate advice using AI. As a result, the support system according to the embodiment can provide optimal support based on the diagnosis and age of the child and the concerns of the parents, thereby reducing the psychological and physical burden on both the parent and child.

[0030] The advice providing unit can provide advice using an AI character. Examples of AI characters include, but are not limited to, anime-style characters and robot-style characters. For example, the advice providing unit can provide friendly advice using an anime-style character. The advice providing unit can also provide reliable advice using a robot-style character. Furthermore, the advice providing unit can provide an environment in which the user can easily seek advice using the AI ​​character. For example, the advice providing unit causes the AI ​​character to provide appropriate advice depending on the content of the user's consultation. In this way, by using the AI ​​character, an environment in which the user can easily seek advice and is friendly can be provided.

[0031] The collection unit can analyze the child's past diagnostic history and select the optimal collection method. For example, the collection unit can collect information from the past diagnostic history, focusing on specific diagnostic items. The collection unit can also adjust the frequency and timing of diagnoses based on the past diagnostic history. Furthermore, the collection unit can analyze the past diagnostic history and collect information taking into account the parent's level of interest in specific diagnostic items. This enables more effective information collection by analyzing the past diagnostic history. The past diagnostic history includes, for example, diagnostic results, diagnostic dates and times, diagnostic content, etc., but is not limited to such examples. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input past diagnostic history data into a generation AI and have the generation AI select the optimal collection method.

[0032] When collecting information, the collection unit can filter the information based on the child's current living situation and the parent's areas of interest. The collection unit, for example, prioritizes collecting relevant information based on the child's current school life and home environment. The collection unit can also filter the information based on the parent's areas of interest (e.g., education, health, socialization, etc.). The collection unit can also collect information based on the child's current living situation (e.g., stress level, health status, etc.). By collecting information based on the child's living situation and the parent's areas of interest, more relevant information can be collected. Examples of the current living situation include, but are not limited to, the home environment, school life, health status, etc. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the child's living situation data into a generation AI and cause the generation AI to filter the information.

[0033] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting information about educational institutions and medical institutions in the user's area. The collection unit can also collect information about support programs and events specific to the area based on the user's geographical location. Furthermore, the collection unit can collect information about nearby experts and support groups based on the user's geographical location. This allows for prioritized collection of region-specific information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information on topics of interest based on information shared by the user on social media. The collection unit can also analyze the content posted by experts and organizations the user follows and collect related information. Furthermore, the collection unit can analyze the activities of online communities the user participates in and collect related information. In this way, information based on the user's interests can be collected by analyzing social media activities. Social media activities include, but are not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above-mentioned processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0035] When providing a support program, the providing unit can adjust the level of detail of the program based on the child's importance. For example, if the child's diagnosis is severe, the providing unit can provide a detailed support program. The providing unit can also provide a concise and easy-to-understand program for a young child. Furthermore, if the parent's concerns are serious, the providing unit can provide a program including specific solutions. By adjusting the level of detail of the program based on the child's importance, more appropriate support can be provided. Examples of the child's importance include, but are not limited to, health status, academic performance, and home environment. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the child's importance data into a generating AI and cause the generating AI to adjust the level of detail of the program.

[0036] When providing a support program, the providing unit can apply different program algorithms depending on the child's category. For example, the providing unit can provide a program to improve social skills to a child on the autism spectrum. The providing unit can also provide a program to improve concentration to a child with ADHD. Furthermore, the providing unit can provide a program to enhance learning support to a child with a learning disability. This allows for more effective support to be provided by applying different program algorithms depending on the child's category. Examples of child categories include, but are not limited to, age, gender, and interests. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input child category data into a generating AI and cause the generating AI to apply a program algorithm.

[0037] When providing support programs, the providing unit can determine the priority of the programs based on the child's diagnosis date. For example, the providing unit can prioritize programs that require early intervention for recently diagnosed children. The providing unit can also provide continuous support programs to children who require long-term support. Furthermore, the providing unit can provide programs tailored to the child's progress for children for whom a certain period of time has passed since diagnosis. This allows for more appropriate support to be provided by determining the priority of programs based on the child's diagnosis date. The diagnosis date includes, but is not limited to, a recent diagnosis, a past diagnosis, etc. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the child's diagnosis date into the generating AI and cause the generating AI to determine the priority of the programs.

[0038] When providing support programs, the providing unit can adjust the order of the programs based on the child's relevance. For example, the providing unit can first provide the program most relevant to the child's diagnosis. The providing unit can also provide the programs in an appropriate order depending on the child's age. Furthermore, the providing unit can prioritize the program most relevant to the parent's concerns. This allows for more effective support by adjusting the order of the programs based on the child's relevance. Relevance includes, but is not limited to, for example, the degree of similarity in content, related topics, etc. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the child's relevance data into a generating AI and cause the generating AI to adjust the order of the programs.

[0039] When receiving a consultation content, the reception unit can select the optimal reception method by referring to the user's past consultation history. For example, the reception unit prioritizes receiving related consultation content based on the content of consultations the user has frequently consulted in the past. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a reception method to be used during a specific time period based on the user's past consultation history. This enables more appropriate consultation reception by referring to the past consultation history. The past consultation history includes, for example, the consultation content, the date and time of the consultation, and the consultation result, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI select the optimal reception method.

[0040] The reception unit can customize the reception method based on the user's current lifestyle when receiving the consultation content. For example, if the user is busy, the reception unit can receive the consultation content in the form of a simple question. Furthermore, if the user is relaxed, the reception unit can also receive the consultation content in the form of a detailed question. Furthermore, if the user is feeling stressed, the reception unit can prioritize voice input and quickly receive the consultation content. This enables more appropriate consultation reception by customizing the reception method based on the user's lifestyle. Examples of the current lifestyle include, but are not limited to, home environment, work situation, and health condition. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's lifestyle data into a generation AI and have the generation AI customize the reception method.

[0041] When accepting a consultation request, the reception unit can select the optimal reception method by taking into account the user's geographical location information. For example, the reception unit prioritizes the reception of consultation requests related to experts and support groups in the user's area. The reception unit can also accept consultation requests related to support programs and event information specific to the area based on the user's geographical location. The reception unit can also accept consultation requests related to nearby medical institutions and educational institutions based on the user's geographical location. This allows the reception of consultation requests specific to the area to be prioritized by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and address information, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data into a generation AI and cause the generation AI to select the optimal reception method.

[0042] When receiving a consultation request, the reception unit can analyze the user's social media activity and suggest a means of receiving the request. For example, the reception unit can receive consultation requests related to topics of interest based on information shared by the user on social media. The reception unit can also analyze posts from experts and organizations the user follows and accept related consultation requests. Furthermore, the reception unit can analyze activities in online communities in which the user participates and accept related consultation requests. By analyzing social media activity, it becomes possible to accept consultation requests based on the user's interests. Examples of social media activity include, but are not limited to, post content, the number of likes, and the number of followers. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest a means of receiving the consultation.

[0043] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the importance of the consultation content. For example, the advice providing unit provides detailed advice for consultation content with a high level of importance. The advice providing unit can also provide concise advice for consultation content with a low level of importance. Furthermore, the advice providing unit can provide additional information as needed depending on the importance of the consultation content. In this way, by adjusting the level of detail of the advice based on the importance of the consultation content, more appropriate advice can be provided. The importance of the consultation content includes, for example, urgency, impact, and relevance, but is not limited to these examples. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input importance data of the consultation content to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0044] When providing advice, the advice providing unit can apply different advice algorithms depending on the category of the consultation content. For example, the advice providing unit can apply an advice algorithm specialized in education to consultation content related to education. Furthermore, the advice providing unit can also apply an advice algorithm specialized in health to consultation content related to health. Furthermore, the advice providing unit can apply an advice algorithm for improving sociality to consultation content related to sociality. In this way, by applying different advice algorithms depending on the category of the consultation content, more effective advice can be provided. Examples of categories of consultation content include, but are not limited to, health, education, and family issues. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input category data of the consultation content into the generation AI and cause the generation AI to apply the advice algorithm.

[0045] When providing advice, the advice providing unit can determine the priority of advice based on the time of submission of the consultation content. For example, the advice providing unit can quickly provide advice to consultation content that has been submitted recently. The advice providing unit can also provide advice preferentially to consultation content that has been left unprocessed for a long period of time. Furthermore, the advice providing unit can provide advice at an appropriate time based on the time of submission. In this way, by determining the priority of advice based on the time of submission of the consultation content, advice can be provided at a more appropriate time. The time of submission includes, for example, recent submission, past submission, etc., but is not limited to such examples. Some or all of the above-described processing in the advice providing unit may be performed using, or without using, AI. For example, the advice providing unit can input data on the time of submission of the consultation content to a generation AI and cause the generation AI to determine the priority of advice.

[0046] When providing advice, the advice providing unit can adjust the order of advice based on the relevance of the consultation content. For example, the advice providing unit prioritizes advice based on the high relevance of the consultation content. The advice providing unit can also provide advice in an appropriate order depending on the relevance of the consultation content. Furthermore, the advice providing unit can also provide additional information as needed based on the relevance of the consultation content. In this way, more effective advice can be provided by adjusting the order of advice based on the relevance of the consultation content. Relevance includes, for example, the degree of content similarity, related topics, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input relevance data of the consultation content to a generation AI and cause the generation AI to adjust the order of advice.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can analyze the user's lifestyle and determine the optimal timing for collecting information. For example, the collection unit can analyze the user's sleep patterns and activity times and collect information during times when the user is most relaxed. The collection unit can also collect information at times when the user is least stressed, taking into account the user's meal times and exercise times. Furthermore, the collection unit can analyze how the user spends their weekends and holidays and collect information during times when the user has more time to relax. This allows for more effective information collection by adjusting the timing of information collection based on the user's lifestyle.

[0049] The collection unit can analyze the child's past diagnostic history and select the optimal collection method. For example, the collection unit can collect information from the past diagnostic history, focusing on specific diagnostic items. The collection unit can also adjust the frequency and timing of diagnostics based on the past diagnostic history. Furthermore, the collection unit can analyze the past diagnostic history and collect information taking into account the parent's interest in specific diagnostic items. This allows for more effective information collection by analyzing the past diagnostic history.

[0050] When collecting information, the collection unit can filter the information based on the child's current living situation and the parent's areas of interest. For example, the collection unit can prioritize collecting relevant information based on the child's current school life and home environment. The collection unit can also filter the information based on the parent's areas of interest (e.g., education, health, social skills, etc.). Furthermore, the collection unit can also collect information based on the child's current living situation (e.g., stress level, health status, etc.). In this way, by filtering the information based on the child's living situation and the parent's areas of interest, more relevant information can be collected.

[0051] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting information about educational institutions and medical institutions in the user's area. The collection unit can also collect information about support programs and events specific to the area based on the user's geographical location. Furthermore, the collection unit can collect information about nearby experts and support groups based on the user's geographical location. In this way, by taking into account the user's geographical location information, it is possible to prioritize collecting area-specific information.

[0052] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, information on topics of interest can be collected based on information shared by the user on social media. The collection unit can also analyze the content posted by experts and organizations the user follows and collect related information. Furthermore, the collection unit can analyze the activities of online communities in which the user participates and collect related information. In this way, information based on the user's interests can be collected by analyzing social media activities.

[0053] When providing a support program, the provider can adjust the level of detail of the program based on the importance of the child. For example, if the child's diagnosis is severe, a detailed support program is provided. The provider can also provide a simple and easy-to-understand program for a young child. Furthermore, if the parent's concerns are serious, the provider can provide a program that includes specific solutions. This allows for more appropriate support by adjusting the level of detail of the program based on the importance of the child.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects the child's diagnosis or age, and parental concerns. For example, the collection unit can collect diagnosis details such as medical diagnosis, psychological diagnosis, and developmental diagnosis. The collection unit can also collect information based on age categories such as 0-3 years old and 4-6 years old. Furthermore, the collection unit can collect parental concerns such as child-rearing concerns, education concerns, and health concerns. Step 2: The provision unit provides a support program based on the information collected by the collection unit. For example, the provision unit can provide support programs such as counseling, educational programs, and medical support. The provision unit uses AI to analyze the collected information and select the optimal support program. Step 3: The reception unit receives the consultation content from the user. For example, the reception unit can receive the consultation content via online chat, telephone, face-to-face, or other methods. Step 4: The advice providing unit provides advice based on the consultation content received by the reception unit. For example, the advice providing unit can provide advice using an AI character. The advice providing unit uses AI to analyze the consultation content and generate appropriate advice.

[0056] (Example 2) A support system according to an embodiment of the present invention uses AI to provide future support for children with developmental disabilities or those in the gray zone, while also reducing the psychological and physical burden on their parents. This support system provides specialized support programs and selects the most appropriate program based on each child's diagnosis, age, and parental concerns. It then provides a chat function for both the child and their parent, allowing them to consult with an AI character to receive advice and vent about things they would otherwise shy away from. This reduces the psychological burden. This system uses AI to provide optimal support tailored to each child's characteristics and parental concerns, thereby reducing the psychological and physical burden on both parents and children. For example, a user inputs the child's diagnosis, age, and parental concerns. The AI ​​then analyzes the input information and provides the optimal support program. Furthermore, the user can consult with the AI ​​character to receive appropriate advice. This mechanism reduces the psychological and physical burden on both parents and children. This allows the support system to provide future support for children with developmental disabilities or those in the gray zone, while also reducing the psychological and physical burden on their parents.

[0057] A support system according to an embodiment includes a collection unit, a provision unit, a reception unit, and an advice provision unit. The collection unit collects information about a child's diagnosis or age and parental concerns. For example, the collection unit can collect information about diagnoses such as medical diagnoses, psychological diagnoses, and developmental diagnoses. The collection unit can also collect information based on age categories such as 0-3 years old and 4-6 years old. The collection unit can also collect information about parental concerns such as child-rearing concerns, education concerns, and health concerns. The provision unit provides a support program based on the information collected by the collection unit. For example, the provision unit can provide support programs such as counseling, educational programs, and medical support. The provision unit analyzes the collected information and selects an optimal support program using AI. The reception unit accepts consultation requests from users. For example, the reception unit can accept consultation requests via online chat, telephone, face-to-face consultation, or other methods. The advice provision unit provides advice based on the consultation requests accepted by the reception unit. For example, the advice provision unit can provide advice using an AI character. The advice provision unit analyzes the consultation requests and generates appropriate advice using AI. As a result, the support system according to the embodiment can provide optimal support based on the diagnosis and age of the child and the concerns of the parents, thereby reducing the psychological and physical burden on both the parent and child.

[0058] The advice providing unit can provide advice using an AI character. Examples of AI characters include, but are not limited to, anime-style characters and robot-style characters. For example, the advice providing unit can provide friendly advice using an anime-style character. The advice providing unit can also provide reliable advice using a robot-style character. Furthermore, the advice providing unit can provide an environment in which the user can easily seek advice using the AI ​​character. For example, the advice providing unit causes the AI ​​character to provide appropriate advice depending on the content of the user's consultation. In this way, by using the AI ​​character, an environment in which the user can easily seek advice and is friendly can be provided.

[0059] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit may collect information at night or on holidays so that the user can collect information in a relaxed state. The collection unit can also collect information during active hours during the day when the user is relaxed. Furthermore, if the user is in a hurry, the collection unit can quickly collect information and provide immediate feedback. This enables more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0060] The collection unit can analyze the child's past diagnostic history and select the optimal collection method. For example, the collection unit can collect information from the past diagnostic history, focusing on specific diagnostic items. The collection unit can also adjust the frequency and timing of diagnoses based on the past diagnostic history. Furthermore, the collection unit can analyze the past diagnostic history and collect information taking into account the parent's level of interest in specific diagnostic items. This enables more effective information collection by analyzing the past diagnostic history. The past diagnostic history includes, for example, diagnostic results, diagnostic dates and times, diagnostic content, etc., but is not limited to such examples. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input past diagnostic history data into a generation AI and have the generation AI select the optimal collection method.

[0061] When collecting information, the collection unit can filter the information based on the child's current living situation and the parent's areas of interest. The collection unit, for example, prioritizes collecting relevant information based on the child's current school life and home environment. The collection unit can also filter the information based on the parent's areas of interest (e.g., education, health, socialization, etc.). The collection unit can also collect information based on the child's current living situation (e.g., stress level, health status, etc.). By collecting information based on the child's living situation and the parent's areas of interest, more relevant information can be collected. Examples of the current living situation include, but are not limited to, the home environment, school life, health status, etc. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the child's living situation data into a generation AI and cause the generation AI to filter the information.

[0062] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information related to stress reduction. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting information related to long-term support. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting information that requires immediate response. This allows for the prioritized collection of more appropriate information by determining the priority of information according to the user's emotions. The estimation of emotions is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of information.

[0063] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting information about educational institutions and medical institutions in the user's area. The collection unit can also collect information about support programs and events specific to the area based on the user's geographical location. Furthermore, the collection unit can collect information about nearby experts and support groups based on the user's geographical location. This allows for prioritized collection of region-specific information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0064] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can collect information on topics of interest based on information shared by the user on social media. The collection unit can also analyze the content posted by experts and organizations the user follows and collect related information. Furthermore, the collection unit can analyze the activities of online communities the user participates in and collect related information. In this way, information based on the user's interests can be collected by analyzing social media activities. Social media activities include, but are not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above-mentioned processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0065] The providing unit can estimate the user's emotions and adjust the support program's expression method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple and easy-to-understand expression method. Furthermore, if the user is relaxed, the providing unit can provide an expression method that includes detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide a short expression method that focuses on the main points. This allows for more effective support by adjusting the support program's expression method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into a generating AI and cause the generating AI to adjust the support program's expression method.

[0066] When providing a support program, the providing unit can adjust the level of detail of the program based on the child's importance. For example, if the child's diagnosis is severe, the providing unit can provide a detailed support program. The providing unit can also provide a concise and easy-to-understand program for a young child. Furthermore, if the parent's concerns are serious, the providing unit can provide a program including specific solutions. By adjusting the level of detail of the program based on the child's importance, more appropriate support can be provided. Examples of the child's importance include, but are not limited to, health status, academic performance, and home environment. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the child's importance data into a generating AI and cause the generating AI to adjust the level of detail of the program.

[0067] When providing a support program, the providing unit can apply different program algorithms depending on the child's category. For example, the providing unit can provide a program to improve social skills to a child on the autism spectrum. The providing unit can also provide a program to improve concentration to a child with ADHD. Furthermore, the providing unit can provide a program to enhance learning support to a child with a learning disability. This allows for more effective support to be provided by applying different program algorithms depending on the child's category. Examples of child categories include, but are not limited to, age, gender, and interests. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input child category data into a generating AI and cause the generating AI to apply a program algorithm.

[0068] The providing unit can estimate the user's emotions and adjust the length of the support program based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a short, to-the-point program. Furthermore, if the user is relaxed, the providing unit can provide a longer program with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide a short program that can be quickly executed. This allows for more appropriate support by adjusting the length of the support program according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into a generating AI and have the generating AI adjust the length of the support program.

[0069] When providing support programs, the providing unit can determine the priority of the programs based on the child's diagnosis date. For example, the providing unit can prioritize programs that require early intervention for recently diagnosed children. The providing unit can also provide continuous support programs to children who require long-term support. Furthermore, the providing unit can provide programs tailored to the child's progress for children for whom a certain period of time has passed since diagnosis. This allows for more appropriate support to be provided by determining the priority of programs based on the child's diagnosis date. The diagnosis date includes, but is not limited to, a recent diagnosis, a past diagnosis, etc. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the child's diagnosis date into the generating AI and cause the generating AI to determine the priority of the programs.

[0070] When providing support programs, the providing unit can adjust the order of the programs based on the child's relevance. For example, the providing unit can first provide the program most relevant to the child's diagnosis. The providing unit can also provide the programs in an appropriate order depending on the child's age. Furthermore, the providing unit can prioritize the program most relevant to the parent's concerns. This allows for more effective support by adjusting the order of the programs based on the child's relevance. Relevance includes, but is not limited to, for example, the degree of similarity in content, related topics, etc. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the child's relevance data into a generating AI and cause the generating AI to adjust the order of the programs.

[0071] The reception unit can estimate the user's emotions and adjust the consultation reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept the consultation. This allows for more appropriate consultation reception by adjusting the consultation reception method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI adjust the consultation reception method.

[0072] When receiving a consultation content, the reception unit can select the optimal reception method by referring to the user's past consultation history. For example, the reception unit prioritizes receiving related consultation content based on the content of consultations the user has frequently consulted in the past. The reception unit can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a reception method to be used during a specific time period based on the user's past consultation history. This enables more appropriate consultation reception by referring to the past consultation history. The past consultation history includes, for example, the consultation content, the date and time of the consultation, and the consultation result, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past consultation history data into a generation AI and have the generation AI select the optimal reception method.

[0073] The reception unit can customize the reception method based on the user's current lifestyle when receiving the consultation content. For example, if the user is busy, the reception unit can receive the consultation content in the form of a simple question. Furthermore, if the user is relaxed, the reception unit can also receive the consultation content in the form of a detailed question. Furthermore, if the user is feeling stressed, the reception unit can prioritize voice input and quickly receive the consultation content. This enables more appropriate consultation reception by customizing the reception method based on the user's lifestyle. Examples of the current lifestyle include, but are not limited to, home environment, work situation, and health condition. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's lifestyle data into a generation AI and have the generation AI customize the reception method.

[0074] The reception unit can estimate the user's emotions and determine the priority of consultation requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize consultation requests regarding stress reduction. Furthermore, if the user is relaxed, the reception unit can prioritize consultation requests regarding long-term support. Furthermore, if the user is in a hurry, the reception unit can prioritize consultation requests requiring immediate attention. This allows for more appropriate consultation requests by prioritizing consultation requests based on the user's emotions. Emotion estimation can be performed using techniques such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of the consultation requests.

[0075] When accepting a consultation request, the reception unit can select the optimal reception method by taking into account the user's geographical location information. For example, the reception unit prioritizes the reception of consultation requests related to experts and support groups in the user's area. The reception unit can also accept consultation requests related to support programs and event information specific to the area based on the user's geographical location. The reception unit can also accept consultation requests related to nearby medical institutions and educational institutions based on the user's geographical location. This allows the reception of consultation requests specific to the area to be prioritized by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and address information, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location data into a generation AI and cause the generation AI to select the optimal reception method.

[0076] When receiving a consultation request, the reception unit can analyze the user's social media activity and suggest a means of receiving the request. For example, the reception unit can receive consultation requests related to topics of interest based on information shared by the user on social media. The reception unit can also analyze posts from experts and organizations the user follows and accept related consultation requests. Furthermore, the reception unit can analyze activities in online communities in which the user participates and accept related consultation requests. By analyzing social media activity, it becomes possible to accept consultation requests based on the user's interests. Examples of social media activity include, but are not limited to, post content, the number of likes, and the number of followers. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest a means of receiving the consultation.

[0077] The advice providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice providing unit can provide a simple and easy-to-understand way of expressing the advice. Furthermore, if the user is relaxed, the advice providing unit can also provide a way of expressing the advice that includes detailed explanations. Furthermore, if the user is in a hurry, the advice providing unit can also provide a short way of expressing the advice that gets to the point. This allows for more effective advice to be provided by adjusting the way the advice is expressed based on the user's emotions. The emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without AI. For example, the advice providing unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the way the advice is expressed.

[0078] When providing advice, the advice providing unit can adjust the level of detail of the advice based on the importance of the consultation content. For example, the advice providing unit provides detailed advice for consultation content with a high level of importance. The advice providing unit can also provide concise advice for consultation content with a low level of importance. Furthermore, the advice providing unit can provide additional information as needed depending on the importance of the consultation content. In this way, by adjusting the level of detail of the advice based on the importance of the consultation content, more appropriate advice can be provided. The importance of the consultation content includes, for example, urgency, impact, and relevance, but is not limited to these examples. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input importance data of the consultation content to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0079] When providing advice, the advice providing unit can apply different advice algorithms depending on the category of the consultation content. For example, the advice providing unit can apply an advice algorithm specialized in education to consultation content related to education. Furthermore, the advice providing unit can also apply an advice algorithm specialized in health to consultation content related to health. Furthermore, the advice providing unit can apply an advice algorithm for improving sociality to consultation content related to sociality. In this way, by applying different advice algorithms depending on the category of the consultation content, more effective advice can be provided. Examples of categories of consultation content include, but are not limited to, health, education, and family issues. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input category data of the consultation content into the generation AI and cause the generation AI to apply the advice algorithm.

[0080] The advice providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice providing unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the advice providing unit can provide longer advice with detailed explanations. Furthermore, if the user is in a hurry, the advice providing unit can provide short advice that can be implemented quickly. This allows for more appropriate advice to be provided by adjusting the length of the advice according to the user's emotions. The emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without AI. For example, the advice providing unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the length of the advice.

[0081] When providing advice, the advice providing unit can determine the priority of advice based on the time of submission of the consultation content. For example, the advice providing unit can quickly provide advice to consultation content that has been submitted recently. The advice providing unit can also provide advice preferentially to consultation content that has been left unprocessed for a long period of time. Furthermore, the advice providing unit can provide advice at an appropriate time based on the time of submission. In this way, by determining the priority of advice based on the time of submission of the consultation content, advice can be provided at a more appropriate time. The time of submission includes, for example, recent submission, past submission, etc., but is not limited to such examples. Some or all of the above-described processing in the advice providing unit may be performed using, or without using, AI. For example, the advice providing unit can input data on the time of submission of the consultation content to a generation AI and cause the generation AI to determine the priority of advice.

[0082] When providing advice, the advice providing unit can adjust the order of advice based on the relevance of the consultation content. For example, the advice providing unit prioritizes advice based on the high relevance of the consultation content. The advice providing unit can also provide advice in an appropriate order depending on the relevance of the consultation content. Furthermore, the advice providing unit can also provide additional information as needed based on the relevance of the consultation content. In this way, more effective advice can be provided by adjusting the order of advice based on the relevance of the consultation content. Relevance includes, for example, the degree of content similarity, related topics, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the advice providing unit may be performed using, or without, AI. For example, the advice providing unit can input relevance data of the consultation content to a generation AI and cause the generation AI to adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, provision unit, reception unit, and advice provision 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 collection unit is realized by the control unit 46A of the smart device 14 and collects the child's diagnosis and age, and the parent's concerns. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an optimal support program based on the collected information. The reception unit is realized, for example, by the control unit 46A of the smart device 14 and receives consultation details from the user. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the received consultation details. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, provision unit, reception unit, and advice provision 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 collection unit is realized by the control unit 46A of the smart glasses 214 and collects the child's diagnosis and age, and the parent's concerns. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an optimal support program based on the collected information. The reception unit is realized, for example, by the control unit 46A of the smart glasses 214 and receives the consultation content from the user. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the received consultation content. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, provision unit, reception unit, and advice provision 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 collection unit is realized by the control unit 46A of the headset type terminal 314 and collects the child's diagnosis and age, and the parent's concerns. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an optimal support program based on the collected information. The reception unit is realized, for example, by the control unit 46A of the headset type terminal 314 and receives the content of the consultation from the user. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the received content of the consultation. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, provision unit, reception unit, and advice provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects the child's diagnosis and age, and the parents' concerns. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an optimal support program based on the collected information. The reception unit is realized, for example, by the control unit 46A of the robot 414 and receives consultation details from the user. The advice provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate advice based on the received consultation details.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The collection unit can analyze the user's lifestyle and determine the optimal timing for collecting information. For example, the collection unit can analyze the user's sleep patterns and activity times and collect information during times when the user is most relaxed. The collection unit can also collect information at times when the user is least stressed, taking into account the user's meal times and exercise times. Furthermore, the collection unit can analyze how the user spends their weekends and holidays and collect information during times when the user has more time to relax. This allows for more effective information collection by adjusting the timing of information collection based on the user's lifestyle.

[0085] The advice providing unit can estimate the user's emotions and adjust the tone of the advice based on the estimated user's emotions. For example, if the user is feeling stressed, the advice can be provided in a gentle tone. If the user is relaxed, the advice can be provided in a friendly tone. Furthermore, if the user is in a hurry, the advice can be provided in a concise and direct tone. This allows for more effective communication by adjusting the tone of the advice according to the user's emotions.

[0086] The collection unit can estimate the user's emotions and customize the information collection method based on the estimated user's emotions. For example, if the user is feeling stressed, information can be collected in the form of simple questions. If the user is relaxed, information can be collected in the form of detailed questions. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly collect information. This allows more appropriate information collection by customizing the information collection method according to the user's emotions.

[0087] The collection unit can analyze the child's past diagnostic history and select the optimal collection method. For example, the collection unit can collect information from the past diagnostic history, focusing on specific diagnostic items. The collection unit can also adjust the frequency and timing of diagnostics based on the past diagnostic history. Furthermore, the collection unit can analyze the past diagnostic history and collect information taking into account the parent's interest in specific diagnostic items. This allows for more effective information collection by analyzing the past diagnostic history.

[0088] When collecting information, the collection unit can filter the information based on the child's current living situation and the parent's areas of interest. For example, the collection unit can prioritize collecting relevant information based on the child's current school life and home environment. The collection unit can also filter the information based on the parent's areas of interest (e.g., education, health, social skills, etc.). Furthermore, the collection unit can also collect information based on the child's current living situation (e.g., stress level, health status, etc.). In this way, by filtering the information based on the child's living situation and the parent's areas of interest, more relevant information can be collected.

[0089] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, information related to stress reduction can be collected with priority. Also, if the user is relaxed, information related to long-term support can be collected with priority. Furthermore, if the user is in a hurry, information requiring immediate response can be collected with priority. In this way, by determining the priority of information according to the user's emotions, more appropriate information can be collected with priority.

[0090] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit can prioritize collecting information about educational institutions and medical institutions in the user's area. The collection unit can also collect information about support programs and events specific to the area based on the user's geographical location. Furthermore, the collection unit can collect information about nearby experts and support groups based on the user's geographical location. In this way, by taking into account the user's geographical location information, it is possible to prioritize collecting area-specific information.

[0091] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, information on topics of interest can be collected based on information shared by the user on social media. The collection unit can also analyze the content posted by experts and organizations the user follows and collect related information. Furthermore, the collection unit can analyze the activities of online communities in which the user participates and collect related information. In this way, information based on the user's interests can be collected by analyzing social media activities.

[0092] The providing unit can estimate the user's emotions and adjust the expression method of the support program based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand expression method can be provided. If the user is relaxed, an expression method including detailed explanations can be provided. Furthermore, if the user is in a hurry, a short expression method that gets to the point can be provided. In this way, more effective support can be provided by adjusting the expression method of the support program according to the user's emotions.

[0093] When providing a support program, the provider can adjust the level of detail of the program based on the importance of the child. For example, if the child's diagnosis is severe, a detailed support program is provided. The provider can also provide a simple and easy-to-understand program for a young child. Furthermore, if the parent's concerns are serious, the provider can provide a program that includes specific solutions. This allows for more appropriate support by adjusting the level of detail of the program based on the importance of the child.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The collection unit collects the child's diagnosis or age, and parental concerns. For example, the collection unit can collect diagnosis details such as medical diagnosis, psychological diagnosis, and developmental diagnosis. The collection unit can also collect information based on age categories such as 0-3 years old and 4-6 years old. Furthermore, the collection unit can collect parental concerns such as child-rearing concerns, education concerns, and health concerns. Step 2: The provision unit provides a support program based on the information collected by the collection unit. For example, the provision unit can provide support programs such as counseling, educational programs, and medical support. The provision unit uses AI to analyze the collected information and select the optimal support program. Step 3: The reception unit receives the consultation content from the user. For example, the reception unit can receive the consultation content via online chat, telephone, face-to-face, or other methods. Step 4: The advice providing unit provides advice based on the consultation content received by the reception unit. For example, the advice providing unit can provide advice using an AI character. The advice providing unit uses AI to analyze the consultation content and generate appropriate advice.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0098] 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.

[0099] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0114] 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.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the 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.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 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.

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

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department collects information on the diagnosis or age of the child and the concerns of the parents. a providing unit that provides a support program based on the information collected by the collecting unit; a reception unit that receives inquiries from users; an advice providing unit that provides advice based on the consultation content received by the reception unit; A system characterized by:

2. The advice providing unit Providing advice using AI characters The system of claim 1 .

3. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The system of claim 1 .

4. The collecting unit Analyze the child's past diagnostic history and select the most appropriate collection method The system of claim 1 .

5. The collecting unit When collecting information, filter it based on the child's current living situation and the parent's areas of interest. The system of claim 1 .

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions. The system of claim 1 .

7. The collecting unit When collecting information, prioritize collecting highly relevant information by taking into account the user's geographical location information. The system of claim 1 .

8. The collecting unit When collecting information, we analyze your social media activity and collect relevant information. The system of claim 1 .

9. The providing unit The method estimates the user's emotions and adjusts the way the support program expresses itself based on the estimated user emotions. The system of claim 1 .

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