system

Abstract

The system according to the embodiment aims to uniformly quantify and record the words of a patient. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a quantification unit, and a recording unit. The reception unit receives the patient's words. The analysis unit analyzes the patient's words received by the reception unit. The quantification unit quantifies the patient's words analyzed by the analysis unit. The recording unit records the data quantified by the quantification unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to uniformly quantify and record the patient's words, making it difficult to respond to medical interviews efficiently.

[0005] The system according to the embodiment aims to uniformly quantify and record the words of a patient. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a quantification unit, and a recording unit. The reception unit receives the patient's words. The analysis unit analyzes the patient's words received by the reception unit. The quantification unit quantifies the patient's words analyzed by the analysis unit. The recording unit records the data quantified by the quantification unit. [Effects of the Invention]

[0007] The system according to the embodiment can uniformly quantify and record the patient's words. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A medical interview response system according to an embodiment of the present invention uses a generative AI to efficiently analyze, quantify, and record patient responses. When a patient verbally describes the location and severity of their pain, the generative AI accepts the patient's responses in a question-and-answer format, analyzes them, and converts them into a standardized expression. The generative AI then quantifies the location and severity of the pain based on the patient's responses and automatically records them in the electronic medical record. This allows doctors and nurses to quickly and accurately grasp the patient's pain status. This system reduces hospital wait times, improves patient awareness, and increases nurse productivity. For example, nurses no longer need to conduct detailed interviews with each patient; the generative AI automatically conducts the interview, allowing nurses to focus on other tasks. The generative AI also has an algorithm for quantifying the location and severity of pain based on the patient's responses. For example, if a patient responds "very painful," the generative AI records the response as "pain intensity: 8 / 10." This allows for a consistent assessment of the patient's pain status. The generative AI also has a database for quantifying the location and severity of pain based on the patient's responses. For example, based on past patient response data, the generation AI analyzes new patient responses and selects the optimal quantification method. This allows the generation AI to always perform accurate quantification based on the most recent data. This allows the medical interview response system to efficiently analyze, quantify, and record patient words.

[0029] The medical interview response system according to the embodiment includes a reception unit, an analysis unit, a quantification unit, and a recording unit. The reception unit receives the patient's words. Examples of the patient's words include, but are not limited to, oral statements, written descriptions, and emails. The reception unit receives, for example, when the patient expresses the location and severity of their pain in a question-and-answer format. The reception unit can also use a generation AI to receive the patient's words in a question-and-answer format. For example, if the generation AI asks the patient, "Where does it hurt?" and the patient replies, "My right shoulder hurts," the reception unit accepts the answer. The analysis unit analyzes the patient's words received by the reception unit. The analysis is performed, for example, using natural language processing technology, but is not limited to, for example. For example, the analysis unit analyzes the patient's words using morphological analysis and performs grammatical analysis. The analysis unit can also analyze the patient's words using a generation AI. For example, the generation AI analyzes the patient's words and understands their meaning. The quantification unit quantifies the patient's words analyzed by the analysis unit. Quantification may be performed, for example, by quantifying the intensity and location of pain, but is not limited to such an example. For example, if a patient answers "it hurts a lot," the quantification unit quantifies the answer as "pain intensity: 8 / 10." The quantification unit may also quantify the patient's words using a generation AI. For example, the generation AI analyzes the patient's words and performs quantification based on the results. The recording unit records the data quantified by the quantification unit. Recording may be performed, for example, by automatically recording the data in an electronic medical record, but is not limited to such an example. For example, the recording unit records the quantified data in the electronic medical record and stores it in a database. The recording unit may also record the quantified data using a generation AI. For example, the generation AI records the quantified data in the electronic medical record and stores it in a database. This allows the medical interview response system according to the embodiment to efficiently analyze, quantify, and record the patient's words.

[0030] The reception unit can accept the location or degree of the patient's pain in a question-and-answer format. For example, when a patient expresses the location or degree of pain in a question-and-answer format, the reception unit accepts the words. For example, if the generation AI asks the patient, "Where does it hurt?" and the patient replies, "My right shoulder hurts," the reception unit accepts the answer. The reception unit can also use the generation AI to accept the patient's words in a question-and-answer format. For example, if the generation AI asks the patient, "How intense is the pain?" and the patient replies, "It hurts a lot," the reception unit accepts the answer. This allows the location and degree of the patient's pain to be accepted efficiently. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can accept the patient's words in a question-and-answer format based on the results of the generation AI analyzing the patient's words.

[0031] The analysis unit can analyze the patient's responses using natural language processing technology. The analysis unit, for example, analyzes the patient's words using morphological analysis. For example, the analysis unit breaks down the patient's words into morphemes and analyzes the meaning of each morpheme. The analysis unit can also analyze the patient's words using grammatical analysis. For example, the analysis unit analyzes the grammatical structure of the patient's words to understand their meaning. The analysis unit can also analyze the patient's words using semantic analysis. For example, the analysis unit analyzes the meaning of the patient's words to understand their content. This allows the patient's responses to be analyzed accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the patient's words using a generation AI and perform analysis based on the results.

[0032] The quantification unit can quantify the intensity and location of pain based on the patient's response. The quantification unit performs quantification, for example, by a method that quantifies the intensity of pain. For example, if the patient responds, "It hurts a lot," the quantification unit quantifies the response as "pain intensity: 8 / 10." The quantification unit can also perform quantification by a method that quantifies the location of pain. For example, if the patient responds, "My right shoulder hurts," the quantification unit quantifies the response as "pain location: right shoulder." The quantification unit can also quantify the patient's words using a generation AI. For example, the generation AI analyzes the patient's words and performs quantification based on the results. This allows the patient's pain intensity and location to be accurately quantified. Some or all of the above-described processing in the quantification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the quantification unit can analyze the patient's words using a generation AI and perform quantification based on the results.

[0033] The recording unit can automatically record the quantified data in the electronic medical record. The recording unit, for example, records the quantified data in the electronic medical record. For example, the recording unit automatically records the quantified data in the electronic medical record and stores it in a database. The recording unit can also record the quantified data using a generating AI. For example, the generating AI records the quantified data in the electronic medical record and stores it in a database. This allows the quantified data to be recorded efficiently. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the recording unit can analyze the quantified data using the generating AI and record based on the results.

[0034] The quantification unit can analyze the responses of a new patient based on past patient data and select a quantification method. The quantification unit, for example, analyzes the responses of a new patient based on past patient data. For example, the quantification unit analyzes the response data of past patients and analyzes the responses of a new patient based on the results. The quantification unit can also select a quantification method based on past patient data. For example, the quantification unit selects an optimal quantification method based on the response data of past patients. This makes it possible to improve the accuracy of quantification by utilizing past data. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the quantification unit can analyze past patient data using a generation AI, and then analyze the responses of a new patient based on the results and select a quantification method.

[0035] The reception unit can analyze the patient's past medical interview history and select the optimal question and answer format. The reception unit, for example, analyzes the patient's past medical interview history and selects the optimal question and answer format based on the results. For example, if the patient has preferred detailed medical interviews in the past, the reception unit can have the generation AI ask detailed questions. Also, if the patient has preferred brief medical interviews in the past, the reception unit can have the generation AI ask brief questions. Also, the reception unit can analyze the patient's past answer patterns and have the generation AI select the optimal question order. This enables efficient medical interviews by selecting the optimal question and answer format using the past medical interview history. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit can have the generation AI analyze the patient's past medical interview history and select the optimal question and answer format based on the results.

[0036] The reception unit can customize the questions asked based on the patient's current health condition and lifestyle habits during the medical interview. For example, if the patient reports their current health condition, the reception unit asks relevant questions based on that information. For example, if the patient reports that they recently caught a cold, the generation AI asks, "What are your cold symptoms?" based on that information. The reception unit can also add specific questions based on the patient's lifestyle habits (smoking, drinking, etc.). For example, if the patient reports that they smoke every day, the generation AI asks, "How often do you smoke?" The reception unit can also ask questions about specific medical conditions based on the patient's medical history. For example, if the patient reports that they have had heart disease in the past, the generation AI asks, "How is your heart condition now?" This enables more appropriate medical interviews by asking questions tailored to the patient's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception department can use the generation AI to analyze the patient's current health condition and lifestyle habits, and customize the questions based on the results.

[0037] During the medical interview, the reception unit can prioritize relevant questions by taking into account the patient's geographic location information. For example, if the patient lives in a specific area, the reception unit asks questions about diseases specific to that area. For example, if the patient reports that they recently traveled to a specific area, the generation AI asks questions about health risks specific to that area. Furthermore, if the patient is currently traveling, the reception unit can also ask questions about health risks at the travel destination. For example, if the patient reports that they are currently traveling, the generation AI asks questions about health risks at the travel destination. Furthermore, if the patient lives in a specific environment (urban, rural, etc.), the reception unit can also ask questions about health risks associated with that environment. For example, if the patient reports that they live in a rural area, the generation AI asks questions about health risks specific to rural areas. This allows for more appropriate medical interviews by asking questions based on the patient's geographic location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception department can use the generation AI to analyze the patient's geographic location information and prioritize relevant questions based on the results.

[0038] The reception unit can analyze the patient's social media activity and ask relevant questions during the medical interview. For example, if the patient has posted about health on social media, the reception unit can ask questions based on the content of the post. For example, if the patient reports that they recently posted about health, the reception unit can ask the patient questions based on the content of the post, such as "Please tell us more about that post." Furthermore, if the patient shows interest in a specific health topic, the reception unit can ask questions related to that topic. For example, if the reception unit reports that they have recently become interested in a specific health topic, the generation AI can ask questions related to that topic. Furthermore, the reception unit can ask questions about lifestyle habits based on the patient's social media activity. For example, if the reception unit reports that they recently posted about lifestyle habits on social media, the generation AI can ask the patient questions based on the content of the post, such as "Please tell us more about that lifestyle habit." This allows for more appropriate medical interviews by asking questions based on the patient's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception department can use generative AI to analyze a patient's social media activity and ask relevant questions based on the results.

[0039] During analysis, the analysis unit can optimize the analysis algorithm by taking into account the context of the patient's response. For example, if the patient's response is ambiguous, the analysis unit allows the generation AI to take the context into account and perform the optimal interpretation. For example, if the patient answers "I have pain," the analysis unit interprets "where the pain is" by taking the context into account. Furthermore, if the patient's response is detailed, the generation AI can perform an analysis that reflects that detail. For example, if the patient answers "I have a sharp pain in my right shoulder," the analysis unit reflects that detail. Furthermore, if the patient's response is inconsistent, the generation AI can consider the context to ensure consistency. For example, if the patient answers "my right shoulder hurts" and then "my left shoulder hurts," the analysis unit considers the context to ensure consistency. This allows for more accurate analysis by taking into account the context of the patient's response. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the generation AI can analyze the context of the patient's response and optimize the analysis algorithm based on the results.

[0040] During analysis, the analysis unit can apply different analysis methods depending on the category of the patient's answer. For example, the generation AI applies a specific analysis method to answers regarding pain intensity. For example, if the patient answers "It hurts a lot," the analysis unit analyzes the pain intensity based on that answer. The generation AI can also apply a different analysis method to answers regarding the location of the pain. For example, if the patient answers "My right shoulder hurts," the analysis unit analyzes the location of the pain based on that answer. The analysis unit can also apply yet another analysis method to answers regarding the duration of the pain. For example, if the patient answers "The pain has been going on for a week," the analysis unit analyzes the duration of the pain based on that answer. This allows for more accurate analysis by applying an analysis method depending on the category of the patient's answer. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the generation AI can analyze the category of the patient's answer and apply a different analysis method based on the results.

[0041] During analysis, the analysis unit can determine the priority of analysis based on the timing of patient response submission. The analysis unit determines the priority of analysis based on, for example, the timing of patient response submission. For example, the analysis unit prioritizes analysis of the most recently submitted response by the patient. Furthermore, if the patient submits an urgent response, the analysis unit can cause the generation AI to analyze that response as the highest priority. Furthermore, the analysis unit can cause the generation AI to adjust the order of analysis based on the timing of patient response submission. This enables efficient analysis by determining the priority of analysis based on the timing of patient response submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to analyze the timing of patient response submission and determine the priority of analysis based on the results.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the patient's answers. For example, if a patient's answer is related to other answers, the analysis unit performs the analysis with the generation AI taking that relevance into account. For example, if a patient answers, "My right shoulder hurts," and then answers, "My left shoulder also hurts," the analysis unit performs the analysis taking that relevance into account. Furthermore, if the patient's answers are independent, the analysis unit can have the generation AI analyze the answers individually. For example, if a patient answers, "My right shoulder hurts," and then answers, "I have a headache," the analysis unit analyzes the answers individually. Furthermore, the analysis unit can optimize the order of analysis with the generation AI based on the relevance of the patient's answers. For example, if the patient's answers are related, the analysis unit adjusts the order of analysis based on that relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the patient's answers. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI analyze the relevance of the patient's answers and adjust the order of analysis based on the results.

[0043] The quantification unit can improve the accuracy of quantification by taking into account the interrelationships between patient responses during quantification. For example, if the patient's responses are consistent, the quantification unit allows the generation AI to reflect that consistency in quantification. For example, if the patient answers "My right shoulder hurts" and then "My right shoulder pain continues," the quantification unit performs quantification that reflects that consistency. Furthermore, if the patient's responses are inconsistent, the quantification unit can also perform quantification by resolving the inconsistency. For example, if the patient answers "My right shoulder hurts" and then "My left shoulder hurts," the quantification unit can perform quantification by resolving the inconsistency. Furthermore, the quantification unit can also perform optimal quantification by taking into account the interrelationships between patient responses. For example, if the patient's responses are related, the quantification unit performs quantification based on the relevance. This allows for more accurate quantification by taking into account the interrelationships between patient responses. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can have the generation AI analyze the interrelationships between patients' answers and perform quantification based on the results.

[0044] The quantification unit can perform quantification taking into account the patient's attribute information. For example, the quantification unit allows the generation AI to perform appropriate quantification based on the patient's age. For example, if the patient answers "50 years old," the quantification unit performs quantification based on the patient's age. The quantification unit can also perform appropriate quantification based on the patient's gender. For example, if the patient answers "female," the quantification unit performs quantification based on the patient's gender. The quantification unit can also perform appropriate quantification based on the patient's medical history. For example, if the patient answers "I have suffered from heart disease in the past," the quantification unit performs quantification based on the medical history. This allows for more accurate quantification by taking the patient's attribute information into consideration. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can analyze the patient's attribute information using the generation AI and perform quantification based on the results.

[0045] The quantification unit can perform quantification taking into account the geographical distribution of the patient. For example, if the patient lives in a specific area, the quantification unit performs quantification taking into account health risks specific to that area. For example, if the patient answers "I live in a specific area," the quantification unit performs quantification based on health risks specific to that area. Furthermore, if the patient is traveling, the quantification unit can also perform quantification taking into account health risks at the travel destination. For example, if the patient answers "I am currently traveling," the quantification unit performs quantification based on health risks at the travel destination. Furthermore, the quantification unit can perform optimal quantification using the generation AI based on the patient's geographical distribution. For example, if the patient answers "I live in a city," the quantification unit performs quantification based on health risks specific to that city. This enables more accurate quantification by taking the patient's geographical distribution into account. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can analyze the geographical distribution of patients using the generation AI and perform quantification based on the results.

[0046] The quantification unit can improve the accuracy of quantification by referring to literature related to the patient during quantification. For example, the quantification unit performs quantification by referring to literature related to the patient's symptoms. For example, if the patient answers, "My right shoulder hurts," the quantification unit performs quantification by referring to literature related to the symptom. The quantification unit can also perform quantification by referring to literature related to the patient's medical history. For example, if the patient answers, "I have had heart disease in the past," the quantification unit performs quantification by referring to literature related to the medical history. The quantification unit can also perform quantification by referring to literature related to the patient's answer. For example, if the patient answers, "I've been having headaches lately," the quantification unit performs quantification by referring to literature related to the answer. By referring to related literature, the accuracy of quantification is improved. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can analyze literature related to the patient and perform quantification based on the results.

[0047] When recording, the recording unit can select the optimal recording method by referring to the patient's past record data. In the recording unit, for example, the generation AI selects the optimal recording method based on the patient's past record data. For example, the recording unit analyzes the patient's past record data and selects the optimal recording format based on the results. The recording unit can also refer to the patient's past record data and have the generation AI improve the accuracy of the recording. For example, the recording unit refers to the patient's past diagnosis results and treatment history and selects the optimal recording method based on the results. In this way, the optimal recording method can be selected by referring to the past record data. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the recording unit can have the generation AI analyze the patient's past record data and select the optimal recording method based on the results.

[0048] The recording unit can customize the recording method based on the patient's current health condition when recording. For example, when the patient reports their current health condition, the recording unit adjusts the recording method based on that information. For example, when the patient reports that they recently caught a cold, the recording unit allows the generation AI to adjust the recording method based on that information. Furthermore, when the patient's health condition changes, the recording unit allows the generation AI to update the recording method. For example, when the patient reports that they have now recovered from their cold, the recording unit updates the recording method based on that information. Furthermore, the recording unit allows the generation AI to select the optimal recording method based on the patient's current health condition. For example, when the patient reports that they are currently in good health, the recording unit selects the optimal recording method based on that information. This enables more appropriate recording by providing a recording method that corresponds to the patient's health condition. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the generation AI can analyze the patient's current health condition and customize the recording method based on the results.

[0049] The recording unit can select the optimal recording method by taking into account the patient's geographic location information when recording. For example, if the patient lives in a specific area, the recording unit selects the recording method by taking into account the health risks specific to that area. For example, if the patient reports that they "live in a specific area," the recording unit selects the recording method based on the health risks specific to that area. Furthermore, if the patient is traveling, the recording unit can also select the recording method by taking into account the health risks of the travel destination. For example, if the patient reports that they "are currently traveling," the recording unit selects the recording method based on the health risks of the travel destination. Furthermore, the recording unit can also select the optimal recording method by using the generation AI based on the patient's geographic location information. For example, if the patient reports that they "live in a city," the recording unit selects the recording method based on the health risks specific to that city. This enables more appropriate recording by providing a recording method based on the patient's geographic location information. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can analyze the patient's geographic location information using the generation AI and select the optimal recording method based on the results.

[0050] The recording unit can analyze the patient's social media activity and suggest a recording method when recording. For example, if the patient posts about health on social media, the recording unit can suggest a recording method based on the content of the post. For example, if the patient reports that they recently posted about health, the recording unit can have the generating AI suggest a recording method based on the content. Furthermore, if the patient shows interest in a specific health topic, the recording unit can also suggest a recording method related to that topic. For example, if the patient reports that they have recently become interested in a specific health topic, the recording unit can suggest a recording method related to that topic. Furthermore, the recording unit can have the generating AI suggest an optimal recording method based on the patient's social media activity. For example, if the patient reports that they recently posted about lifestyle habits on social media, the recording unit can suggest a recording method based on the content. This enables more appropriate recording by providing a recording method based on the patient's social media activity. Some or all of the above-described processing in the recording unit can be performed using, or without, the generating AI. For example, the recording unit can have the generating AI analyze the patient's social media activity and suggest a recording method based on the results.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When analyzing a patient's responses, the analysis unit can refer to the patient's past medical history to improve the accuracy of the analysis. For example, if the patient has suffered from a specific disease in the past, it can prioritize analysis of responses related to that disease. It can also analyze the relationship with current symptoms based on the patient's past treatment history. Furthermore, it is possible to take the patient's family history into consideration and perform an analysis that includes genetic factors. This allows for more accurate analysis by utilizing the patient's past medical history.

[0053] When quantifying the patient's responses, the quantification unit can adjust the quantification criteria taking into account the patient's lifestyle. For example, if the patient is a smoker, the intensity of pain can be quantified taking into account the effects of smoking. Also, if the patient exercises regularly, the location and intensity of pain can be quantified taking into account the patient's exercise habits. Furthermore, it is possible to perform quantification that makes it easier to identify the cause of pain by taking into account the patient's dietary and sleeping habits. This allows for quantification that reflects the patient's lifestyle.

[0054] The recording unit can use encryption technology to protect patient privacy when recording quantified data. For example, data recorded in electronic medical records can be encrypted to prevent unauthorized access by third parties. Privacy protection can also be strengthened by anonymizing and recording patient personal information. Furthermore, it is possible to set a data retention period and have the system equipped with a function to automatically delete data after a certain period. This allows for efficient data recording while protecting patient privacy.

[0055] The analysis unit can provide real-time feedback when analyzing the patient's answers. For example, it can present advice or the next question based on the patient's answer immediately after the patient enters it. It can also instantly display the analysis results of the patient's answers, making it easier for the patient to understand their condition. Furthermore, if the patient has any questions or concerns, it can ask additional questions on the spot to gather more detailed information. This provides real-time feedback and enables efficient medical interviews.

[0056] When recording quantified data, the recording unit can store patient data in cloud storage. For example, electronic medical record data can be stored in the cloud and shared among multiple medical institutions. Using cloud storage also makes it easier to back up and recover data. Furthermore, performing data analysis on the cloud can yield more advanced analysis results. This allows for efficient data management using cloud storage.

[0057] When analyzing the patient's answers, the analysis unit can evaluate the reliability of the patient's answers. For example, it can check whether the patient's answers are consistent and detect inconsistent answers. It can also check whether the patient's answers match past data and prioritize analysis of reliable answers. Furthermore, if the patient's answers are ambiguous, it can ask additional questions to resolve the ambiguity. This allows for evaluation of the reliability of the patient's answers and more accurate analysis.

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

[0059] Step 1: The reception unit accepts the patient's words. These include oral statements, written descriptions, emails, etc. For example, when a patient expresses the location and severity of their pain in a question-and-answer format, the reception unit accepts these words. Generative AI can also be used to accept the patient's words in a question-and-answer format. Step 2: The analysis unit analyzes the patient's words received by the reception unit. The analysis is performed using natural language processing technology, morphological analysis, and grammatical analysis. Generative AI can also be used to analyze the patient's words and understand their meaning. Step 3: The quantification unit quantifies the patient's words analyzed by the analysis unit. Quantification is performed by quantifying the intensity and location of pain. For example, if the patient answers "It hurts a lot," the answer is quantified as "Pain intensity: 8 / 10." Generative AI can also be used to quantify the patient's words. Step 4: The recording unit records the data quantified by the quantification unit. The recording is performed by automatically recording the data in an electronic medical record. For example, the quantified data may be recorded in an electronic medical record and stored in a database. Alternatively, the quantified data may be recorded using generative AI.

[0060] (Example 2) A medical interview response system according to an embodiment of the present invention uses a generative AI to efficiently analyze, quantify, and record patient responses. When a patient verbally describes the location and severity of their pain, the generative AI accepts the patient's responses in a question-and-answer format, analyzes them, and converts them into a standardized expression. The generative AI then quantifies the location and severity of the pain based on the patient's responses and automatically records them in the electronic medical record. This allows doctors and nurses to quickly and accurately grasp the patient's pain status. This system reduces hospital wait times, improves patient awareness, and increases nurse productivity. For example, nurses no longer need to conduct detailed interviews with each patient; the generative AI automatically conducts the interview, allowing nurses to focus on other tasks. The generative AI also has an algorithm for quantifying the location and severity of pain based on the patient's responses. For example, if a patient responds "very painful," the generative AI records the response as "pain intensity: 8 / 10." This allows for a consistent assessment of the patient's pain status. The generative AI also has a database for quantifying the location and severity of pain based on the patient's responses. For example, based on past patient response data, the generation AI analyzes new patient responses and selects the optimal quantification method. This allows the generation AI to always perform accurate quantification based on the most recent data. This allows the medical interview response system to efficiently analyze, quantify, and record patient words.

[0061] The medical interview response system according to the embodiment includes a reception unit, an analysis unit, a quantification unit, and a recording unit. The reception unit receives the patient's words. Examples of the patient's words include, but are not limited to, oral statements, written descriptions, and emails. The reception unit receives, for example, when the patient expresses the location and severity of their pain in a question-and-answer format. The reception unit can also use a generation AI to receive the patient's words in a question-and-answer format. For example, if the generation AI asks the patient, "Where does it hurt?" and the patient replies, "My right shoulder hurts," the reception unit accepts the answer. The analysis unit analyzes the patient's words received by the reception unit. The analysis is performed, for example, using natural language processing technology, but is not limited to, for example. For example, the analysis unit analyzes the patient's words using morphological analysis and performs grammatical analysis. The analysis unit can also analyze the patient's words using a generation AI. For example, the generation AI analyzes the patient's words and understands their meaning. The quantification unit quantifies the patient's words analyzed by the analysis unit. Quantification may be performed, for example, by quantifying the intensity and location of pain, but is not limited to such an example. For example, if a patient answers "it hurts a lot," the quantification unit quantifies the answer as "pain intensity: 8 / 10." The quantification unit may also quantify the patient's words using a generation AI. For example, the generation AI analyzes the patient's words and performs quantification based on the results. The recording unit records the data quantified by the quantification unit. Recording may be performed, for example, by automatically recording the data in an electronic medical record, but is not limited to such an example. For example, the recording unit records the quantified data in the electronic medical record and stores it in a database. The recording unit may also record the quantified data using a generation AI. For example, the generation AI records the quantified data in the electronic medical record and stores it in a database. This allows the medical interview response system according to the embodiment to efficiently analyze, quantify, and record the patient's words.

[0062] The reception unit can accept the location or degree of the patient's pain in a question-and-answer format. For example, when a patient expresses the location or degree of pain in a question-and-answer format, the reception unit accepts the words. For example, if the generation AI asks the patient, "Where does it hurt?" and the patient replies, "My right shoulder hurts," the reception unit accepts the answer. The reception unit can also use the generation AI to accept the patient's words in a question-and-answer format. For example, if the generation AI asks the patient, "How intense is the pain?" and the patient replies, "It hurts a lot," the reception unit accepts the answer. This allows the location and degree of the patient's pain to be accepted efficiently. Some or all of the above-described processing in the reception unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reception unit can accept the patient's words in a question-and-answer format based on the results of the generation AI analyzing the patient's words.

[0063] The analysis unit can analyze the patient's responses using natural language processing technology. The analysis unit, for example, analyzes the patient's words using morphological analysis. For example, the analysis unit breaks down the patient's words into morphemes and analyzes the meaning of each morpheme. The analysis unit can also analyze the patient's words using grammatical analysis. For example, the analysis unit analyzes the grammatical structure of the patient's words to understand their meaning. The analysis unit can also analyze the patient's words using semantic analysis. For example, the analysis unit analyzes the meaning of the patient's words to understand their content. This allows the patient's responses to be analyzed accurately. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can analyze the patient's words using a generation AI and perform analysis based on the results.

[0064] The quantification unit can quantify the intensity and location of pain based on the patient's response. The quantification unit performs quantification, for example, by a method that quantifies the intensity of pain. For example, if the patient responds, "It hurts a lot," the quantification unit quantifies the response as "pain intensity: 8 / 10." The quantification unit can also perform quantification by a method that quantifies the location of pain. For example, if the patient responds, "My right shoulder hurts," the quantification unit quantifies the response as "pain location: right shoulder." The quantification unit can also quantify the patient's words using a generation AI. For example, the generation AI analyzes the patient's words and performs quantification based on the results. This allows the patient's pain intensity and location to be accurately quantified. Some or all of the above-described processing in the quantification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the quantification unit can analyze the patient's words using a generation AI and perform quantification based on the results.

[0065] The recording unit can automatically record the quantified data in the electronic medical record. The recording unit, for example, records the quantified data in the electronic medical record. For example, the recording unit automatically records the quantified data in the electronic medical record and stores it in a database. The recording unit can also record the quantified data using a generating AI. For example, the generating AI records the quantified data in the electronic medical record and stores it in a database. This allows the quantified data to be recorded efficiently. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the recording unit can analyze the quantified data using the generating AI and record based on the results.

[0066] The quantification unit can analyze the responses of a new patient based on past patient data and select a quantification method. The quantification unit, for example, analyzes the responses of a new patient based on past patient data. For example, the quantification unit analyzes the response data of past patients and analyzes the responses of a new patient based on the results. The quantification unit can also select a quantification method based on past patient data. For example, the quantification unit selects an optimal quantification method based on the response data of past patients. This makes it possible to improve the accuracy of quantification by utilizing past data. Some or all of the above-mentioned processing in the quantification unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the quantification unit can analyze past patient data using a generation AI, and then analyze the responses of a new patient based on the results and select a quantification method.

[0067] The reception unit can estimate the patient's emotions and adjust the speed of the dialogue based on the estimated emotions. The reception unit, for example, estimates the patient's emotions and adjusts the speed of the dialogue based on the emotions. For example, if the patient is nervous, the reception unit has the generation AI ask questions at a slow pace to allow the patient to relax. Alternatively, if the patient is relaxed, the reception unit can have the generation AI ask questions at a normal pace to efficiently conduct the interview. Alternatively, if the patient is in a hurry, the reception unit can have the generation AI ask questions quickly to collect necessary information in a short time. This allows for more appropriate interviews by adjusting the speed of the dialogue based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or without the generation AI. For example, the reception department can use the generation AI to estimate the patient's emotions and adjust the pace of the question and answer session based on the results.

[0068] The reception unit can analyze the patient's past medical interview history and select the optimal question and answer format. The reception unit, for example, analyzes the patient's past medical interview history and selects the optimal question and answer format based on the results. For example, if the patient has preferred detailed medical interviews in the past, the reception unit can have the generation AI ask detailed questions. Also, if the patient has preferred brief medical interviews in the past, the reception unit can have the generation AI ask brief questions. Also, the reception unit can analyze the patient's past answer patterns and have the generation AI select the optimal question order. This enables efficient medical interviews by selecting the optimal question and answer format using the past medical interview history. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit can have the generation AI analyze the patient's past medical interview history and select the optimal question and answer format based on the results.

[0069] The reception unit can customize the questions asked based on the patient's current health condition and lifestyle habits during the medical interview. For example, if the patient reports their current health condition, the reception unit asks relevant questions based on that information. For example, if the patient reports that they recently caught a cold, the generation AI asks, "What are your cold symptoms?" based on that information. The reception unit can also add specific questions based on the patient's lifestyle habits (smoking, drinking, etc.). For example, if the patient reports that they smoke every day, the generation AI asks, "How often do you smoke?" The reception unit can also ask questions about specific medical conditions based on the patient's medical history. For example, if the patient reports that they have had heart disease in the past, the generation AI asks, "How is your heart condition now?" This enables more appropriate medical interviews by asking questions tailored to the patient's health condition and lifestyle habits. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception department can use the generation AI to analyze the patient's current health condition and lifestyle habits, and customize the questions based on the results.

[0070] The reception unit can estimate the patient's emotions and prioritize questions based on the estimated patient emotions. For example, the reception unit estimates the patient's emotions and prioritizes questions based on the estimated emotions. For example, if the patient is feeling anxious, the reception unit can have the generation AI first ask reassuring questions. Also, if the patient is relaxed, the reception unit can have the generation AI start with important questions. Also, if the patient is in a hurry, the reception unit can have the generation AI prioritize the most important questions. This enables more appropriate medical interviews by prioritizing questions based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can have the generation AI estimate the patient's emotions and prioritize questions based on the results.

[0071] During the medical interview, the reception unit can prioritize relevant questions by taking into account the patient's geographic location information. For example, if the patient lives in a specific area, the reception unit asks questions about diseases specific to that area. For example, if the patient reports that they recently traveled to a specific area, the generation AI asks questions about health risks specific to that area. Furthermore, if the patient is currently traveling, the reception unit can also ask questions about health risks at the travel destination. For example, if the patient reports that they are currently traveling, the generation AI asks questions about health risks at the travel destination. Furthermore, if the patient lives in a specific environment (urban, rural, etc.), the reception unit can also ask questions about health risks associated with that environment. For example, if the patient reports that they live in a rural area, the generation AI asks questions about health risks specific to rural areas. This allows for more appropriate medical interviews by asking questions based on the patient's geographic location information. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception department can use the generation AI to analyze the patient's geographic location information and prioritize relevant questions based on the results.

[0072] The reception unit can analyze the patient's social media activity and ask relevant questions during the medical interview. For example, if the patient has posted about health on social media, the reception unit can ask questions based on the content of the post. For example, if the patient reports that they recently posted about health, the reception unit can ask the patient questions based on the content of the post, such as "Please tell us more about that post." Furthermore, if the patient shows interest in a specific health topic, the reception unit can ask questions related to that topic. For example, if the reception unit reports that they have recently become interested in a specific health topic, the generation AI can ask questions related to that topic. Furthermore, the reception unit can ask questions about lifestyle habits based on the patient's social media activity. For example, if the reception unit reports that they recently posted about lifestyle habits on social media, the generation AI can ask the patient questions based on the content of the post, such as "Please tell us more about that lifestyle habit." This allows for more appropriate medical interviews by asking questions based on the patient's social media activity. Some or all of the above-described processing in the reception unit can be performed using, or without, the generation AI. For example, the reception department can use generative AI to analyze a patient's social media activity and ask relevant questions based on the results.

[0073] The analysis unit can estimate the patient's emotions and adjust the accuracy of the analysis based on the estimated patient's emotions. The analysis unit, for example, estimates the patient's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, if the patient is nervous, the analysis unit can have the generation AI perform the analysis carefully to avoid misunderstandings. Furthermore, if the patient is relaxed, the analysis unit can have the generation AI perform the analysis with normal accuracy. Furthermore, if the patient is in a hurry, the analysis unit can have the generation AI perform the analysis quickly and provide the necessary information. This enables more accurate analysis by adjusting the accuracy of the analysis according to the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can have the generation AI estimate the patient's emotions and adjust the accuracy of the analysis based on the result.

[0074] During analysis, the analysis unit can optimize the analysis algorithm by taking into account the context of the patient's response. For example, if the patient's response is ambiguous, the analysis unit allows the generation AI to take the context into account and perform the optimal interpretation. For example, if the patient answers "I have pain," the analysis unit interprets "where the pain is" by taking the context into account. Furthermore, if the patient's response is detailed, the generation AI can perform an analysis that reflects that detail. For example, if the patient answers "I have a sharp pain in my right shoulder," the analysis unit reflects that detail. Furthermore, if the patient's response is inconsistent, the generation AI can consider the context to ensure consistency. For example, if the patient answers "my right shoulder hurts" and then "my left shoulder hurts," the analysis unit considers the context to ensure consistency. This allows for more accurate analysis by taking into account the context of the patient's response. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the generation AI can analyze the context of the patient's response and optimize the analysis algorithm based on the results.

[0075] During analysis, the analysis unit can apply different analysis methods depending on the category of the patient's answer. For example, the generation AI applies a specific analysis method to answers regarding pain intensity. For example, if the patient answers "It hurts a lot," the analysis unit analyzes the pain intensity based on that answer. The generation AI can also apply a different analysis method to answers regarding the location of the pain. For example, if the patient answers "My right shoulder hurts," the analysis unit analyzes the location of the pain based on that answer. The analysis unit can also apply yet another analysis method to answers regarding the duration of the pain. For example, if the patient answers "The pain has been going on for a week," the analysis unit analyzes the duration of the pain based on that answer. This allows for more accurate analysis by applying an analysis method depending on the category of the patient's answer. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the generation AI can analyze the category of the patient's answer and apply a different analysis method based on the results.

[0076] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated patient's emotions. For example, the analysis unit estimates the patient's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the patient is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the patient is relaxed, the generation AI can provide a display method that includes detailed information. Furthermore, if the patient is in a hurry, the generation AI can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method of the analysis results according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can use the generation AI to estimate the patient's emotions and adjust the display method of the analysis results based on the estimation result.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the timing of patient response submission. The analysis unit determines the priority of analysis based on, for example, the timing of patient response submission. For example, the analysis unit prioritizes analysis of the most recently submitted response by the patient. Furthermore, if the patient submits an urgent response, the analysis unit can cause the generation AI to analyze that response as the highest priority. Furthermore, the analysis unit can cause the generation AI to adjust the order of analysis based on the timing of patient response submission. This enables efficient analysis by determining the priority of analysis based on the timing of patient response submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can cause the generation AI to analyze the timing of patient response submission and determine the priority of analysis based on the results.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the patient's answers. For example, if a patient's answer is related to other answers, the analysis unit performs the analysis with the generation AI taking that relevance into account. For example, if a patient answers, "My right shoulder hurts," and then answers, "My left shoulder also hurts," the analysis unit performs the analysis taking that relevance into account. Furthermore, if the patient's answers are independent, the analysis unit can have the generation AI analyze the answers individually. For example, if a patient answers, "My right shoulder hurts," and then answers, "I have a headache," the analysis unit analyzes the answers individually. Furthermore, the analysis unit can optimize the order of analysis with the generation AI based on the relevance of the patient's answers. For example, if the patient's answers are related, the analysis unit adjusts the order of analysis based on that relevance. This enables efficient analysis by adjusting the order of analysis based on the relevance of the patient's answers. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can have the generation AI analyze the relevance of the patient's answers and adjust the order of analysis based on the results.

[0079] The quantification unit can estimate the patient's emotions and adjust the quantification criteria based on the estimated patient's emotions. For example, the quantification unit estimates the patient's emotions and adjusts the quantification criteria based on the emotions. For example, if the patient is nervous, the quantification unit can have the generation AI perform quantification carefully to avoid misunderstandings. Furthermore, if the patient is relaxed, the quantification unit can have the generation AI perform quantification using the normal criteria. Furthermore, if the patient is in a hurry, the quantification unit can have the generation AI perform quantification quickly and provide the necessary information. This enables more accurate quantification by adjusting the quantification criteria according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the quantification unit may be performed using, for example, the generation AI, or without the generation AI. For example, the quantification unit can have the generation AI estimate the patient's emotions and adjust the quantification criteria based on the results.

[0080] The quantification unit can improve the accuracy of quantification by taking into account the interrelationships between patient responses during quantification. For example, if the patient's responses are consistent, the quantification unit allows the generation AI to reflect that consistency in quantification. For example, if the patient answers "My right shoulder hurts" and then "My right shoulder pain continues," the quantification unit performs quantification that reflects that consistency. Furthermore, if the patient's responses are inconsistent, the quantification unit can also perform quantification by resolving the inconsistency. For example, if the patient answers "My right shoulder hurts" and then "My left shoulder hurts," the quantification unit can perform quantification by resolving the inconsistency. Furthermore, the quantification unit can also perform optimal quantification by taking into account the interrelationships between patient responses. For example, if the patient's responses are related, the quantification unit performs quantification based on the relevance. This allows for more accurate quantification by taking into account the interrelationships between patient responses. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can have the generation AI analyze the interrelationships between patients' answers and perform quantification based on the results.

[0081] The quantification unit can perform quantification taking into account the patient's attribute information. For example, the quantification unit allows the generation AI to perform appropriate quantification based on the patient's age. For example, if the patient answers "50 years old," the quantification unit performs quantification based on the patient's age. The quantification unit can also perform appropriate quantification based on the patient's gender. For example, if the patient answers "female," the quantification unit performs quantification based on the patient's gender. The quantification unit can also perform appropriate quantification based on the patient's medical history. For example, if the patient answers "I have suffered from heart disease in the past," the quantification unit performs quantification based on the medical history. This allows for more accurate quantification by taking the patient's attribute information into consideration. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can analyze the patient's attribute information using the generation AI and perform quantification based on the results.

[0082] The quantification unit can estimate the patient's emotions and adjust the display method of the quantification results based on the estimated patient's emotions. For example, the quantification unit estimates the patient's emotions and adjusts the display method of the quantification results based on the estimated emotions. For example, if the patient is nervous, the generation AI can provide a simple, highly visible display method. Furthermore, if the patient is relaxed, the quantification unit can provide a display method that includes detailed information. Furthermore, if the patient is in a hurry, the quantification unit can provide a display method that focuses on the main points. This allows for more appropriate information provision by adjusting the display method of the quantification results according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the quantification unit may be performed using, for example, the generation AI, or without the generation AI. For example, the quantification unit can use the generation AI to estimate the patient's emotions and adjust the display method of the quantification results based on the estimation result.

[0083] The quantification unit can perform quantification taking into account the geographical distribution of the patient. For example, if the patient lives in a specific area, the quantification unit performs quantification taking into account health risks specific to that area. For example, if the patient answers "I live in a specific area," the quantification unit performs quantification based on health risks specific to that area. Furthermore, if the patient is traveling, the quantification unit can also perform quantification taking into account health risks at the travel destination. For example, if the patient answers "I am currently traveling," the quantification unit performs quantification based on health risks at the travel destination. Furthermore, the quantification unit can perform optimal quantification using the generation AI based on the patient's geographical distribution. For example, if the patient answers "I live in a city," the quantification unit performs quantification based on health risks specific to that city. This enables more accurate quantification by taking the patient's geographical distribution into account. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can analyze the geographical distribution of patients using the generation AI and perform quantification based on the results.

[0084] The quantification unit can improve the accuracy of quantification by referring to literature related to the patient during quantification. For example, the quantification unit performs quantification by referring to literature related to the patient's symptoms. For example, if the patient answers, "My right shoulder hurts," the quantification unit performs quantification by referring to literature related to the symptom. The quantification unit can also perform quantification by referring to literature related to the patient's medical history. For example, if the patient answers, "I have had heart disease in the past," the quantification unit performs quantification by referring to literature related to the medical history. The quantification unit can also perform quantification by referring to literature related to the patient's answer. For example, if the patient answers, "I've been having headaches lately," the quantification unit performs quantification by referring to literature related to the answer. By referring to related literature, the accuracy of quantification is improved. Some or all of the above-described processing in the quantification unit may be performed using, or without, the generation AI. For example, the quantification unit can analyze literature related to the patient and perform quantification based on the results.

[0085] The recording unit can estimate the patient's emotions and adjust the recording method based on the estimated patient's emotions. For example, the recording unit can estimate the patient's emotions and adjust the recording method based on the estimated emotions. For example, if the patient is nervous, the generation AI can provide a simple, highly visible recording method. Furthermore, if the patient is relaxed, the generation AI can provide a recording method that includes detailed information. Furthermore, if the patient is in a hurry, the generation AI can provide a recording method that focuses on the main points. This allows for more appropriate recording by adjusting the recording method according to the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the recording unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation AI can estimate the patient's emotions and adjust the recording method based on the result.

[0086] When recording, the recording unit can select the optimal recording method by referring to the patient's past record data. In the recording unit, for example, the generation AI selects the optimal recording method based on the patient's past record data. For example, the recording unit analyzes the patient's past record data and selects the optimal recording format based on the results. The recording unit can also refer to the patient's past record data and have the generation AI improve the accuracy of the recording. For example, the recording unit refers to the patient's past diagnosis results and treatment history and selects the optimal recording method based on the results. In this way, the optimal recording method can be selected by referring to the past record data. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the recording unit can have the generation AI analyze the patient's past record data and select the optimal recording method based on the results.

[0087] The recording unit can customize the recording method based on the patient's current health condition when recording. For example, when the patient reports their current health condition, the recording unit adjusts the recording method based on that information. For example, when the patient reports that they recently caught a cold, the recording unit allows the generation AI to adjust the recording method based on that information. Furthermore, when the patient's health condition changes, the recording unit allows the generation AI to update the recording method. For example, when the patient reports that they have now recovered from their cold, the recording unit updates the recording method based on that information. Furthermore, the recording unit allows the generation AI to select the optimal recording method based on the patient's current health condition. For example, when the patient reports that they are currently in good health, the recording unit selects the optimal recording method based on that information. This enables more appropriate recording by providing a recording method that corresponds to the patient's health condition. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the generation AI can analyze the patient's current health condition and customize the recording method based on the results.

[0088] The recording unit can estimate the patient's emotions and determine the priority of recording based on the estimated patient's emotions. The recording unit, for example, estimates the patient's emotions and determines the priority of recording based on the emotions. For example, if the patient is nervous, the generation AI can prioritize recording important information. Also, if the patient is relaxed, the generation AI can prioritize recording detailed information. Also, if the patient is in a hurry, the generation AI can prioritize recording key points. This enables more appropriate recording by determining the priority of recording based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the recording unit can use the generation AI to estimate the patient's emotions and determine the priority of recording based on the result.

[0089] The recording unit can select the optimal recording method by taking into account the patient's geographic location information when recording. For example, if the patient lives in a specific area, the recording unit selects the recording method by taking into account the health risks specific to that area. For example, if the patient reports that they "live in a specific area," the recording unit selects the recording method based on the health risks specific to that area. Furthermore, if the patient is traveling, the recording unit can also select the recording method by taking into account the health risks of the travel destination. For example, if the patient reports that they "are currently traveling," the recording unit selects the recording method based on the health risks of the travel destination. Furthermore, the recording unit can also select the optimal recording method by using the generation AI based on the patient's geographic location information. For example, if the patient reports that they "live in a city," the recording unit selects the recording method based on the health risks specific to that city. This enables more appropriate recording by providing a recording method based on the patient's geographic location information. Some or all of the above-described processing in the recording unit may be performed using, or without, the generation AI. For example, the recording unit can analyze the patient's geographic location information using the generation AI and select the optimal recording method based on the results.

[0090] The recording unit can analyze the patient's social media activity and suggest a recording method when recording. For example, if the patient posts about health on social media, the recording unit can suggest a recording method based on the content of the post. For example, if the patient reports that they recently posted about health, the recording unit can have the generating AI suggest a recording method based on the content. Furthermore, if the patient shows interest in a specific health topic, the recording unit can also suggest a recording method related to that topic. For example, if the patient reports that they have recently become interested in a specific health topic, the recording unit can suggest a recording method related to that topic. Furthermore, the recording unit can have the generating AI suggest an optimal recording method based on the patient's social media activity. For example, if the patient reports that they recently posted about lifestyle habits on social media, the recording unit can suggest a recording method based on the content. This enables more appropriate recording by providing a recording method based on the patient's social media activity. Some or all of the above-described processing in the recording unit can be performed using, or without, the generating AI. For example, the recording unit can have the generating AI analyze the patient's social media activity and suggest a recording method based on the results. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, quantification unit, and recording unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives the patient's words. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the patient's words. The quantification unit is realized by the specific processing unit 290 of the data processing device 12 and quantifies the analyzed words. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the quantified data in the electronic medical record. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, quantification unit, and recording unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives the patient's words. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the patient's words. The quantification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quantifies the analyzed words. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the quantified data in the electronic medical record. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, quantification unit, and recording unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives the patient's words. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the patient's words. The quantification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quantifies the analyzed words. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the quantified data in the electronic medical record. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, quantification unit, and recording unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the patient's words. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the patient's words. The quantification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and quantifies the analyzed words. The recording unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the quantified data in the electronic medical record.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When receiving a patient's words, the reception unit can analyze the patient's tone of voice and speaking style to infer the patient's emotions. For example, if the patient is nervous, the generation AI can ask questions that will relax them to ease their tension. Also, if the patient is feeling angry or anxious, the reception unit can take these emotions into consideration and respond gently and politely. Furthermore, if the patient is relaxed, the generation AI can smoothly continue the questions to maintain that state. This makes it possible to respond according to the patient's emotions and conduct more appropriate medical interviews.

[0093] When analyzing a patient's responses, the analysis unit can refer to the patient's past medical history to improve the accuracy of the analysis. For example, if the patient has suffered from a specific disease in the past, it can prioritize analysis of responses related to that disease. It can also analyze the relationship with current symptoms based on the patient's past treatment history. Furthermore, it is possible to take the patient's family history into consideration and perform an analysis that includes genetic factors. This allows for more accurate analysis by utilizing the patient's past medical history.

[0094] When quantifying the patient's responses, the quantification unit can adjust the quantification criteria taking into account the patient's lifestyle. For example, if the patient is a smoker, the intensity of pain can be quantified taking into account the effects of smoking. Also, if the patient exercises regularly, the location and intensity of pain can be quantified taking into account the patient's exercise habits. Furthermore, it is possible to perform quantification that makes it easier to identify the cause of pain by taking into account the patient's dietary and sleeping habits. This allows for quantification that reflects the patient's lifestyle.

[0095] The recording unit can use encryption technology to protect patient privacy when recording quantified data. For example, data recorded in electronic medical records can be encrypted to prevent unauthorized access by third parties. Privacy protection can also be strengthened by anonymizing and recording patient personal information. Furthermore, it is possible to set a data retention period and have the system equipped with a function to automatically delete data after a certain period. This allows for efficient data recording while protecting patient privacy.

[0096] The reception unit can estimate the patient's emotions and provide feedback to the patient based on the estimated patient emotions. For example, if the patient is feeling anxious, the reception unit can provide advice to reduce that anxiety. If the patient is relaxed, the reception unit can provide positive feedback to maintain that state. Furthermore, if the patient is feeling angry, the reception unit can take action to alleviate that anger. This allows for feedback based on the patient's emotions, resulting in a better medical interview experience.

[0097] The analysis unit can provide real-time feedback when analyzing the patient's answers. For example, it can present advice or the next question based on the patient's answer immediately after the patient enters it. It can also instantly display the analysis results of the patient's answers, making it easier for the patient to understand their condition. Furthermore, if the patient has any questions or concerns, it can ask additional questions on the spot to gather more detailed information. This provides real-time feedback and enables efficient medical interviews.

[0098] The quantification unit can estimate the patient's emotions and visually display the results of the quantification based on the estimated patient emotions. For example, if the patient is nervous, the quantification results can be displayed as simple graphs or icons to make them easier to understand. If the patient is relaxed, detailed data and statistical information can be displayed. Furthermore, if the patient is in a hurry, a concise display that focuses on the main points can be provided. This allows for visual displays that correspond to the patient's emotions, enabling more appropriate information to be provided.

[0099] When recording quantified data, the recording unit can store patient data in cloud storage. For example, electronic medical record data can be stored in the cloud and shared among multiple medical institutions. Using cloud storage also makes it easier to back up and recover data. Furthermore, performing data analysis on the cloud can yield more advanced analysis results. This allows for efficient data management using cloud storage.

[0100] The reception unit can estimate the patient's emotions and customize how it responds to the patient based on the estimated emotions. For example, if the patient is nervous, the generation AI can respond in a gentle tone to ease the patient's tension. If the patient is relaxed, the reception unit can respond smoothly to maintain that state. Furthermore, if the patient is feeling angry, the generation AI can respond calmly and politely to ease the patient's anger. This allows the reception unit to respond in accordance with the patient's emotions, resulting in a better medical interview experience.

[0101] When analyzing the patient's answers, the analysis unit can evaluate the reliability of the patient's answers. For example, it can check whether the patient's answers are consistent and detect inconsistent answers. It can also check whether the patient's answers match past data and prioritize analysis of reliable answers. Furthermore, if the patient's answers are ambiguous, it can ask additional questions to resolve the ambiguity. This allows for evaluation of the reliability of the patient's answers and more accurate analysis.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit accepts the patient's words. These include oral statements, written descriptions, emails, etc. For example, when a patient expresses the location and severity of their pain in a question-and-answer format, the reception unit accepts these words. Generative AI can also be used to accept the patient's words in a question-and-answer format. Step 2: The analysis unit analyzes the patient's words received by the reception unit. The analysis is performed using natural language processing technology, morphological analysis, and grammatical analysis. Generative AI can also be used to analyze the patient's words and understand their meaning. Step 3: The quantification unit quantifies the patient's words analyzed by the analysis unit. Quantification is performed by quantifying the intensity and location of pain. For example, if the patient answers "It hurts a lot," the answer is quantified as "Pain intensity: 8 / 10." Generative AI can also be used to quantify the patient's words. Step 4: The recording unit records the data quantified by the quantification unit. The recording is performed by automatically recording the data in an electronic medical record. For example, the quantified data may be recorded in an electronic medical record and stored in a database. Alternatively, the quantified data may be recorded using generative AI.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] 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.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section that receives patient comments; an analysis unit that analyzes the words of the patient received by the reception unit; a quantification unit that quantifies the words of the patient analyzed by the analysis unit; a recording unit that records the data quantified by the quantification unit. A system characterized by:

2. The reception unit Accept the patient's pain location and severity in a question-and-answer format 2. The system of claim 1.

3. The analysis unit Analyze patient responses using natural language processing technology 2. The system of claim 1.

4. The quantification unit Quantifying pain intensity and location based on patient responses 2. The system of claim 1.

5. The recording unit Automatically record quantified data in the electronic medical record 2. The system of claim 1.

6. The quantification unit Analyze new patient responses based on past patient data and select quantification methods 2. The system of claim 1.

7. The reception unit Estimate the patient's emotions and adjust the pace of the conversation based on the estimated emotions.

2. The system of claim 1.

8. The reception unit Analyze the patient's past medical history and select the optimal question and answer format 2. The system of claim 1.

Images