system

Abstract

The system according to this embodiment aims to support general pediatricians in the diagnosis and treatment of developmental disorders. [Solution] The system according to this embodiment comprises a reception unit, a developmental analysis unit, a feature detection unit, a disease probability calculation unit, a medical interview support unit, and an advice provision unit. The reception unit receives input of photos and videos of children from parents. The developmental analysis unit analyzes the data received by the reception unit. The feature detection unit detects features specific to developmental disorders based on the data analyzed by the developmental analysis unit. The disease probability calculation unit calculates the probability of disease onset based on the features detected by the feature detection unit. The medical interview support unit presents observation points during the medical interview based on the probability calculated by the disease probability calculation unit. The advice provision unit provides advice on treatment plans, how to interact with the child at home, and family care based on the information presented by the medical interview support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that there are few specialists in developmental disorders, and it is difficult for general pediatricians to perform appropriate medical treatment.

[0005] The system according to the embodiment aims to assist general pediatricians in diagnosing and treating developmental disorders.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a developmental analysis unit, a feature detection unit, a disease probability calculation unit, a medical interview support unit, and an advice provision unit. The reception unit receives input of photos and videos of children from parents. The developmental analysis unit analyzes the data received by the reception unit. The feature detection unit detects features specific to developmental disorders based on the data analyzed by the developmental analysis unit. The disease probability calculation unit calculates the probability of disease onset based on the features detected by the feature detection unit. The medical interview support unit presents observation points during the medical interview based on the probability calculated by the disease probability calculation unit. The advice provision unit provides advice on treatment plans, how to interact with the child at home, and family care based on the information presented by the medical interview support unit. [Effects of the Invention]

[0007] The system according to this embodiment can support the diagnosis and treatment of developmental disorders even for general pediatricians. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The medical support system according to an embodiment of the present invention is an AI-powered medical support system designed to enable children with developmental disabilities and their families to receive appropriate medical care. This system allows parents to upload photos and videos of their children, and the AI ​​analyzes this data to calculate the probability of disease occurrence, providing support during consultations and suggesting treatment plans. For example, when parents upload photos and videos of their children, the AI ​​analyzes this data and calculates the probability of disease occurrence. For instance, it might present probabilities such as "Autism 74%, ADHD 15%, Comorbidity 11%." Furthermore, the AI ​​suggests points that doctors should particularly observe during consultations (e.g., whether the child makes eye contact, whether they point with interest, etc.). Finally, the AI ​​provides specific advice on treatment plans, how to interact with the child at home, and family care. This mechanism enables general pediatricians to treat developmental disabilities, eliminating delays in treatment due to a shortage of specialists. Parents can also receive appropriate medical care online without having to travel long distances. Additionally, the medical support information provided by the AI ​​can shorten doctors' consultation times and is expected to improve medical reimbursement. For example, when parents input information they have recorded about their child's developmental progress into the AI, the AI ​​analyzes it using natural language processing and extracts important information. It can also analyze images and videos of the child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. This allows the AI ​​to detect characteristics specific to developmental disorders and calculate the probability of the child having the disorder. In this way, the AI-powered developmental diagnosis support system provides significant support to children with developmental disorders and their families, enabling early diagnosis and appropriate support. As a result, the diagnosis support system can ensure that children with developmental disorders and their families receive appropriate medical care.

[0029] The medical support system according to this embodiment comprises a reception unit, a growth analysis unit, a feature detection unit, a disease probability calculation unit, a medical interview support unit, and an advice provision unit. The reception unit receives input of photos and videos of children from parents. Input of photos and videos of children from parents includes, but is not limited to, resolution, format, shooting conditions, etc. The reception unit can, for example, allow parents to upload photos and videos taken with their smartphones. The reception unit can also accept high-resolution photos and videos taken with digital cameras by parents. Furthermore, the reception unit can also allow parents to upload photos and videos they have taken in the past from cloud storage. The growth analysis unit analyzes the data received by the reception unit. The growth analysis unit analyzes, for example, information on the child's growth progress recorded by parents using natural language processing and extracts important information. Natural language processing includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., and is not limited to such examples. The growth analysis unit analyzes, for example, diaries and memos recorded by parents and extracts important information regarding the child's growth. Furthermore, the developmental analysis unit can analyze text data entered by parents and extract important keywords related to the child's development. In addition, the developmental analysis unit can analyze audio data recorded by parents and extract important information related to the child's development. The feature detection unit detects features specific to developmental disorders based on the data analyzed by the developmental analysis unit. For example, the feature detection unit analyzes images and videos of a child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. The detection of facial expressions, eye movements, tantrums, and specific behaviors includes, but is not limited to, facial recognition technology and behavioral analysis algorithms. For example, the feature detection unit analyzes images of a child's face to detect smiles and angry expressions. The feature detection unit can also analyze videos of a child to detect eye movements and tantrums. Furthermore, the feature detection unit can analyze a child's behavioral patterns and detect specific behaviors. The disease probability calculation unit calculates the probability of disease incidence based on the features detected by the feature detection unit. The disease probability calculation unit calculates the probability of disease occurrence, for example, "autism 74%, ADHD 15%, comorbidity 11%."The calculation of disease incidence probability includes, but is not limited to, the statistical data used and the calculation algorithm. For example, the incidence probability calculation unit calculates disease incidence probability based on past medical data. The incidence probability calculation unit can also calculate disease incidence probability using a statistical model. Furthermore, the incidence probability calculation unit can also calculate disease incidence probability using a machine learning algorithm. The medical interview support unit presents observation points during the medical interview based on the probability calculated by the incidence probability calculation unit. For example, the medical interview support unit presents points that the doctor should particularly observe during the examination (e.g., whether the patient makes eye contact, whether the patient points with interest). The presentation of observation points includes, but is not limited to, whether the patient makes eye contact, whether the patient points with interest. For example, the medical interview support unit presents points that the doctor should observe during the examination in a list format. Furthermore, the medical interview support unit can also visually present observation points in graphs or charts. Furthermore, the medical interview support unit can also present observation points audibly. The advice-providing unit provides advice on treatment plans, how to interact with the child at home, and family care based on the information presented by the medical interview support unit. The advice-providing unit provides specific advice on treatment plans, how to interact with the child at home, and family care, for example. The advice provided includes, but is not limited to, treatment steps and specific guidelines for actions at home. The advice-providing unit provides treatment plans in text format, for example. The advice-providing unit can also provide video guides on how to interact with the child at home. Furthermore, the advice-providing unit can provide advice on family care in audio format. In this way, the medical support system according to the embodiment can enable children with developmental disabilities and their families to receive appropriate medical care.

[0030] The reception unit accepts input of photos and videos of children from parents. This input from parents includes, but is not limited to, information such as resolution, format, and shooting conditions. For example, parents can upload photos and videos taken with their smartphones. The reception unit can also accept high-resolution photos and videos taken with digital cameras. Furthermore, parents can upload photos and videos they have taken in the past from cloud storage. When accepting this data, the reception unit has the functionality to automatically check the quality and format of the data and perform format conversion or resolution adjustment as needed. For example, if a photo taken with a smartphone is low resolution, the reception unit will automatically apply an algorithm to improve the resolution. Also, if video data in a different format is uploaded, the reception unit will convert it to a unified format so that the subsequent analysis unit can process it efficiently. Furthermore, the reception unit also has the functionality to add metadata to the data uploaded by parents. For example, it automatically extracts information such as the date and time of shooting, location of shooting, and photographer's information and stores it in the database. This allows for the provision of more detailed and accurate information in subsequent analysis and diagnosis. The reception area features an intuitive and user-friendly interface for parents uploading data, making it easy for them to provide data. For example, it includes drag-and-drop functionality for uploading photos and videos, as well as integration with cloud storage. This allows parents to provide necessary data without hassle, improving the overall efficiency of the system.

[0031] The Developmental Analysis Unit analyzes data received by the Reception Unit. For example, the Developmental Analysis Unit analyzes information on a child's developmental progress recorded by parents using natural language processing and extracts important information. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the Developmental Analysis Unit analyzes diaries and notes recorded by parents and extracts important information about the child's development. The Developmental Analysis Unit can also analyze text data entered by parents and extract important keywords related to the child's development. Furthermore, the Developmental Analysis Unit can analyze audio data recorded by parents and extract important information about the child's development. The Developmental Analysis Unit utilizes AI technology when analyzing this data. For example, it uses the latest generative AI and large-scale language models (LLMs) for natural language processing to deeply understand the meaning of text data and extract important information. Specifically, it extracts specific episodes and behavioral patterns related to the child's development from diaries and notes recorded by parents and stores them in a database. Furthermore, for analyzing audio data, speech recognition technology is used to convert speech into text, and then natural language processing is applied. This allows for efficient analysis of information recorded orally by parents. In addition, the developmental analysis unit also supports the analysis of image and video data. For example, it analyzes images and videos of children's faces to detect changes in facial expressions and movements. This uses facial recognition technology and motion analysis algorithms to provide a detailed understanding of the child's developmental status. The developmental analysis unit integrates these analysis results to generate a comprehensive report on the child's development. This report is provided to doctors and parents and serves as important material for objectively evaluating the child's developmental status.

[0032] The feature detection unit detects characteristics specific to developmental disorders based on data analyzed by the developmental analysis unit. For example, the feature detection unit analyzes images and videos of a child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. The detection of facial expressions, eye movements, tantrums, and specific behaviors includes, but is not limited to, facial recognition technology and behavioral analysis algorithms. For example, the feature detection unit analyzes images of a child's face to detect expressions such as smiles and anger. It can also analyze videos of a child to detect eye movements and tantrums. Furthermore, the feature detection unit can analyze a child's behavioral patterns to detect specific behaviors. The feature detection unit makes full use of AI technology when performing these analyses. For example, it applies the latest algorithms using deep learning to facial recognition technology to analyze children's facial expressions with high accuracy. Specifically, it can detect not only basic expressions such as smiles, anger, and sadness, but also subtle changes in facial expressions. In addition, eye-tracking technology is used to understand in real time which direction the child is looking when analyzing eye movements. This allows for the assessment of a child's interest in specific objects or people. Furthermore, behavioral analysis algorithms are used to analyze the child's movement patterns in detail in order to detect tantrums and specific behaviors. For example, the child's movements are analyzed frame by frame from video data to detect specific behavioral patterns. This includes movements such as walking, hand movements, and body swaying. The feature detection unit integrates these analysis results to generate a detailed report for assessing the possibility of developmental disorders in the child. This report is provided to doctors and parents and serves as important material for objectively evaluating the child's developmental status.

[0033] The disease incidence probability calculation unit calculates the probability of disease incidence based on features detected by the feature detection unit. For example, the disease incidence probability calculation unit calculates disease incidence probabilities such as "autism 74%, ADHD 15%, comorbidity 11%". The calculation of disease incidence probability includes, but is not limited to, the statistical data used and the calculation algorithm. For example, the disease incidence probability calculation unit calculates disease incidence probability based on past medical data. The disease incidence probability calculation unit can also calculate disease incidence probability using statistical models. Furthermore, the disease incidence probability calculation unit can also calculate disease incidence probability using machine learning algorithms. The disease incidence probability calculation unit utilizes AI technology when analyzing this data. For example, it constructs statistical models based on past medical data and uses them to calculate disease incidence probability for new data. Specifically, it constructs regression models or classification models to predict disease incidence probability based on features extracted from past medical data. It also uses machine learning algorithms to learn data patterns and calculate disease incidence probability with higher accuracy. For example, a neural network model using deep learning is constructed, and child characteristic data is taken as input to output the probability of disease occurrence. This model is trained using past medical data and has high predictive accuracy even with new data. Furthermore, the disease probability calculation unit also has a function to visually display the calculated disease probability. For example, the disease probability of each disease can be visually displayed using graphs and charts, making it easy for doctors and parents to understand intuitively. As a result, the disease probability calculation unit can accurately assess the possibility of developmental disorders in children and support the determination of appropriate diagnoses and treatment plans.

[0034] The medical interview support unit presents observation points during the medical interview based on probabilities calculated by the disease probability calculation unit. For example, the medical interview support unit presents points that doctors should particularly observe during the examination (such as whether the patient makes eye contact or points with interest). These observation points include, but are not limited to, whether the patient makes eye contact or points with interest. The medical interview support unit can also present points that doctors should observe during the examination in a list format. Furthermore, the medical interview support unit can visually present observation points using graphs and charts. In addition, the medical interview support unit can present observation points verbally. The medical interview support unit utilizes AI technology when presenting these observation points. For example, it prioritizes and presents the most important observation points based on probabilities calculated by the disease probability calculation unit. Specifically, the AI ​​analyzes past medical data and extracts important observation points related to specific diseases. This allows doctors to perform observations efficiently and effectively during examinations. The medical interview support unit also provides an interactive user interface when visually presenting observation points. For example, the system includes a function that allows doctors to tap on observation points using a tablet or smartphone to display detailed information. This enables doctors to quickly access necessary information during consultations. Furthermore, the consultation support unit also includes a function that presents observation points aloud. For instance, it provides a function where a voice assistant reads out observation points so that doctors can check them without using their hands during consultations. This allows doctors to efficiently acquire necessary information while concentrating on the consultation. Through these functions, the consultation support unit provides assistance to doctors in making appropriate diagnoses without overlooking important observation points during consultations.

[0035] The Advice Department provides advice on treatment plans, how to interact with children at home, and family care based on information presented by the Medical Interview Support Department. The Advice Department provides specific advice on treatment plans, how to interact with children at home, and family care. This advice includes, but is not limited to, treatment steps and specific behavioral guidelines at home. For example, the Advice Department provides treatment plans in text format. It can also provide video guides on how to interact with children at home. Furthermore, it can provide advice on family care in audio format. The Advice Department utilizes AI technology when providing this advice. For example, it builds algorithms to propose optimal treatment plans based on past medical data and treatment results. Specifically, the AI ​​learns from past successes and failures to propose the most suitable treatment plan for each individual child. Advice on how to interact with children at home includes specific behavioral guidelines and communication methods. For example, it provides detailed guidelines on how to respond when a child exhibits certain behaviors and how to communicate in daily life. This includes video and audio guides to help parents understand the information intuitively. Furthermore, the advice department also provides advice on caring for the whole family. For example, they offer advice on how parents can cope with stress and how the whole family can relax. This includes counseling by mental health professionals and introductions to relaxation techniques. The advice department also customizes this advice to suit the individual family's situation and needs. For instance, they select and provide the most appropriate advice based on the family's structure and living environment. In this way, the advice department can support the health and well-being of the entire family, not just the children.

[0036] The developmental analysis unit can analyze information on a child's developmental progress recorded by parents using natural language processing and extract important information. For example, the developmental analysis unit can analyze diaries and memos recorded by parents and extract important information about the child's development. For example, the developmental analysis unit can analyze text data entered by parents and extract important keywords related to the child's development. For example, the developmental analysis unit can analyze audio data recorded by parents and extract important information about the child's development. By analyzing the developmental progress information recorded by parents and extracting important information, more accurate medical support information can be provided. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the developmental analysis unit may be performed using AI, for example, or without AI. For example, the developmental analysis unit can input text data recorded by parents into a generating AI and have the generating AI perform the extraction of important information.

[0037] The feature detection unit can analyze images and videos of a child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. For example, the feature detection unit can analyze a child's face image to detect smiles and angry expressions. The feature detection unit can also analyze a child's video to detect eye movements and tantrums. The feature detection unit can also analyze a child's behavior patterns to detect specific behaviors. This allows for the accurate detection of characteristics specific to developmental disorders by analyzing images and videos of a child's face. The detection of facial expressions, eye movements, tantrums, and specific behaviors includes, but is not limited to, facial recognition technology and behavioral analysis algorithms. Some or all of the above-described processes in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input a child's face image into a generating AI and have the generating AI perform facial expression detection.

[0038] The disease incidence probability calculation unit can calculate disease incidence probabilities such as "autism 74%, ADHD 15%, comorbidity 11%." The disease incidence probability calculation unit calculates disease incidence probabilities based on past medical data, for example. The disease incidence probability calculation unit can also calculate disease incidence probabilities using statistical models, for example. The disease incidence probability calculation unit can also calculate disease incidence probabilities using machine learning algorithms, for example. This allows for the provision of specific probability information that doctors can refer to during medical consultations by calculating disease incidence probabilities. The calculation of disease incidence probabilities includes, but is not limited to, the statistical data used and the calculation algorithms. Some or all of the above-described processes in the disease incidence probability calculation unit may be performed using AI, for example, or without AI. For example, the disease incidence probability calculation unit can input past medical data into a generating AI and have the generating AI perform the calculation of disease incidence probabilities.

[0039] The medical history support unit can present points that physicians should particularly observe during consultations. For example, the medical history support unit can present points that physicians should observe during consultations in a list format. The medical history support unit can also present observation points visually, for example, in graphs or charts. The medical history support unit can also present observation points verbally, for example. This improves the accuracy of consultations by presenting points that physicians should particularly observe during consultations. The presentation of observation points includes, but is not limited to, whether the patient makes eye contact or points with interest. Some or all of the above processing in the medical history support unit may be performed using, for example, AI, or not using AI. For example, the medical history support unit can input observation points into a generating AI and have the generating AI perform the presentation of observation points.

[0040] The advice-providing unit can provide specific advice on treatment plans, how to interact with children at home, and family care. For example, the advice-providing unit can provide treatment plans in text format. For example, the advice-providing unit can provide video guides on how to interact with children at home. For example, the advice-providing unit can provide advice on family care in audio format. By providing specific advice, the way children are interacted with at home and family care are improved. The advice provided may include, but is not limited to, treatment steps and specific guidelines for actions at home. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input treatment plans into a generating AI and have the generating AI provide specific advice.

[0041] The reception unit can analyze the parent's past upload history and select the optimal data acquisition method. For example, the reception unit can analyze the time periods when the parent frequently uploaded in the past and prompt the parent to upload during those times. The reception unit can also analyze the devices and apps the parent has used in the past and suggest the optimal data acquisition method. For example, the reception unit can select a data acquisition method for a specific event or situation from the parent's past upload history. This allows the reception unit to suggest the optimal data acquisition method by analyzing past upload history. The optimal data acquisition method includes, but is not limited to, data type, acquisition frequency, and acquisition method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the parent's past upload history into a generating AI and have the generating AI select the optimal data acquisition method.

[0042] The reception unit can filter photos and videos based on the parent's current living situation and areas of interest when acquiring them. For example, if the parent inputs their current living situation, the AI ​​can suggest appropriate methods for acquiring photos and videos based on that situation. The reception unit can also have the AI ​​prioritize the acquisition of relevant data based on the parent's areas of interest (e.g., specific play or learning activities). For example, if the parent is in a specific living situation (e.g., traveling or visiting a hospital), the AI ​​can suggest appropriate methods for acquiring data based on that situation. This allows for the acquisition of more relevant data by filtering the data based on the parent's living situation and areas of interest. The parent's current living situation and areas of interest include, but are not limited to, survey results and social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the parent's living situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0043] The reception unit can prioritize the acquisition of highly relevant data by considering the parent's geographical location information when acquiring data. For example, if the parent is in a specific region, the reception unit will prioritize the acquisition of data related to that region. For example, if the parent is traveling, the reception unit can also acquire relevant data based on geographical information of the travel destination. For example, if the parent is at home, the reception unit can also acquire relevant data based on information about the area around the home. This allows for the priority acquisition of highly relevant data by considering the parent's geographical location information. The parent's geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the parent's geographical location information into a generating AI and have the generating AI acquire highly relevant data.

[0044] The reception unit can analyze the parent's social media activity and obtain relevant data when acquiring data. For example, the reception unit can obtain relevant data based on information shared by the parent on social media. The reception unit can also obtain relevant data based on information about accounts followed by the parent on social media. The reception unit can also obtain relevant data based on information about groups and events the parent participates in on social media. This allows for the efficient acquisition of relevant data by analyzing the parent's social media activity. The parent's social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the processing described above in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the parent's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0045] The growth analysis unit can adjust the level of detail of the analysis based on the importance of the data during growth analysis. For example, the growth analysis unit can perform a detailed analysis on data with high importance. For example, the growth analysis unit can also perform a simplified analysis on data with low importance. The growth analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Data importance includes, but is not limited to, data freshness, relevance, and reliability. Some or all of the above processing in the growth analysis unit may be performed using, for example, AI, or not using AI. For example, the growth analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The growth analysis unit can apply different analysis algorithms depending on the data category during growth analysis. For example, the growth analysis unit can apply an image analysis algorithm to video data. For example, the growth analysis unit can also apply a natural language processing algorithm to text data. For example, the growth analysis unit can also apply a speech analysis algorithm to audio data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, image data, text data, and audio data. Some or all of the above processing in the growth analysis unit may be performed using AI, for example, or without AI. For example, the growth analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0047] The growth analysis unit can determine the priority of analysis based on the data submission date during growth analysis. For example, the growth analysis unit may prioritize the analysis of the most recent data. For example, the growth analysis unit may postpone the analysis of older data. For example, the growth analysis unit may also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. The data submission date includes, but is not limited to, the submission date and time, and the submission frequency. Some or all of the above processing in the growth analysis unit may be performed using AI, for example, or without AI. For example, the growth analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0048] The growth analysis unit can adjust the order of analysis based on the relevance of the data during growth analysis. For example, the growth analysis unit can prioritize the analysis of highly relevant data. For example, it can postpone the analysis of less relevant data. The growth analysis unit can also adjust the analysis schedule based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, but is not limited to, interrelationships and commonalities between data. Some or all of the above processing in the growth analysis unit may be performed using AI, for example, or without AI. For example, the growth analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0049] The feature detection unit can improve the accuracy of feature detection by considering the interrelationships between data during feature detection. For example, the feature detection unit can detect features by considering the interrelationships between video data and text data. The feature detection unit can also detect features by considering the interrelationships between audio data and image data. The feature detection unit can also detect features by considering the interrelationships between multiple data sources. This improves the accuracy of feature detection by considering the interrelationships between data. Examples of data interrelationships include, but are not limited to, correlation analysis and co-occurrence networks. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input the interrelationships between data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0050] The feature detection unit can perform feature detection while considering the attribute information of the data submitter. For example, the feature detection unit can detect features by considering the submitter's age and gender. The feature detection unit can also detect features by considering the submitter's living environment and background. The feature detection unit can also detect features by considering the submitter's health status and past medical history. This makes it possible to perform more accurate feature detection by considering the attribute information of the data submitter. The attribute information of the data submitter includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without using AI. For example, the feature detection unit can input the submitter's attribute information into a generating AI and have the generating AI perform feature detection.

[0051] The feature detection unit can perform feature detection while considering the geographical distribution of the data. For example, the feature detection unit can prioritize the detection of features associated with a specific region. The feature detection unit can also improve the accuracy of feature detection based on geographical distribution. For example, the feature detection unit can adjust the order of feature detection while considering geographical distribution. This improves the accuracy of feature detection by considering the geographical distribution of the data. The geographical distribution of the data includes, but is not limited to, data distribution by region and geographical bias. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the detection accuracy.

[0052] The feature detection unit can improve the accuracy of feature detection by referring to relevant literature for the data during feature detection. For example, the feature detection unit sets feature detection criteria based on relevant literature. The feature detection unit can also improve the accuracy of feature detection by referring to relevant literature. The feature detection unit can also adjust the feature detection algorithm based on relevant literature. This improves the accuracy of feature detection by referring to relevant literature for the data. Relevant literature for the data includes, but is not limited to, literature search methods and citation criteria. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input relevant literature data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0053] The disease probability calculation unit can predict the current probability by referring to past disease data when calculating the disease probability. For example, the disease probability calculation unit predicts the current disease probability based on past disease data. The disease probability calculation unit can also, for example, analyze past disease data and calculate the current disease probability. The disease probability calculation unit can also, for example, apply an algorithm to predict the current disease probability by referring to past disease data. This allows for accurate prediction of the current disease probability by referring to past disease data. Past disease data includes, but is not limited to, the type of database and the reliability of the data. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input past disease data into a generating AI and have the generating AI perform a prediction of the current disease probability.

[0054] The disease probability calculation unit can apply different calculation methods to each data category when calculating disease probability. For example, when calculating the disease probability of autism, the disease probability calculation unit applies a specific algorithm. For example, when calculating the disease probability of ADHD, the disease probability calculation unit can also apply a different algorithm. For example, when calculating the disease probability of comorbidities, the disease probability calculation unit can also apply a combination of multiple algorithms. This improves the accuracy of disease probability calculation by applying different calculation methods to each data category. Different calculation methods for each data category include, but are not limited to, image data, text data, and audio data. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input data categories into a generating AI and have the generating AI perform the application of calculation methods.

[0055] The disease probability calculation unit can analyze changes in probability based on the data submission date when calculating disease probability. For example, the disease probability calculation unit can analyze changes in disease probability based on the latest data. The disease probability calculation unit can also analyze changes in disease probability based on older data. For example, the disease probability calculation unit can apply an algorithm to analyze changes in disease probability based on the submission date. This allows for accurate understanding of changes in disease probability by analyzing changes in probability based on the data submission date. Changes in probability include, but are not limited to, temporal changes and seasonal variations. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or without AI. For example, the disease probability calculation unit can input the data submission date to a generating AI and have the generating AI perform the analysis of changes in probability.

[0056] The disease probability calculation unit can analyze probabilities by referring to relevant market data when calculating disease probability. For example, the disease probability calculation unit analyzes disease probability based on relevant market data. The disease probability calculation unit can also improve the accuracy of disease probability calculation by referring to relevant market data. For example, the disease probability calculation unit can also analyze changes in disease probability based on relevant market data. This improves the accuracy of disease probability calculation by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and economic indicators. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input relevant market data into a generating AI and have the generating AI perform the probability analysis.

[0057] The medical interview support unit can optimize the current medical interview by referring to past medical interview data during medical interview support. For example, the medical interview support unit optimizes the current medical interview based on past medical interview data. The medical interview support unit can also optimize the current medical interview by analyzing past medical interview data. The medical interview support unit can also apply an algorithm to optimize the current medical interview by referring to past medical interview data. This allows the current medical interview to be optimized by referring to past medical interview data. Past medical interview data includes, but is not limited to, the type of database and the reliability of the data. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input past medical interview data into a generating AI and have the generating AI perform the optimization of the current medical interview.

[0058] The medical interview support unit can apply different medical interview support methods to each data category during medical interviews. For example, the medical interview support unit can apply a specific method to medical interview support for autism. For example, the medical interview support unit can apply a different method to medical interview support for ADHD. For example, the medical interview support unit can combine and apply multiple methods to medical interview support for comorbidities. This improves the accuracy of medical interview support by applying different medical interview support methods to each data category. Different medical interview support methods for each data category include, but are not limited to, image data, text data, and audio data. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input data categories into a generating AI and have the generating AI perform the application of medical interview support methods.

[0059] The medical interview support unit can analyze changes in medical interviews based on the data submission date during medical interview support. For example, the medical interview support unit can analyze changes in medical interviews based on the latest data. For example, the medical interview support unit can also analyze changes in medical interviews based on older data. For example, the medical interview support unit can apply an algorithm to analyze changes in medical interviews based on the submission date. This allows for an accurate understanding of changes in medical interviews by analyzing them based on the data submission date. Changes in medical interviews include, but are not limited to, temporal changes and seasonal variations. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input the data submission date into a generating AI and have the generating AI perform the analysis of changes in medical interviews.

[0060] The medical interview support unit can analyze medical interviews by referring to relevant market data during the interview process. For example, the medical interview support unit analyzes medical interviews based on relevant market data. The medical interview support unit can also improve the accuracy of medical interview analysis by referring to relevant market data. For example, the medical interview support unit can also analyze changes in medical interviews based on relevant market data. This improves the accuracy of medical interview analysis by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and economic indicators. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input relevant market data into a generating AI and have the generating AI perform the medical interview analysis.

[0061] The advice-providing unit can optimize current advice by referring to past advice data when providing advice. For example, the advice-providing unit optimizes current advice based on past advice data. The advice-providing unit can also optimize current advice by analyzing past advice data. The advice-providing unit can also apply an algorithm to optimize current advice by referring to past advice data. This allows for the optimization of current advice by referring to past advice data. Past advice data includes, but is not limited to, database type and data reliability. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input past advice data into a generating AI and have the generating AI perform the optimization of current advice.

[0062] The advice-providing unit can apply different advice methods to each data category when providing advice. For example, the advice-providing unit can apply a specific method to advice for autism. For example, the advice-providing unit can apply a different method to advice for ADHD. For example, the advice-providing unit can combine and apply multiple methods to advice for comorbidities. This improves the accuracy of the advice by applying different advice methods to each data category. Different advice methods for each data category include, but are not limited to, image data, text data, and audio data. Some or all of the processing described above in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input data categories into a generating AI and have the generating AI perform the application of advice methods.

[0063] The advice-providing unit can analyze changes in advice based on the data submission date when providing advice. For example, the advice-providing unit can analyze changes in advice based on the latest data. For example, the advice-providing unit can also analyze changes in advice based on older data. For example, the advice-providing unit can apply an algorithm to analyze changes in advice based on the submission date. This allows for an accurate understanding of changes in advice by analyzing changes in advice based on the data submission date. Changes in advice include, but are not limited to, temporal changes and seasonal variations. Some or all of the above processing in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input the data submission date into a generating AI and have the generating AI perform the analysis of changes in advice.

[0064] The advice-providing unit can analyze advice by referring to relevant market data when providing advice. For example, the advice-providing unit analyzes advice based on relevant market data. The advice-providing unit can also improve the accuracy of the advice analysis by referring to relevant market data. For example, the advice-providing unit can also analyze changes in advice based on relevant market data. This improves the accuracy of the advice analysis by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and economic indicators. Some or all of the above processing in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input relevant market data into a generating AI and have the generating AI perform the analysis of the advice.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The medical support system can further analyze the parent's past upload history and select the optimal data acquisition method. For example, it can analyze the times of day when the parent frequently uploaded in the past and prompt uploads during those times. It can also analyze the devices and apps the parent has used in the past and suggest the optimal data acquisition method. It can also select the data acquisition method for specific events or situations based on the parent's past upload history. In this way, by analyzing past upload history, the system can suggest the optimal data acquisition method. The optimal data acquisition method includes, but is not limited to, data type, acquisition frequency, and acquisition method. The reception desk can input the parent's past upload history into the generating AI and have the generating AI select the optimal data acquisition method.

[0067] The medical support system can further filter data based on the parents' current living situation and areas of interest. For example, when a parent inputs their current living situation, the AI ​​suggests appropriate ways to acquire photos and videos based on that situation. The AI ​​can also prioritize acquiring relevant data based on the parents' areas of interest (e.g., specific play or learning activities). If a parent is in a specific living situation (e.g., traveling or visiting a hospital), the AI ​​can also suggest appropriate ways to acquire data based on that situation. This allows for the acquisition of more relevant data by filtering data based on the parents' living situation and areas of interest. Examples of the parents' current living situation and areas of interest include, but are not limited to, survey results and social media activity. The reception desk can input data on the parents' living situation and areas of interest into the generating AI and have the generating AI perform data filtering.

[0068] The medical support system can further prioritize the acquisition of highly relevant data by considering the parent's geographical location. For example, if the parent is in a specific area, it will prioritize the acquisition of data related to that area. If the parent is traveling, it can also acquire relevant data based on the geographical information of the travel destination. If the parent is at home, it can also acquire relevant data based on information about the area around their home. In this way, by considering the parent's geographical location, it can prioritize the acquisition of highly relevant data. The parent's geographical location information includes, but is not limited to, GPS data and location information services. The reception desk can input the parent's geographical location information into the generating AI and have the generating AI acquire highly relevant data.

[0069] The medical support system can further analyze parents' social media activity and acquire relevant data. For example, it can acquire relevant data based on information parents have shared on social media. It can also acquire relevant data based on information about accounts parents follow on social media. It can also acquire relevant data based on information about groups and events parents participate in on social media. This allows for the efficient acquisition of relevant data by analyzing parents' social media activity. Parents' social media activity includes, but is not limited to, posts, the number of likes, and comments. The reception desk can input the parents' social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk accepts photos and videos of children from parents. Parental input includes resolution, format, and shooting conditions. For example, parents can upload photos and videos taken with their smartphones. High-resolution photos and videos taken with digital cameras, as well as photos and videos taken previously and stored in cloud storage, can also be accepted. Step 2: The developmental analysis unit analyzes the data received by the reception unit. For example, it analyzes the child's developmental progress information recorded by the parents using natural language processing and extracts important information. Natural language processing includes morphological analysis, grammatical analysis, and semantic analysis. It can also analyze diaries, notes, and audio data recorded by the parents to extract important information about the child's development. Step 3: The feature detection unit detects features specific to developmental disorders based on the data analyzed by the developmental analysis unit. For example, it analyzes images and videos of children's faces to detect facial expressions, eye movements, tantrums, and specific behaviors. This uses facial recognition technology and behavioral analysis algorithms. Step 4: The disease probability calculation unit calculates the disease probability based on the features detected by the feature detection unit. For example, it calculates disease probabilities such as "autism 74%, ADHD 15%, comorbidity 11%". This uses statistical data, calculation algorithms, and machine learning algorithms. Step 5: The medical interview support unit presents observation points during the medical interview based on the probabilities calculated by the disease probability calculation unit. For example, it presents points that doctors should particularly observe during the examination (such as whether the patient makes eye contact or points with interest) in the form of a list, graph, chart, or audio. Step 6: The advice department provides advice on treatment plans, how to interact with the child at home, and family care based on the information presented by the medical interview support department. For example, they may provide treatment plans in text format, video guides on how to interact with the child at home, and audio advice on family care.

[0072] (Example of form 2) The medical support system according to an embodiment of the present invention is an AI-powered medical support system designed to enable children with developmental disabilities and their families to receive appropriate medical care. This system allows parents to upload photos and videos of their children, and the AI ​​analyzes this data to calculate the probability of disease occurrence, providing support during consultations and suggesting treatment plans. For example, when parents upload photos and videos of their children, the AI ​​analyzes this data and calculates the probability of disease occurrence. For instance, it might present probabilities such as "Autism 74%, ADHD 15%, Comorbidity 11%." Furthermore, the AI ​​suggests points that doctors should particularly observe during consultations (e.g., whether the child makes eye contact, whether they point with interest, etc.). Finally, the AI ​​provides specific advice on treatment plans, how to interact with the child at home, and family care. This mechanism enables general pediatricians to treat developmental disabilities, eliminating delays in treatment due to a shortage of specialists. Parents can also receive appropriate medical care online without having to travel long distances. Additionally, the medical support information provided by the AI ​​can shorten doctors' consultation times and is expected to improve medical reimbursement. For example, when parents input information they have recorded about their child's developmental progress into the AI, the AI ​​analyzes it using natural language processing and extracts important information. It can also analyze images and videos of the child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. This allows the AI ​​to detect characteristics specific to developmental disorders and calculate the probability of the child having the disorder. In this way, the AI-powered developmental diagnosis support system provides significant support to children with developmental disorders and their families, enabling early diagnosis and appropriate support. As a result, the diagnosis support system can ensure that children with developmental disorders and their families receive appropriate medical care.

[0073] The medical support system according to this embodiment comprises a reception unit, a growth analysis unit, a feature detection unit, a disease probability calculation unit, a medical interview support unit, and an advice provision unit. The reception unit receives input of photos and videos of children from parents. Input of photos and videos of children from parents includes, but is not limited to, resolution, format, shooting conditions, etc. The reception unit can, for example, allow parents to upload photos and videos taken with their smartphones. The reception unit can also accept high-resolution photos and videos taken with digital cameras by parents. Furthermore, the reception unit can also allow parents to upload photos and videos they have taken in the past from cloud storage. The growth analysis unit analyzes the data received by the reception unit. The growth analysis unit analyzes, for example, information on the child's growth progress recorded by parents using natural language processing and extracts important information. Natural language processing includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., and is not limited to such examples. The growth analysis unit analyzes, for example, diaries and memos recorded by parents and extracts important information regarding the child's growth. Furthermore, the developmental analysis unit can analyze text data entered by parents and extract important keywords related to the child's development. In addition, the developmental analysis unit can analyze audio data recorded by parents and extract important information related to the child's development. The feature detection unit detects features specific to developmental disorders based on the data analyzed by the developmental analysis unit. For example, the feature detection unit analyzes images and videos of a child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. The detection of facial expressions, eye movements, tantrums, and specific behaviors includes, but is not limited to, facial recognition technology and behavioral analysis algorithms. For example, the feature detection unit analyzes images of a child's face to detect smiles and angry expressions. The feature detection unit can also analyze videos of a child to detect eye movements and tantrums. Furthermore, the feature detection unit can analyze a child's behavioral patterns and detect specific behaviors. The disease probability calculation unit calculates the probability of disease incidence based on the features detected by the feature detection unit. The disease probability calculation unit calculates the probability of disease occurrence, for example, "autism 74%, ADHD 15%, comorbidity 11%."The calculation of disease incidence probability includes, but is not limited to, the statistical data used and the calculation algorithm. For example, the incidence probability calculation unit calculates disease incidence probability based on past medical data. The incidence probability calculation unit can also calculate disease incidence probability using a statistical model. Furthermore, the incidence probability calculation unit can also calculate disease incidence probability using a machine learning algorithm. The medical interview support unit presents observation points during the medical interview based on the probability calculated by the incidence probability calculation unit. For example, the medical interview support unit presents points that the doctor should particularly observe during the examination (e.g., whether the patient makes eye contact, whether the patient points with interest). The presentation of observation points includes, but is not limited to, whether the patient makes eye contact, whether the patient points with interest. For example, the medical interview support unit presents points that the doctor should observe during the examination in a list format. Furthermore, the medical interview support unit can also visually present observation points in graphs or charts. Furthermore, the medical interview support unit can also present observation points audibly. The advice-providing unit provides advice on treatment plans, how to interact with the child at home, and family care based on the information presented by the medical interview support unit. The advice-providing unit provides specific advice on treatment plans, how to interact with the child at home, and family care, for example. The advice provided includes, but is not limited to, treatment steps and specific guidelines for actions at home. The advice-providing unit provides treatment plans in text format, for example. The advice-providing unit can also provide video guides on how to interact with the child at home. Furthermore, the advice-providing unit can provide advice on family care in audio format. In this way, the medical support system according to the embodiment can enable children with developmental disabilities and their families to receive appropriate medical care.

[0074] The reception unit accepts input of photos and videos of children from parents. This input from parents includes, but is not limited to, information such as resolution, format, and shooting conditions. For example, parents can upload photos and videos taken with their smartphones. The reception unit can also accept high-resolution photos and videos taken with digital cameras. Furthermore, parents can upload photos and videos they have taken in the past from cloud storage. When accepting this data, the reception unit has the functionality to automatically check the quality and format of the data and perform format conversion or resolution adjustment as needed. For example, if a photo taken with a smartphone is low resolution, the reception unit will automatically apply an algorithm to improve the resolution. Also, if video data in a different format is uploaded, the reception unit will convert it to a unified format so that the subsequent analysis unit can process it efficiently. Furthermore, the reception unit also has the functionality to add metadata to the data uploaded by parents. For example, it automatically extracts information such as the date and time of shooting, location of shooting, and photographer's information and stores it in the database. This allows for the provision of more detailed and accurate information in subsequent analysis and diagnosis. The reception area features an intuitive and user-friendly interface for parents uploading data, making it easy for them to provide data. For example, it includes drag-and-drop functionality for uploading photos and videos, as well as integration with cloud storage. This allows parents to provide necessary data without hassle, improving the overall efficiency of the system.

[0075] The Developmental Analysis Unit analyzes data received by the Reception Unit. For example, the Developmental Analysis Unit analyzes information on a child's developmental progress recorded by parents using natural language processing and extracts important information. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the Developmental Analysis Unit analyzes diaries and notes recorded by parents and extracts important information about the child's development. The Developmental Analysis Unit can also analyze text data entered by parents and extract important keywords related to the child's development. Furthermore, the Developmental Analysis Unit can analyze audio data recorded by parents and extract important information about the child's development. The Developmental Analysis Unit utilizes AI technology when analyzing this data. For example, it uses the latest generative AI and large-scale language models (LLMs) for natural language processing to deeply understand the meaning of text data and extract important information. Specifically, it extracts specific episodes and behavioral patterns related to the child's development from diaries and notes recorded by parents and stores them in a database. Furthermore, for analyzing audio data, speech recognition technology is used to convert speech into text, and then natural language processing is applied. This allows for efficient analysis of information recorded orally by parents. In addition, the developmental analysis unit also supports the analysis of image and video data. For example, it analyzes images and videos of children's faces to detect changes in facial expressions and movements. This uses facial recognition technology and motion analysis algorithms to provide a detailed understanding of the child's developmental status. The developmental analysis unit integrates these analysis results to generate a comprehensive report on the child's development. This report is provided to doctors and parents and serves as important material for objectively evaluating the child's developmental status.

[0076] The feature detection unit detects characteristics specific to developmental disorders based on data analyzed by the developmental analysis unit. For example, the feature detection unit analyzes images and videos of a child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. The detection of facial expressions, eye movements, tantrums, and specific behaviors includes, but is not limited to, facial recognition technology and behavioral analysis algorithms. For example, the feature detection unit analyzes images of a child's face to detect expressions such as smiles and anger. It can also analyze videos of a child to detect eye movements and tantrums. Furthermore, the feature detection unit can analyze a child's behavioral patterns to detect specific behaviors. The feature detection unit makes full use of AI technology when performing these analyses. For example, it applies the latest algorithms using deep learning to facial recognition technology to analyze children's facial expressions with high accuracy. Specifically, it can detect not only basic expressions such as smiles, anger, and sadness, but also subtle changes in facial expressions. In addition, eye-tracking technology is used to understand in real time which direction the child is looking when analyzing eye movements. This allows for the assessment of a child's interest in specific objects or people. Furthermore, behavioral analysis algorithms are used to analyze the child's movement patterns in detail in order to detect tantrums and specific behaviors. For example, the child's movements are analyzed frame by frame from video data to detect specific behavioral patterns. This includes movements such as walking, hand movements, and body swaying. The feature detection unit integrates these analysis results to generate a detailed report for assessing the possibility of developmental disorders in the child. This report is provided to doctors and parents and serves as important material for objectively evaluating the child's developmental status.

[0077] The disease incidence probability calculation unit calculates the probability of disease incidence based on features detected by the feature detection unit. For example, the disease incidence probability calculation unit calculates disease incidence probabilities such as "autism 74%, ADHD 15%, comorbidity 11%". The calculation of disease incidence probability includes, but is not limited to, the statistical data used and the calculation algorithm. For example, the disease incidence probability calculation unit calculates disease incidence probability based on past medical data. The disease incidence probability calculation unit can also calculate disease incidence probability using statistical models. Furthermore, the disease incidence probability calculation unit can also calculate disease incidence probability using machine learning algorithms. The disease incidence probability calculation unit utilizes AI technology when analyzing this data. For example, it constructs statistical models based on past medical data and uses them to calculate disease incidence probability for new data. Specifically, it constructs regression models or classification models to predict disease incidence probability based on features extracted from past medical data. It also uses machine learning algorithms to learn data patterns and calculate disease incidence probability with higher accuracy. For example, a neural network model using deep learning is constructed, and child characteristic data is taken as input to output the probability of disease occurrence. This model is trained using past medical data and has high predictive accuracy even with new data. Furthermore, the disease probability calculation unit also has a function to visually display the calculated disease probability. For example, the disease probability of each disease can be visually displayed using graphs and charts, making it easy for doctors and parents to understand intuitively. As a result, the disease probability calculation unit can accurately assess the possibility of developmental disorders in children and support the determination of appropriate diagnoses and treatment plans.

[0078] The medical interview support unit presents observation points during the medical interview based on probabilities calculated by the disease probability calculation unit. For example, the medical interview support unit presents points that doctors should particularly observe during the examination (such as whether the patient makes eye contact or points with interest). These observation points include, but are not limited to, whether the patient makes eye contact or points with interest. The medical interview support unit can also present points that doctors should observe during the examination in a list format. Furthermore, the medical interview support unit can visually present observation points using graphs and charts. In addition, the medical interview support unit can present observation points verbally. The medical interview support unit utilizes AI technology when presenting these observation points. For example, it prioritizes and presents the most important observation points based on probabilities calculated by the disease probability calculation unit. Specifically, the AI ​​analyzes past medical data and extracts important observation points related to specific diseases. This allows doctors to perform observations efficiently and effectively during examinations. The medical interview support unit also provides an interactive user interface when visually presenting observation points. For example, the system includes a function that allows doctors to tap on observation points using a tablet or smartphone to display detailed information. This enables doctors to quickly access necessary information during consultations. Furthermore, the consultation support unit also includes a function that presents observation points aloud. For instance, it provides a function where a voice assistant reads out observation points so that doctors can check them without using their hands during consultations. This allows doctors to efficiently acquire necessary information while concentrating on the consultation. Through these functions, the consultation support unit provides assistance to doctors in making appropriate diagnoses without overlooking important observation points during consultations.

[0079] The Advice Department provides advice on treatment plans, how to interact with children at home, and family care based on information presented by the Medical Interview Support Department. The Advice Department provides specific advice on treatment plans, how to interact with children at home, and family care. This advice includes, but is not limited to, treatment steps and specific behavioral guidelines at home. For example, the Advice Department provides treatment plans in text format. It can also provide video guides on how to interact with children at home. Furthermore, it can provide advice on family care in audio format. The Advice Department utilizes AI technology when providing this advice. For example, it builds algorithms to propose optimal treatment plans based on past medical data and treatment results. Specifically, the AI ​​learns from past successes and failures to propose the most suitable treatment plan for each individual child. Advice on how to interact with children at home includes specific behavioral guidelines and communication methods. For example, it provides detailed guidelines on how to respond when a child exhibits certain behaviors and how to communicate in daily life. This includes video and audio guides to help parents understand the information intuitively. Furthermore, the advice department also provides advice on caring for the whole family. For example, they offer advice on how parents can cope with stress and how the whole family can relax. This includes counseling by mental health professionals and introductions to relaxation techniques. The advice department also customizes this advice to suit the individual family's situation and needs. For instance, they select and provide the most appropriate advice based on the family's structure and living environment. In this way, the advice department can support the health and well-being of the entire family, not just the children.

[0080] The developmental analysis unit can analyze information on a child's developmental progress recorded by parents using natural language processing and extract important information. For example, the developmental analysis unit can analyze diaries and memos recorded by parents and extract important information about the child's development. For example, the developmental analysis unit can analyze text data entered by parents and extract important keywords related to the child's development. For example, the developmental analysis unit can analyze audio data recorded by parents and extract important information about the child's development. By analyzing the developmental progress information recorded by parents and extracting important information, more accurate medical support information can be provided. Natural language processing includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the developmental analysis unit may be performed using AI, for example, or without AI. For example, the developmental analysis unit can input text data recorded by parents into a generating AI and have the generating AI perform the extraction of important information.

[0081] The feature detection unit can analyze images and videos of a child's face to detect facial expressions, eye movements, tantrums, and specific behaviors. For example, the feature detection unit can analyze a child's face image to detect smiles and angry expressions. The feature detection unit can also analyze a child's video to detect eye movements and tantrums. The feature detection unit can also analyze a child's behavior patterns to detect specific behaviors. This allows for the accurate detection of characteristics specific to developmental disorders by analyzing images and videos of a child's face. The detection of facial expressions, eye movements, tantrums, and specific behaviors includes, but is not limited to, facial recognition technology and behavioral analysis algorithms. Some or all of the above-described processes in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input a child's face image into a generating AI and have the generating AI perform facial expression detection.

[0082] The disease incidence probability calculation unit can calculate disease incidence probabilities such as "autism 74%, ADHD 15%, comorbidity 11%." The disease incidence probability calculation unit calculates disease incidence probabilities based on past medical data, for example. The disease incidence probability calculation unit can also calculate disease incidence probabilities using statistical models, for example. The disease incidence probability calculation unit can also calculate disease incidence probabilities using machine learning algorithms, for example. This allows for the provision of specific probability information that doctors can refer to during medical consultations by calculating disease incidence probabilities. The calculation of disease incidence probabilities includes, but is not limited to, the statistical data used and the calculation algorithms. Some or all of the above-described processes in the disease incidence probability calculation unit may be performed using AI, for example, or without AI. For example, the disease incidence probability calculation unit can input past medical data into a generating AI and have the generating AI perform the calculation of disease incidence probabilities.

[0083] The medical history support unit can present points that physicians should particularly observe during consultations. For example, the medical history support unit can present points that physicians should observe during consultations in a list format. The medical history support unit can also present observation points visually, for example, in graphs or charts. The medical history support unit can also present observation points verbally, for example. This improves the accuracy of consultations by presenting points that physicians should particularly observe during consultations. The presentation of observation points includes, but is not limited to, whether the patient makes eye contact or points with interest. Some or all of the above processing in the medical history support unit may be performed using, for example, AI, or not using AI. For example, the medical history support unit can input observation points into a generating AI and have the generating AI perform the presentation of observation points.

[0084] The advice-providing unit can provide specific advice on treatment plans, how to interact with children at home, and family care. For example, the advice-providing unit can provide treatment plans in text format. For example, the advice-providing unit can provide video guides on how to interact with children at home. For example, the advice-providing unit can provide advice on family care in audio format. By providing specific advice, the way children are interacted with at home and family care are improved. The advice provided may include, but is not limited to, treatment steps and specific guidelines for actions at home. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input treatment plans into a generating AI and have the generating AI provide specific advice.

[0085] The reception desk can estimate the parent's emotions and adjust the timing of photo and video uploads based on the estimated emotions. For example, if the parent is stressed, the reception desk can use AI to estimate a time when the parent can relax and prompt them to upload at that time. For example, if the parent is busy, the reception desk can use AI to analyze the parent's schedule and suggest the optimal upload time. For example, if the parent is emotionally unstable, the reception desk can use AI to provide advice on how to stabilize the parent's emotions and then prompt them to upload. This reduces the burden on the parent by adjusting the upload timing according to their emotions. The estimation of the parent's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input parent emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reception unit can analyze the parent's past upload history and select the optimal data acquisition method. For example, the reception unit can analyze the time periods when the parent frequently uploaded in the past and prompt the parent to upload during those times. The reception unit can also analyze the devices and apps the parent has used in the past and suggest the optimal data acquisition method. For example, the reception unit can select a data acquisition method for a specific event or situation from the parent's past upload history. This allows the reception unit to suggest the optimal data acquisition method by analyzing past upload history. The optimal data acquisition method includes, but is not limited to, data type, acquisition frequency, and acquisition method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the parent's past upload history into a generating AI and have the generating AI select the optimal data acquisition method.

[0087] The reception unit can filter photos and videos based on the parent's current living situation and areas of interest when acquiring them. For example, if the parent inputs their current living situation, the AI ​​can suggest appropriate methods for acquiring photos and videos based on that situation. The reception unit can also have the AI ​​prioritize the acquisition of relevant data based on the parent's areas of interest (e.g., specific play or learning activities). For example, if the parent is in a specific living situation (e.g., traveling or visiting a hospital), the AI ​​can suggest appropriate methods for acquiring data based on that situation. This allows for the acquisition of more relevant data by filtering the data based on the parent's living situation and areas of interest. The parent's current living situation and areas of interest include, but are not limited to, survey results and social media activity. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input data on the parent's living situation and areas of interest into a generating AI and have the generating AI perform data filtering.

[0088] The reception unit can estimate the parent's emotions and determine the priority of data to acquire based on the estimated parent's emotions. For example, if the parent is stressed, the reception unit will prioritize acquiring data that helps reduce stress. For example, if the parent is relaxed, the reception unit can also prioritize acquiring data that helps maintain that relaxed state. For example, if the parent is in a hurry, the reception unit can also prioritize acquiring data that can be acquired quickly by the AI. This reduces the burden on the parent by prioritizing data based on the parent's emotions. Estimation of the parent's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input parent emotion data into a generative AI and have the generative AI determine the priority of the data.

[0089] The reception unit can prioritize the acquisition of highly relevant data by considering the parent's geographical location information when acquiring data. For example, if the parent is in a specific region, the reception unit will prioritize the acquisition of data related to that region. For example, if the parent is traveling, the reception unit can also acquire relevant data based on geographical information of the travel destination. For example, if the parent is at home, the reception unit can also acquire relevant data based on information about the area around the home. This allows for the priority acquisition of highly relevant data by considering the parent's geographical location information. The parent's geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the parent's geographical location information into a generating AI and have the generating AI acquire highly relevant data.

[0090] The reception unit can analyze the parent's social media activity and obtain relevant data when acquiring data. For example, the reception unit can obtain relevant data based on information shared by the parent on social media. The reception unit can also obtain relevant data based on information about accounts followed by the parent on social media. The reception unit can also obtain relevant data based on information about groups and events the parent participates in on social media. This allows for the efficient acquisition of relevant data by analyzing the parent's social media activity. The parent's social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the processing described above in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the parent's social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0091] The developmental analysis unit can estimate the parent's emotions and adjust the presentation of the developmental analysis based on the estimated parent's emotions. For example, if the parent is stressed, the developmental analysis unit can provide a concise and easy-to-understand presentation. For example, if the parent is relaxed, the developmental analysis unit can also provide detailed analysis results. For example, if the parent is anxious, the developmental analysis unit can also provide a reassuring presentation. By adjusting the presentation of the developmental analysis based on the parent's emotions, the system can provide analysis results that are easy for the parent to understand. Presentation methods for developmental analysis include, but are not limited to, graphs, text, and audio explanations. Some or all of the above-described processes in the developmental analysis unit may be performed using, for example, AI, or not using AI. For example, the developmental analysis unit can input parent's emotion data into a generating AI and have the generating AI adjust the presentation method.

[0092] The growth analysis unit can adjust the level of detail of the analysis based on the importance of the data during growth analysis. For example, the growth analysis unit can perform a detailed analysis on data with high importance. For example, the growth analysis unit can also perform a simplified analysis on data with low importance. The growth analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Data importance includes, but is not limited to, data freshness, relevance, and reliability. Some or all of the above processing in the growth analysis unit may be performed using, for example, AI, or not using AI. For example, the growth analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0093] The growth analysis unit can apply different analysis algorithms depending on the data category during growth analysis. For example, the growth analysis unit can apply an image analysis algorithm to video data. For example, the growth analysis unit can also apply a natural language processing algorithm to text data. For example, the growth analysis unit can also apply a speech analysis algorithm to audio data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, image data, text data, and audio data. Some or all of the above processing in the growth analysis unit may be performed using AI, for example, or without AI. For example, the growth analysis unit can input the data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0094] The developmental analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated parent's emotions. For example, if the parent is in a hurry, the developmental analysis unit can provide analysis results in a short time. For example, if the parent is relaxed, the developmental analysis unit can also provide detailed analysis results. For example, if the parent is feeling anxious, the developmental analysis unit can also provide analysis results of an appropriate length to provide reassurance. In this way, by adjusting the length of the analysis based on the parent's emotions, appropriate analysis results can be provided to the parent. The length of the analysis includes, but is not limited to, analysis time and level of detail. Some or all of the above processing in the developmental analysis unit may be performed using, for example, AI, or not using AI. For example, the developmental analysis unit can input parent's emotion data into a generating AI and have the generating AI perform the adjustment of the analysis length.

[0095] The growth analysis unit can determine the priority of analysis based on the data submission date during growth analysis. For example, the growth analysis unit may prioritize the analysis of the most recent data. For example, the growth analysis unit may postpone the analysis of older data. For example, the growth analysis unit may also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. The data submission date includes, but is not limited to, the submission date and time, and the submission frequency. Some or all of the above processing in the growth analysis unit may be performed using AI, for example, or without AI. For example, the growth analysis unit can input the data submission date into a generating AI and have the generating AI determine the analysis priority.

[0096] The growth analysis unit can adjust the order of analysis based on the relevance of the data during growth analysis. For example, the growth analysis unit can prioritize the analysis of highly relevant data. For example, it can postpone the analysis of less relevant data. The growth analysis unit can also adjust the analysis schedule based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, but is not limited to, interrelationships and commonalities between data. Some or all of the above processing in the growth analysis unit may be performed using AI, for example, or without AI. For example, the growth analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0097] The feature detection unit can estimate the parent's emotions and adjust the feature detection criteria based on the estimated parent's emotions. For example, if the parent is stressed, the feature detection unit can provide concise and easy-to-understand criteria. For example, if the parent is relaxed, the feature detection unit can also provide detailed criteria. For example, if the parent is anxious, the feature detection unit can also provide reassuring criteria. By adjusting the feature detection criteria based on the parent's emotions, the feature detection results can be provided that are easy for the parent to understand. The feature detection criteria include, but are not limited to, detection thresholds and detection algorithms. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or not using AI. For example, the feature detection unit can input parent emotion data into a generating AI and have the generating AI perform the adjustment of the feature detection criteria.

[0098] The feature detection unit can improve the accuracy of feature detection by considering the interrelationships between data during feature detection. For example, the feature detection unit can detect features by considering the interrelationships between video data and text data. The feature detection unit can also detect features by considering the interrelationships between audio data and image data. The feature detection unit can also detect features by considering the interrelationships between multiple data sources. This improves the accuracy of feature detection by considering the interrelationships between data. Examples of data interrelationships include, but are not limited to, correlation analysis and co-occurrence networks. Some or all of the above-described processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input the interrelationships between data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0099] The feature detection unit can perform feature detection while considering the attribute information of the data submitter. For example, the feature detection unit can detect features by considering the submitter's age and gender. The feature detection unit can also detect features by considering the submitter's living environment and background. The feature detection unit can also detect features by considering the submitter's health status and past medical history. This makes it possible to perform more accurate feature detection by considering the attribute information of the data submitter. The attribute information of the data submitter includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without using AI. For example, the feature detection unit can input the submitter's attribute information into a generating AI and have the generating AI perform feature detection.

[0100] The feature detection unit can estimate the parent's emotions and adjust the order in which the feature detection results are displayed based on the estimated parent's emotions. For example, if the parent is stressed, the feature detection unit will prioritize displaying important features. For example, if the parent is relaxed, the feature detection unit can also display detailed features in a specific order. For example, if the parent is anxious, the feature detection unit can also prioritize displaying features that provide a sense of security. By adjusting the order in which the feature detection results are displayed based on the parent's emotions, it becomes possible to display the results in a way that is easy for the parent to understand. The order in which the feature detection results are displayed includes, but is not limited to, examples such as by importance or by detection date and time. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input the parent's emotion data into a generating AI and have the generating AI perform the adjustment of the display order.

[0101] The feature detection unit can perform feature detection while considering the geographical distribution of the data. For example, the feature detection unit can prioritize the detection of features associated with a specific region. The feature detection unit can also improve the accuracy of feature detection based on geographical distribution. For example, the feature detection unit can adjust the order of feature detection while considering geographical distribution. This improves the accuracy of feature detection by considering the geographical distribution of the data. The geographical distribution of the data includes, but is not limited to, data distribution by region and geographical bias. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input geographical distribution data into a generating AI and have the generating AI perform the task of improving the detection accuracy.

[0102] The feature detection unit can improve the accuracy of feature detection by referring to relevant literature for the data during feature detection. For example, the feature detection unit sets feature detection criteria based on relevant literature. The feature detection unit can also improve the accuracy of feature detection by referring to relevant literature. The feature detection unit can also adjust the feature detection algorithm based on relevant literature. This improves the accuracy of feature detection by referring to relevant literature for the data. Relevant literature for the data includes, but is not limited to, literature search methods and citation criteria. Some or all of the above processing in the feature detection unit may be performed using, for example, AI, or without AI. For example, the feature detection unit can input relevant literature data into a generating AI and have the generating AI perform the detection accuracy improvement.

[0103] The disease probability calculation unit can estimate the parent's emotions and adjust the disease probability calculation method based on the estimated parent's emotions. For example, if the parent is stressed, the disease probability calculation unit can provide a simple and easy-to-understand calculation method. For example, if the parent is relaxed, the disease probability calculation unit can also provide a detailed calculation method. For example, if the parent is anxious, the disease probability calculation unit can also provide a reassuring calculation method. By adjusting the disease probability calculation method based on the parent's emotions, the calculation results can be provided that are easy for the parent to understand. The disease probability calculation method includes, but is not limited to, the algorithm used and the weighting of the data. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input parent emotion data into a generating AI and have the generating AI perform the adjustment of the calculation method.

[0104] The disease probability calculation unit can predict the current probability by referring to past disease data when calculating the disease probability. For example, the disease probability calculation unit predicts the current disease probability based on past disease data. The disease probability calculation unit can also, for example, analyze past disease data and calculate the current disease probability. The disease probability calculation unit can also, for example, apply an algorithm to predict the current disease probability by referring to past disease data. This allows for accurate prediction of the current disease probability by referring to past disease data. Past disease data includes, but is not limited to, the type of database and the reliability of the data. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input past disease data into a generating AI and have the generating AI perform a prediction of the current disease probability.

[0105] The disease probability calculation unit can apply different calculation methods to each data category when calculating disease probability. For example, when calculating the disease probability of autism, the disease probability calculation unit applies a specific algorithm. For example, when calculating the disease probability of ADHD, the disease probability calculation unit can also apply a different algorithm. For example, when calculating the disease probability of comorbidities, the disease probability calculation unit can also apply a combination of multiple algorithms. This improves the accuracy of disease probability calculation by applying different calculation methods to each data category. Different calculation methods for each data category include, but are not limited to, image data, text data, and audio data. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input data categories into a generating AI and have the generating AI perform the application of calculation methods.

[0106] The disease probability calculation unit can estimate the parent's emotions and adjust the importance of disease probabilities based on the estimated parent's emotions. For example, if the parent is stressed, the disease probability calculation unit will prioritize displaying important disease probabilities. For example, if the parent is relaxed, the disease probability calculation unit can also display detailed disease probabilities. For example, if the parent is anxious, the disease probability calculation unit can prioritize displaying disease probabilities that provide a sense of security. By adjusting the importance of disease probabilities based on the parent's emotions, it becomes possible to display information that is easy for the parent to understand. The importance of disease probabilities includes, but is not limited to, high or low probability and degree of impact. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input parent emotion data into a generating AI and have the generating AI perform the importance adjustment.

[0107] The disease probability calculation unit can analyze changes in probability based on the data submission date when calculating disease probability. For example, the disease probability calculation unit can analyze changes in disease probability based on the latest data. The disease probability calculation unit can also analyze changes in disease probability based on older data. For example, the disease probability calculation unit can apply an algorithm to analyze changes in disease probability based on the submission date. This allows for accurate understanding of changes in disease probability by analyzing changes in probability based on the data submission date. Changes in probability include, but are not limited to, temporal changes and seasonal variations. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or without AI. For example, the disease probability calculation unit can input the data submission date to a generating AI and have the generating AI perform the analysis of changes in probability.

[0108] The disease probability calculation unit can analyze probabilities by referring to relevant market data when calculating disease probability. For example, the disease probability calculation unit analyzes disease probability based on relevant market data. The disease probability calculation unit can also improve the accuracy of disease probability calculation by referring to relevant market data. For example, the disease probability calculation unit can also analyze changes in disease probability based on relevant market data. This improves the accuracy of disease probability calculation by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and economic indicators. Some or all of the above processing in the disease probability calculation unit may be performed using, for example, AI, or not using AI. For example, the disease probability calculation unit can input relevant market data into a generating AI and have the generating AI perform the probability analysis.

[0109] The medical interview support unit can estimate the parent's emotions and adjust the display method of the medical interview support based on the estimated parent's emotions. For example, if the parent is feeling stressed, the medical interview support unit can provide a concise and easy-to-understand display method. For example, if the parent is relaxed, the medical interview support unit can also provide a detailed display method. For example, if the parent is feeling anxious, the medical interview support unit can also provide a reassuring display method. By adjusting the display method of the medical interview support based on the parent's emotions, it becomes possible to provide a display that is easy for the parent to understand. The display methods of the medical interview support include, but are not limited to, graph displays, text displays, and audio explanations. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input the parent's emotion data into a generating AI and have the generating AI perform the adjustment of the display method.

[0110] The medical interview support unit can optimize the current medical interview by referring to past medical interview data during medical interview support. For example, the medical interview support unit optimizes the current medical interview based on past medical interview data. The medical interview support unit can also optimize the current medical interview by analyzing past medical interview data. The medical interview support unit can also apply an algorithm to optimize the current medical interview by referring to past medical interview data. This allows the current medical interview to be optimized by referring to past medical interview data. Past medical interview data includes, but is not limited to, the type of database and the reliability of the data. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input past medical interview data into a generating AI and have the generating AI perform the optimization of the current medical interview.

[0111] The medical interview support unit can apply different medical interview support methods to each data category during medical interviews. For example, the medical interview support unit can apply a specific method to medical interview support for autism. For example, the medical interview support unit can apply a different method to medical interview support for ADHD. For example, the medical interview support unit can combine and apply multiple methods to medical interview support for comorbidities. This improves the accuracy of medical interview support by applying different medical interview support methods to each data category. Different medical interview support methods for each data category include, but are not limited to, image data, text data, and audio data. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input data categories into a generating AI and have the generating AI perform the application of medical interview support methods.

[0112] The consultation support unit can estimate the parent's emotions and adjust the importance of consultation support based on the estimated parent's emotions. For example, if the parent is feeling stressed, the consultation support unit will prioritize displaying important consultation support. For example, if the parent is relaxed, the consultation support unit may also display detailed consultation support. For example, if the parent is feeling anxious, the consultation support unit may also prioritize displaying consultation support that provides a sense of security. By adjusting the importance of consultation support based on the parent's emotions, it becomes possible to display information in a way that is easy for the parent to understand. The importance of consultation support includes, but is not limited to, the urgency and impact of the support. Some or all of the above processing in the consultation support unit may be performed using, for example, AI, or not using AI. For example, the consultation support unit can input the parent's emotion data into a generating AI and have the generating AI perform the importance adjustment.

[0113] The medical interview support unit can analyze changes in medical interviews based on the data submission date during medical interview support. For example, the medical interview support unit can analyze changes in medical interviews based on the latest data. For example, the medical interview support unit can also analyze changes in medical interviews based on older data. For example, the medical interview support unit can apply an algorithm to analyze changes in medical interviews based on the submission date. This allows for an accurate understanding of changes in medical interviews by analyzing them based on the data submission date. Changes in medical interviews include, but are not limited to, temporal changes and seasonal variations. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input the data submission date into a generating AI and have the generating AI perform the analysis of changes in medical interviews.

[0114] The medical interview support unit can analyze medical interviews by referring to relevant market data during the interview process. For example, the medical interview support unit analyzes medical interviews based on relevant market data. The medical interview support unit can also improve the accuracy of medical interview analysis by referring to relevant market data. For example, the medical interview support unit can also analyze changes in medical interviews based on relevant market data. This improves the accuracy of medical interview analysis by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and economic indicators. Some or all of the above processing in the medical interview support unit may be performed using, for example, AI, or not using AI. For example, the medical interview support unit can input relevant market data into a generating AI and have the generating AI perform the medical interview analysis.

[0115] The advice-providing unit can estimate the parent's emotions and adjust the method of providing advice based on the estimated emotions. For example, if the parent is stressed, the advice-providing unit will provide concise and easy-to-understand advice. For example, if the parent is relaxed, the advice-providing unit may also provide detailed advice. For example, if the parent is anxious, the advice-providing unit may also provide reassuring advice. By adjusting the method of providing advice based on the parent's emotions, the advice-providing unit can provide advice that is easy for the parent to understand. Methods of providing advice include, but are not limited to, text displays, audio explanations, and video guides. Some or all of the above processing in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input parent emotion data into a generating AI and have the generating AI perform the adjustment of the method of providing advice.

[0116] The advice-providing unit can optimize current advice by referring to past advice data when providing advice. For example, the advice-providing unit optimizes current advice based on past advice data. The advice-providing unit can also optimize current advice by analyzing past advice data. The advice-providing unit can also apply an algorithm to optimize current advice by referring to past advice data. This allows for the optimization of current advice by referring to past advice data. Past advice data includes, but is not limited to, database type and data reliability. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input past advice data into a generating AI and have the generating AI perform the optimization of current advice.

[0117] The advice-providing unit can apply different advice methods to each data category when providing advice. For example, the advice-providing unit can apply a specific method to advice for autism. For example, the advice-providing unit can apply a different method to advice for ADHD. For example, the advice-providing unit can combine and apply multiple methods to advice for comorbidities. This improves the accuracy of the advice by applying different advice methods to each data category. Different advice methods for each data category include, but are not limited to, image data, text data, and audio data. Some or all of the processing described above in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input data categories into a generating AI and have the generating AI perform the application of advice methods.

[0118] The advice-providing unit can estimate the parent's emotions and adjust the importance of advice based on the estimated emotions. For example, if the parent is stressed, the advice-providing unit may prioritize important advice. For example, if the parent is relaxed, the advice-providing unit may also provide detailed advice. For example, if the parent is anxious, the advice-providing unit may also prioritize reassuring advice. By adjusting the importance of advice based on the parent's emotions, the advice can be provided in a way that is easy for the parent to understand. The importance of advice includes, but is not limited to, the urgency and impact of the advice. Some or all of the above processing in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input parent emotion data into a generating AI and have the generating AI perform the importance adjustment.

[0119] The advice-providing unit can analyze changes in advice based on the data submission date when providing advice. For example, the advice-providing unit can analyze changes in advice based on the latest data. For example, the advice-providing unit can also analyze changes in advice based on older data. For example, the advice-providing unit can apply an algorithm to analyze changes in advice based on the submission date. This allows for an accurate understanding of changes in advice by analyzing changes in advice based on the data submission date. Changes in advice include, but are not limited to, temporal changes and seasonal variations. Some or all of the above processing in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input the data submission date into a generating AI and have the generating AI perform the analysis of changes in advice.

[0120] The advice-providing unit can analyze advice by referring to relevant market data when providing advice. For example, the advice-providing unit analyzes advice based on relevant market data. The advice-providing unit can also improve the accuracy of the advice analysis by referring to relevant market data. For example, the advice-providing unit can also analyze changes in advice based on relevant market data. This improves the accuracy of the advice analysis by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and economic indicators. Some or all of the above processing in the advice-providing unit may be performed using, for example, AI, or not using AI. For example, the advice-providing unit can input relevant market data into a generating AI and have the generating AI perform the analysis of the advice.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The medical support system can further estimate the parent's emotions and adjust the way medical support information is provided based on those estimated emotions. For example, if the parent is stressed, the system will provide concise and easy-to-understand information. If the parent is relaxed, it can provide more detailed information. If the parent is anxious, it can prioritize providing reassuring information. This allows for information provision tailored to the parent's emotions, reducing their burden. Emotion estimation uses algorithms that analyze the parent's input data and behavioral patterns, for example. The system inputs the parent's emotional data into a generating AI, which then adjusts the information provision method.

[0123] The developmental analysis unit can estimate the parent's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the parent is stressed, the system will provide a concise and easy-to-understand analysis result. If the parent is relaxed, it can also provide a detailed analysis result. If the parent is anxious, it can prioritize providing analysis results that provide a sense of security. This makes it possible to display analysis results that are tailored to the parent's emotions, helping the parent understand the situation. For emotion estimation, algorithms that analyze the parent's input data and behavioral patterns are used, for example. The developmental analysis unit can input the parent's emotional data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0124] The feature detection unit can estimate the parent's emotions and adjust the order in which the feature detection results are displayed based on the estimated emotions. For example, if the parent is stressed, important features will be displayed preferentially. If the parent is relaxed, detailed features can be displayed in a sequential manner. If the parent is anxious, features that provide a sense of security can be displayed preferentially. By adjusting the order in which the feature detection results are displayed based on the parent's emotions, it becomes possible to display the information in a way that is easy for the parent to understand. The order in which the feature detection results are displayed includes, but is not limited to, examples such as by importance or by detection date and time. The feature detection unit can input the parent's emotion data into a generating AI and have the generating AI perform the adjustment of the display order.

[0125] The disease probability calculation unit can estimate the parent's emotions and adjust the disease probability calculation method based on the estimated emotions. For example, if the parent is stressed, it can provide a simple and easy-to-understand calculation method. If the parent is relaxed, it can also provide a detailed calculation method. If the parent is anxious, it can also provide a reassuring calculation method. By adjusting the disease probability calculation method based on the parent's emotions, it is possible to provide calculation results that are easy for the parent to understand. The disease probability calculation method includes, but is not limited to, the algorithm used and the weighting of the data. The disease probability calculation unit can input the parent's emotion data into a generating AI and have the generating AI perform the adjustment of the calculation method.

[0126] The consultation support unit can estimate the parent's emotions and adjust the display method of the consultation support based on the estimated emotions. For example, if the parent is stressed, it can provide a concise and easy-to-understand display method. If the parent is relaxed, it can also provide a detailed display method. If the parent is anxious, it can also provide a display method that provides reassurance. By adjusting the display method of the consultation support based on the parent's emotions, it becomes possible to provide a display that is easy for the parent to understand. The display methods of the consultation support include, but are not limited to, graph displays, text displays, and audio explanations. The consultation support unit can input the parent's emotional data into a generating AI and have the generating AI perform the adjustment of the display method.

[0127] The medical support system can further analyze the parent's past upload history and select the optimal data acquisition method. For example, it can analyze the times of day when the parent frequently uploaded in the past and prompt uploads during those times. It can also analyze the devices and apps the parent has used in the past and suggest the optimal data acquisition method. It can also select the data acquisition method for specific events or situations based on the parent's past upload history. In this way, by analyzing past upload history, the system can suggest the optimal data acquisition method. The optimal data acquisition method includes, but is not limited to, data type, acquisition frequency, and acquisition method. The reception desk can input the parent's past upload history into the generating AI and have the generating AI select the optimal data acquisition method.

[0128] The medical support system can further filter data based on the parents' current living situation and areas of interest. For example, when a parent inputs their current living situation, the AI ​​suggests appropriate ways to acquire photos and videos based on that situation. The AI ​​can also prioritize acquiring relevant data based on the parents' areas of interest (e.g., specific play or learning activities). If a parent is in a specific living situation (e.g., traveling or visiting a hospital), the AI ​​can also suggest appropriate ways to acquire data based on that situation. This allows for the acquisition of more relevant data by filtering data based on the parents' living situation and areas of interest. Examples of the parents' current living situation and areas of interest include, but are not limited to, survey results and social media activity. The reception desk can input data on the parents' living situation and areas of interest into the generating AI and have the generating AI perform data filtering.

[0129] The medical support system can further prioritize the acquisition of highly relevant data by considering the parent's geographical location. For example, if the parent is in a specific area, it will prioritize the acquisition of data related to that area. If the parent is traveling, it can also acquire relevant data based on the geographical information of the travel destination. If the parent is at home, it can also acquire relevant data based on information about the area around their home. In this way, by considering the parent's geographical location, it can prioritize the acquisition of highly relevant data. The parent's geographical location information includes, but is not limited to, GPS data and location information services. The reception desk can input the parent's geographical location information into the generating AI and have the generating AI acquire highly relevant data.

[0130] The medical support system can further analyze parents' social media activity and acquire relevant data. For example, it can acquire relevant data based on information parents have shared on social media. It can also acquire relevant data based on information about accounts parents follow on social media. It can also acquire relevant data based on information about groups and events parents participate in on social media. This allows for the efficient acquisition of relevant data by analyzing parents' social media activity. Parents' social media activity includes, but is not limited to, posts, the number of likes, and comments. The reception desk can input the parents' social media activity data into a generating AI and have the generating AI acquire the relevant data.

[0131] The medical support system can further estimate the parent's emotions and prioritize the data to be retrieved based on those estimated emotions. For example, if the parent is stressed, the AI ​​will prioritize retrieving data that helps reduce stress. If the parent is relaxed, the AI ​​can also prioritize retrieving data that helps maintain that relaxed state. If the parent is in a hurry, the AI ​​can also prioritize retrieving data that can be retrieved quickly. This reduces the burden on the parent by prioritizing data based on their emotions. The estimation of the parent's emotions is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The reception desk can input the parent's emotion data into the generative AI and have the generative AI determine the priority of the data.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The reception desk accepts photos and videos of children from parents. Parental input includes resolution, format, and shooting conditions. For example, parents can upload photos and videos taken with their smartphones. High-resolution photos and videos taken with digital cameras, as well as photos and videos taken previously and stored in cloud storage, can also be accepted. Step 2: The developmental analysis unit analyzes the data received by the reception unit. For example, it analyzes the child's developmental progress information recorded by the parents using natural language processing and extracts important information. Natural language processing includes morphological analysis, grammatical analysis, and semantic analysis. It can also analyze diaries, notes, and audio data recorded by the parents to extract important information about the child's development. Step 3: The feature detection unit detects features specific to developmental disorders based on the data analyzed by the developmental analysis unit. For example, it analyzes images and videos of children's faces to detect facial expressions, eye movements, tantrums, and specific behaviors. This uses facial recognition technology and behavioral analysis algorithms. Step 4: The disease probability calculation unit calculates the disease probability based on the features detected by the feature detection unit. For example, it calculates disease probabilities such as "autism 74%, ADHD 15%, comorbidity 11%". This uses statistical data, calculation algorithms, and machine learning algorithms. Step 5: The medical interview support unit presents observation points during the medical interview based on the probabilities calculated by the disease probability calculation unit. For example, it presents points that doctors should particularly observe during the examination (such as whether the patient makes eye contact or points with interest) in the form of a list, graph, chart, or audio. Step 6: The advice department provides advice on treatment plans, how to interact with the child at home, and family care based on the information presented by the medical interview support department. For example, they may provide treatment plans in text format, video guides on how to interact with the child at home, and audio advice on family care.

[0134] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0137] Each of the multiple elements described above, including the reception unit, growth analysis unit, feature detection unit, disease probability calculation unit, medical interview support unit, and advice provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which allows parents to upload photos and videos taken with their smartphones. The growth analysis unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, which analyzes the child's growth progress information recorded by the parents using natural language processing and extracts important information. The feature detection unit analyzes the child's facial images and videos using, for example, the camera 42 of the smart device 14, and detects facial expressions, eye movements, tantrums, and specific behaviors. The disease probability calculation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, which calculates the probability of disease onset based on the detected features. The medical interview support unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, which presents observation points during the medical interview based on the calculated probability. The advice-providing unit is implemented, for example, by the control unit 46A of the smart device 14, and provides specific advice on treatment plans, how to interact with children at home, and family care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, developmental analysis unit, feature detection unit, disease probability calculation unit, medical interview support unit, and advice provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which allows parents to upload photos and videos taken with the smart glasses 214. The developmental analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the child's developmental progress information recorded by the parents using natural language processing and extracts important information. The feature detection unit analyzes the child's facial images and videos using the camera 42 of the smart glasses 214, for example, and detects facial expressions, eye movements, tantrums, and specific behaviors. The disease probability calculation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which calculates the probability of disease onset based on the detected features. The medical interview support unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which presents observation points during the medical interview based on the calculated probability. The advice-providing unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides specific advice on treatment plans, how to interact with children at home, and family care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, developmental analysis unit, feature detection unit, disease probability calculation unit, medical interview support unit, and advice provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which allows parents to upload photos and videos taken with the headset terminal 314. The developmental analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the child's developmental progress information recorded by the parents using natural language processing and extracts important information. The feature detection unit analyzes the child's facial images and videos using, for example, the camera 42 of the headset terminal 314, and detects facial expressions, eye movements, tantrums, and specific behaviors. The disease probability calculation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which calculates the probability of disease incidence based on the detected features. The medical interview support unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and presents observation points during the medical interview based on the calculated probability. The advice provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides specific advice on treatment plans, how to interact with children at home, and family care. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the reception unit, developmental analysis unit, feature detection unit, disease probability calculation unit, medical interview support unit, and advice provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, and allows parents to upload photos and videos taken with the robot 414. The developmental analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the child's developmental progress information recorded by the parents using natural language processing to extract important information. The feature detection unit analyzes the child's facial images and videos using, for example, the camera 42 of the robot 414, and detects facial expressions, eye movements, tantrums, and specific behaviors. The disease probability calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and calculates the probability of disease onset based on the detected features. The medical interview support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and presents observation points during the medical interview based on the calculated probability. The advice-providing unit is implemented, for example, by the control unit 46A of the robot 414, and provides specific advice on treatment plans, how to interact with children at home, and family care. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0187] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] 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.

[0197] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] (Note 1) A reception desk that accepts photos and videos of children from parents, A growth analysis unit analyzes the data received by the reception unit, A feature detection unit detects characteristics specific to developmental disorders based on data analyzed by the aforementioned growth analysis unit, A disease incidence probability calculation unit calculates the probability of disease incidence based on the features detected by the feature detection unit, A medical interview support unit presents observation points during the medical interview based on the probability calculated by the disease probability calculation unit, The system includes an advice provision unit that provides advice on treatment plans, how to interact with children at home, and family care based on the information provided by the aforementioned medical interview support unit. A system characterized by the following features. (Note 2) The aforementioned growth analysis unit is, We analyze the child's developmental progress information recorded by parents using natural language processing and extract important information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The feature detection unit, It analyzes children's facial images and videos to detect expressions, eye movements, tantrums, and specific behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned disease probability calculation unit, The probability of prevalence of disorders is calculated, such as "Autism 74%, ADHD 15%, Comorbidity 11%." The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned medical interview support department, This section presents key points that doctors should particularly observe during medical examinations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice-providing unit, We provide specific advice on treatment plans, how to interact with children at home, and family care. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the parent's emotions and adjusts the timing of photo and video uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the parent's past upload history and select the optimal data acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When acquiring photos and videos, filtering is performed based on the parents' current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the parents' emotions and prioritizes the data to be collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When acquiring data, the system prioritizes retrieving highly relevant data by considering the parent's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When acquiring data, we analyze the parents' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned growth analysis unit is, We estimate the parents' emotions and adjust the representation of the developmental analysis based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned growth analysis unit is, During growth analysis, adjust the level of detail of the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned growth analysis unit is, When performing growth analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned growth analysis unit is, The system estimates the parents' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned growth analysis unit is, When performing growth analysis, the priority of the analysis is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned growth analysis unit is, During developmental analysis, the order of analysis is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The feature detection unit, We estimate the parent's emotions and adjust the feature detection criteria based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The feature detection unit, When detecting features, consider the interrelationships between data to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The feature detection unit, During feature detection, the data submitter's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The feature detection unit, It estimates the parent's emotions and adjusts the order in which feature detection results are displayed based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The feature detection unit, When performing feature detection, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The feature detection unit, During feature detection, we improve detection accuracy by referring to relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned disease probability calculation unit, We estimate the parents' emotions and adjust the method for calculating the probability of illness based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned disease probability calculation unit, When calculating the probability of infection, past infection data is referenced to predict the current probability. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned disease probability calculation unit, When calculating the probability of illness, different calculation methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned disease probability calculation unit, We estimate the parents' emotions and adjust the importance of the probability of illness based on the estimated parents' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned disease probability calculation unit, When calculating the probability of illness, analyze how the probability changes based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned disease probability calculation unit, When calculating the probability of infection, the probability is analyzed by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned medical interview support department, The system estimates the parents' emotions and adjusts the display method of the consultation support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned medical interview support department, During the medical interview process, past interview data is referenced to optimize the current interview. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned medical interview support department, When providing medical interview support, different interview support methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned medical interview support department, We estimate the parents' emotions and adjust the importance of the consultation support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned medical interview support department, When providing support for patient interviews, analyze changes in the interview based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned medical interview support department, When providing support for patient interviews, analyze the interview data by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned advice-providing unit, It estimates the parents' emotions and adjusts the way advice is given based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned advice-providing unit, When providing advice, we optimize the current advice by referring to past advice data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned advice-providing unit, When providing advice, we apply different advice methods depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned advice-providing unit, It estimates the parents' emotions and adjusts the importance of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned advice-providing unit, When providing advice, we analyze how the advice changes based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned advice-providing unit, When providing advice, we analyze the advice by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts photos and videos of children from parents, A growth analysis unit analyzes the data received by the reception unit, A feature detection unit detects characteristics specific to developmental disorders based on data analyzed by the aforementioned growth analysis unit, A disease incidence probability calculation unit calculates the probability of disease incidence based on the features detected by the feature detection unit, A medical interview support unit that presents observation points during medical interviews based on the probability calculated by the disease probability calculation unit, The system includes an advice provision unit that provides advice on treatment plans, how to interact with children at home, and family care based on the information provided by the aforementioned medical interview support unit. A system characterized by the following features.

2. The aforementioned growth analysis unit is, We analyze child development progress information recorded by parents using natural language processing and extract important information. The system according to feature 1.

3. The feature detection unit, It analyzes children's facial images and videos to detect expressions, eye movements, tantrums, and specific behaviors. The system according to feature 1.

4. The aforementioned medical interview support department, This section presents key points that doctors should particularly observe during medical examinations. The system according to feature 1.

5. The aforementioned advice-providing unit, We provide specific advice on treatment plans, how to interact with children at home, and family care. The system according to feature 1.

6. The aforementioned reception unit is It estimates the parent's emotions and adjusts the timing of photo and video uploads based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the parent's past upload history and select the optimal data acquisition method. The system according to feature 1.

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