System

Abstract

To provide a system capable of supporting the diagnosis of a doctor by analyzing voice data and listing the names of diseases.SOLUTION: The system in an embodiment includes a collection unit, an analysis unit, a listing unit, and an output unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and extracts specific keywords or phrases. The listing unit lists the names of diseases based on keywords and phrases extracted by the analysis unit. The output unit outputs the factors used as criteria for the disease names listed by the listing unit and their contribution rates.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to identify the name of the disease based on voice data, and there was a problem in that it was not possible to efficiently provide data to support a doctor's diagnosis.

[0005] The system according to the embodiment aims to analyze voice data, list the names of diseases, and support doctors in making diagnoses. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a list-up unit, and an output unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and extracts specific keywords and phrases. The list-up unit lists disease names based on the keywords and phrases extracted by the analysis unit. The output unit outputs the factors that served as judgment criteria for the disease names listed by the list-up unit and their contribution rates. Effect of the Invention

[0007] The system according to the embodiment can analyze voice data, list the name of a disease, and support a doctor's diagnosis. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The disease name listing system according to the embodiment of the present invention is a system that lists possible disease names by capturing and analyzing the subject's everyday conversation as voice data. This disease name listing system collects the subject's conversation as voice data, analyzes it, and lists possible disease names. At this time, the elements (words, sentences, etc.) that were used as the judgment criteria and the contribution rate of the elements are also output. This can be used as support data for doctors when they actually visit the doctor. In addition, if the output data of this service is used by doctors, a usage fee can be charged from patients and hospitals. For example, the subject's conversation is collected as voice data. At this time, the contents of the subject's daily conversation are recorded and saved as voice data. For example, the contents of the subject's conversations with family and friends, conversations at work, etc. can be collected. Next, the collected voice data is analyzed. A generation AI is used for the analysis, and specific words and phrases are extracted from the voice data. For example, if words such as "tired," "pain," and "can't sleep" appear frequently, these words become the judgment criteria for disease names. Based on the analysis results, possible disease names are listed. The generation AI compares the collected voice data with existing medical databases and lists the names of diseases that match the subject's symptoms. For example, if words such as "tired," "pain," and "can't sleep" appear frequently, the generation AI will list the names of diseases related to those symptoms. In addition, for the listed disease names, the elements (words, sentences, etc.) that were used as judgment criteria and the contribution rate of the elements are also output. This allows the doctor to understand to what extent each element contributed to the judgment of the disease name. For example, if the word "tired" contributes 50% to the judgment of the disease name and the word "pain" contributes 30%, this information can be provided to the doctor. This system can be used as support data for the doctor when actually visiting the doctor. The doctor can make a diagnosis while referring to the listed disease names and their judgment criteria. This improves the accuracy of the diagnosis and enables appropriate treatment for the patient. In addition, if the output data of this service is used by the doctor, a usage fee can be charged from the patient and the hospital. For example, using the data of this service during a diagnosis improves the accuracy of the diagnosis and increases the satisfaction of the patient.As a result, it is expected that hospitals and patients will receive usage fees. This will enable the disease name listing system to analyze the subjects' everyday conversations and support doctors in making diagnoses.

[0029] The disease name listing system according to the embodiment includes a collection unit, an analysis unit, a list unit, and an output unit. The collection unit collects the subject's everyday conversation as voice data. The subject's everyday conversation includes, for example, conversation at home, conversation at work, conversation with friends, etc., but is not limited to such examples. The collection unit, for example, records the content that the subject speaks at home and saves it as voice data. The collection unit can also record the content that the subject speaks at work and save it as voice data. Furthermore, the collection unit can also record the content that the subject speaks with friends and save it as voice data. For example, the collection unit records the content that the subject speaks at home with a high-sensitivity microphone and saves it as voice data. For the conversation at work, the content of a specific meeting or meeting is recorded and saved as voice data. For the conversation with friends, the conversation on the go is recorded and saved as voice data. The analysis unit uses a generation AI to analyze the voice data collected by the collection unit and extract specific words and phrases. The analysis is performed, for example, using voice recognition technology or natural language processing technology, but is not limited to such examples. For example, the analysis unit converts voice data into text data using voice recognition technology and extracts specific words and phrases. The analysis unit can also analyze text data using natural language processing technology and extract specific words and phrases. Furthermore, the analysis unit can extract specific words and phrases from voice data using generation AI. For example, the analysis unit converts voice data into text data using voice recognition technology and performs frequency analysis to extract specific words and phrases. The natural language processing technology performs semantic analysis of the text data and extracts specific words and phrases. The generation AI directly analyzes the voice data and extracts specific words and phrases. The list-up unit lists the disease names based on the words and phrases extracted by the analysis unit. The list-up is performed, for example, by checking against an existing medical database, but is not limited to such examples. For example, the list-up unit checks the words and phrases extracted by the analysis unit against an existing medical database and lists the disease names that match the symptoms of the subject. The listing unit can also use a generative AI to list disease names based on the words and phrases extracted by the analysis unit.Furthermore, the list-up unit can use a machine learning model to list disease names based on the words and phrases extracted by the analysis unit. For example, the list-up unit uses an existing medical database to list disease names that match the words and phrases extracted by the analysis unit. The generation AI compares the voice data with the medical database and lists disease names that match the symptoms of the subject. The machine learning model learns past case data and lists disease names based on the words and phrases extracted by the analysis unit. The output unit outputs the elements that served as the judgment criteria for the disease names listed by the list-up unit and the contribution rate of the elements. The output is performed, for example, in a text format or a graph format, but is not limited to such examples. For example, the output unit outputs the words and phrases that served as the judgment criteria for the listed disease names in a text format. The output unit can also output the contribution rate of the words and phrases that served as the judgment criteria for the listed disease names in a graph format. Furthermore, the output unit can also output the contribution rate of the words and phrases that served as the judgment criteria for the listed disease names in a text format. For example, the output unit outputs the words and phrases that were the criteria for the listed disease names in text format, and displays the contribution rate in graph format. The text format output displays the words and phrases that were the criteria in list format. The graph format output displays the contribution rate in a bar graph or pie chart. In this way, the disease name listing system according to the embodiment can analyze the subject's everyday conversation and support the doctor's diagnosis. Some or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can output the listed disease names and their judgment criteria using an AI model.

[0030] The collecting unit collects the subject's everyday conversation as voice data. The subject's everyday conversation includes, for example, conversation at home, conversation at work, conversation with friends, etc., but is not limited to such examples. For example, the collecting unit records the content that the subject speaks at home and saves it as voice data. The collecting unit can also record the content that the subject speaks at work and save it as voice data. Furthermore, the collecting unit can also record the content that the subject speaks with friends and save it as voice data. For example, the collecting unit records the content that the subject speaks at home with a high-sensitivity microphone and saves it as voice data. For conversation at work, the content of a specific meeting or meeting is recorded and saved as voice data. For conversation with friends, conversations while out are recorded and saved as voice data. The collecting unit manages these voice data in a centralized manner and makes them accessible to the analyzing unit. When collecting voice data, the collecting unit takes appropriate measures to protect the privacy of the subject. For example, the collecting unit encrypts and saves the voice data, and restricts access rights. The collection unit collects voice data with the subject's consent and clearly explains the purpose of collection and how it will be used. Furthermore, the collection unit can use noise reduction technology and echo cancellation technology to ensure the quality of the voice data. This allows the collection unit to collect the subject's everyday conversations as high-quality voice data, providing a foundation for the analysis unit to perform accurate analysis.

[0031] The analysis unit uses the generation AI to analyze the voice data collected by the collection unit and extracts specific words and phrases. The analysis is performed, for example, using voice recognition technology or natural language processing technology, but is not limited to such examples. For example, the analysis unit converts the voice data into text data using voice recognition technology and extracts specific words and phrases. The analysis unit can also analyze the text data using natural language processing technology and extract specific words and phrases. Furthermore, the analysis unit can extract specific words and phrases from the voice data using the generation AI. For example, the analysis unit converts the voice data into text data using voice recognition technology and performs frequency analysis to extract specific words and phrases. The natural language processing technology performs semantic analysis of the text data and extracts specific words and phrases. The generation AI directly analyzes the voice data and extracts specific words and phrases. The analysis unit can combine these technologies to perform more accurate analysis. For example, the text data converted by the voice recognition technology is analyzed by natural language processing technology and supplemented by the generation AI, thereby improving the accuracy of extraction of specific words and phrases. In addition, the analysis unit can also analyze the context and emotions of the subject's conversation. For example, it can analyze the emotions and context when the subject talks about a specific symptom, and use that information to help create a list of illnesses. This allows the analysis unit to analyze the collected voice data from multiple angles and get a more accurate understanding of the subject's health condition.

[0032] The list-up unit lists disease names based on the words and phrases extracted by the analysis unit. The list-up is performed, for example, by checking against an existing medical database, but is not limited to such an example. For example, the list-up unit checks the words and phrases extracted by the analysis unit against an existing medical database to list disease names that match the symptoms of the subject. The list-up unit can also use a generation AI to list disease names based on the words and phrases extracted by the analysis unit. Furthermore, the list-up unit can also use a machine learning model to list disease names based on the words and phrases extracted by the analysis unit. For example, the list-up unit uses an existing medical database to list disease names that match the words and phrases extracted by the analysis unit. The generation AI checks the voice data against the medical database to list disease names that match the symptoms of the subject. The machine learning model learns past case data and lists disease names based on the words and phrases extracted by the analysis unit. The list-up unit can combine these technologies to perform a more accurate disease name list. For example, by combining an existing medical database with generative AI and machine learning models, the system can list the disease names that best match the subject's symptoms. The list-up unit can also evaluate the reliability of the listed disease names and prioritize the display of the most reliable disease names. This allows the list-up unit to list the disease names that best match the subject's symptoms with a high degree of accuracy, supporting doctors' diagnoses.

[0033] The output unit outputs the elements and the contribution rate of the elements that became the judgment criteria for the disease names listed by the list unit. The output is performed, for example, in a text format or a graph format, but is not limited to such examples. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in a text format. The output unit can also output the contribution rate of the words and phrases that became the judgment criteria for the listed disease names in a graph format. Furthermore, the output unit can output the contribution rate of the words and phrases that became the judgment criteria for the listed disease names in a text format. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in a text format, and displays the contribution rate in a graph format. The output in the text format displays the words and phrases that became the judgment criteria in a list format. The output in the graph format displays the contribution rate in a bar graph or a pie chart. The output unit can provide information that is easier to understand by combining these output formats. For example, the judgment criteria and the contribution rate of the listed disease names are visually displayed by using both the text format and the graph format. The output unit can also customize the output contents. For example, detailed information on specific disease names and diagnosis criteria can be added and displayed in response to a doctor's request. Furthermore, the output unit can link the output contents with electronic medical record systems and other medical systems. This allows the output unit to display the listed disease names and their diagnosis criteria in an easy-to-understand manner, supporting the doctor's diagnosis.

[0034] The collection unit can collect the subject's daily conversation as voice data. Daily conversation includes, for example, conversation at home, conversation at work, conversation with friends, etc., but is not limited to such examples. For example, the collection unit records the content that the subject speaks at home and saves it as voice data. The collection unit can also record the content that the subject speaks at work and save it as voice data. Furthermore, the collection unit can also record the content that the subject speaks with friends and save it as voice data. For example, the collection unit records the content that the subject speaks at home with a high-sensitivity microphone and saves it as voice data. For conversation at work, the content of a specific meeting or meeting is recorded and saved as voice data. For conversation with friends, conversations while out are recorded and saved as voice data. In this way, natural conversation content can be obtained by collecting daily conversation. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect the subject's daily conversation using an AI model.

[0035] The analysis unit can extract specific keywords and phrases from the voice data. The analysis unit can convert the voice data into text data using, for example, voice recognition technology, and extract specific keywords and phrases. The analysis unit can also analyze the text data using natural language processing technology and extract specific keywords and phrases. Furthermore, the analysis unit can extract specific keywords and phrases from the voice data using a generation AI. For example, the analysis unit converts the voice data into text data using voice recognition technology, and performs frequency analysis to extract specific keywords and phrases. The natural language processing technology performs semantic analysis of the text data and extracts specific keywords and phrases. The generation AI directly analyzes the voice data and extracts specific keywords and phrases. As a result, by extracting specific keywords and phrases, it is possible to clarify the criteria for determining the name of a disease. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the voice data using an AI model and extract specific keywords and phrases.

[0036] The list-up unit can list disease names based on the extracted keywords and phrases. For example, the list-up unit checks against an existing medical database to list disease names that match the extracted keywords and phrases. The list-up unit can also use a generation AI to list disease names based on the extracted keywords and phrases. Furthermore, the list-up unit can also use a machine learning model to list disease names based on the extracted keywords and phrases. For example, the list-up unit uses an existing medical database to list disease names that match the extracted keywords and phrases. The generation AI checks the voice data against the medical database to list disease names that match the symptoms of the subject. The machine learning model learns past case data and lists disease names based on the extracted keywords and phrases. This makes it possible to support the doctor's diagnosis by listing disease names. A part or all of the above-mentioned processing in the list-up unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the list-up unit can input voice data to the generation AI and cause the generation AI to execute a list of disease names.

[0037] The output unit can output the elements that became the judgment criteria and their contribution rates for the listed disease names. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in text format. The output unit can also output the contribution rates of the words and phrases that became the judgment criteria for the listed disease names in graph format. The output unit can also output the contribution rates of the words and phrases that became the judgment criteria for the listed disease names in text format. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in text format and displays the contribution rates in graph format. The text format output displays the words and phrases that became the judgment criteria in list format. The graph format output displays the contribution rates in bar graphs and pie charts. This makes it possible to support the doctor's diagnosis by outputting the elements that became the judgment criteria and the contribution rates of the elements. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can output the listed disease names and their judgment criteria using an AI model.

[0038] The output unit can provide the doctor with the listed disease names and their judgment criteria. The output unit, for example, provides the doctor with the listed disease names and their judgment criteria in a text format. The output unit can also provide the doctor with the listed disease names and their judgment criteria in a graph format. The output unit can also provide the doctor with the listed disease names and their judgment criteria in a text format. For example, the output unit provides the doctor with the listed disease names and their judgment criteria in a text format, and displays the contribution rate in a graph format. The text format displays the listed disease names and their judgment criteria in a list format. The graph format displays the contribution rate in a bar graph or a pie chart. This improves the accuracy of diagnosis by providing the doctor with the listed disease names and their judgment criteria. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can provide the doctor with the listed disease names and their judgment criteria using an AI model.

[0039] The collection unit can analyze the subject's past conversation history and select an appropriate collection method. The collection unit, for example, analyzes a time period in which the subject spoke a lot in the past and collects conversations during that time period. In addition, if the subject tends to speak a lot at a specific location, the collection unit can also prioritize collection at that location. In addition, if the subject provides a lot of information when talking to a specific person, the collection unit can also prioritize collection of conversations with that person. For example, the collection unit analyzes a time period in which the subject spoke a lot in the past and collects conversations during that time period. If the subject tends to speak a lot at a specific location, collection at that location is prioritized. If the subject provides a lot of information when talking to a specific person, conversations with that person are prioritized. In this way, the optimal collection method can be selected by analyzing the past conversation history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the subject's past conversation history using an AI model and select an appropriate collection method.

[0040] When collecting conversations, the collection unit can perform filtering based on the subject's current living situation and field of interest. For example, if the subject is interested in health, the collection unit preferentially collects conversations related to health. In addition, if the subject talks a lot about work, the collection unit can also collect mainly on that topic. In addition, if the subject talks a lot about home, the collection unit can also collect the contents of the home with a focus. For example, if the subject is interested in health, the collection unit preferentially collects conversations related to health. If the subject talks a lot about work, the collection unit collects mainly on that topic. If the subject talks a lot about home, the collection unit collects the contents of the home with a focus. In this way, by filtering based on the living situation and field of interest, highly relevant conversations can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the living situation and field of interest of the subject using an AI model and perform filtering.

[0041] When collecting conversations, the collection unit can preferentially collect conversations with high relevance based on the geographical location information of the subject. For example, when the subject is in a hospital, the collection unit preferentially collects conversations related to health. In addition, when the subject is at work, the collection unit can also preferentially collect conversations related to work. Furthermore, when the subject is at home, the collection unit can also preferentially collect conversations related to home. For example, when the subject is in a hospital, the collection unit preferentially collects conversations related to health. When the subject is at work, the collection unit preferentially collects conversations related to work. When the subject is at home, the collection unit preferentially collects conversations related to home. In this way, by taking the geographical location information into consideration, highly relevant conversations can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the geographical location information of the subject using an AI model and preferentially collect conversations with high relevance.

[0042] The collection unit can analyze the social media activity of the subject at the time of collecting the conversation, and collect related conversation. For example, when the subject posts a lot on health on social media, the collection unit collects conversation related to the content. In addition, when the subject posts a lot on work, the collection unit can also collect conversation related to the content. In addition, when the subject posts a lot on home, the collection unit can also collect conversation related to the content. For example, when the subject posts a lot on health on social media, the collection unit collects conversation related to the content. When the subject posts a lot on work, the collection unit collects conversation related to the content. When the subject posts a lot on home, the collection unit collects conversation related to the content. In this way, by analyzing the social media activity, related conversation can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the social media activity of the subject using an AI model and collect related conversation.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of a specific keyword or phrase during analysis. For example, when the word "painful" appears frequently, the analysis unit performs a detailed analysis related to the word. In addition, when the word "tired" appears frequently, the analysis unit can also perform a detailed analysis related to the word. In addition, when the word "can't sleep" appears frequently, the analysis unit can also perform a detailed analysis related to the word. For example, when the word "painful" appears frequently, the analysis unit performs a detailed analysis related to the word. When the word "tired" appears frequently, the analysis unit performs a detailed analysis related to the word. When the word "can't sleep" appears frequently, the analysis unit performs a detailed analysis related to the word. This allows the level of detail of the analysis to be adjusted based on the importance, thereby providing a more accurate analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the importance of a specific keyword or phrase using an AI model and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms based on the category of the conversation during analysis. For example, in the case of a conversation about health, the analysis unit applies a health-related analysis algorithm. In addition, in the case of a conversation about work, the analysis unit can also apply a work-related analysis algorithm. In addition, in the case of a conversation about home, the analysis unit can also apply a home-related analysis algorithm. For example, in the case of a conversation about health, the analysis unit applies a health-related analysis algorithm. In the case of a conversation about work, the analysis unit applies a work-related analysis algorithm. In the case of a conversation about home, the analysis unit applies a home-related analysis algorithm. In this way, by applying different analysis algorithms according to the category, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can classify the category of the conversation using an AI model and apply different analysis algorithms.

[0045] The analysis unit can determine the priority of the analysis according to the time of collection of the conversation during the analysis. For example, the analysis unit preferentially analyzes the conversation collected recently. The analysis unit can also preferentially analyze the conversation collected in a specific period. Furthermore, the analysis unit can also preferentially analyze important conversations based on the past conversation history. For example, the analysis unit preferentially analyzes the conversation collected recently. The analysis unit preferentially analyzes the conversation collected in a specific period. The analysis unit preferentially analyzes important conversations based on the past conversation history. In this way, by determining the priority of the analysis based on the collection time, it is possible to preferentially analyze important conversations. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the collection time of the conversation using an AI model and determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the conversation during analysis. For example, the analysis unit preferentially analyzes conversations related to health. The analysis unit can also preferentially analyze conversations related to work. Furthermore, the analysis unit can also preferentially analyze conversations related to home. For example, the analysis unit preferentially analyzes conversations related to health. The analysis unit preferentially analyzes conversations related to work. The analysis unit preferentially analyzes conversations related to home. By adjusting the order of analysis based on the relevance, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can evaluate the relevance of the conversation using an AI model and adjust the order of analysis.

[0047] The list-up unit can improve the accuracy of the list-up by considering the interrelationship between specific keywords and phrases when listing. For example, when the words "pain" and "tired" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. In addition, when the words "can't sleep" and "anxiety" appear frequently, the list-up unit can also perform the list-up by considering the interrelationship between them. In addition, when the words "no appetite" and "lost weight" appear frequently, the list-up unit can also perform the list-up by considering the interrelationship between them. For example, when the words "pain" and "tired" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. When the words "can't sleep" and "anxiety" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. When the words "no appetite" and "lost weight" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. As a result, the accuracy of the list-up is improved by considering the interrelationship. A part or all of the above-mentioned processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the listing unit can use AI models to evaluate the interrelationships between certain keywords or phrases to improve the accuracy of the listings.

[0048] The list-up unit can perform the listing based on the attribute information of the subject at the time of listing. For example, the list-up unit lists the disease names related to the age of the subject, taking into consideration the age of the subject. In addition, the list-up unit can also list the disease names related to the sex of the subject, taking into consideration the sex of the subject. Furthermore, the list-up unit can also list the disease names related to the occupation of the subject, taking into consideration the occupation of the subject. For example, the list-up unit lists the disease names related to the age of the subject. Taking into consideration the sex of the subject, the list-up unit lists the disease names related to the sex. Taking into consideration the occupation of the subject, the list-up unit lists the disease names related to the occupation. In this way, by taking into consideration the attribute information, it is possible to provide a more appropriate listing result. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can analyze the attribute information of the subject using an AI model and perform the listing.

[0049] The list-up unit can perform the listing taking into consideration the geographical distribution of the conversation when listing. For example, when the subject speaks a lot in a specific area, the list-up unit lists the disease names related to the area. In addition, when the subject speaks a lot in a specific place, the list-up unit can also list the disease names related to the place. In addition, when the subject speaks a lot in a specific country, the list-up unit can also list the disease names related to the country. For example, when the subject speaks a lot in a specific area, the list-up unit lists the disease names related to the area. When the subject speaks a lot in a specific place, the list-up unit lists the disease names related to the place. When the subject speaks a lot in a specific country, the list-up unit lists the disease names related to the country. In this way, by taking the geographical distribution into consideration, a more appropriate listing result can be provided. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can analyze the geographical distribution of the conversation using an AI model and perform the listing.

[0050] The list-up unit can improve the accuracy of the list-up by referring to related literature when listing. The list-up unit, for example, refers to medical literature and lists based on the latest research results. The list-up unit can also refer to related academic papers and list based on highly reliable information. Furthermore, the list-up unit can also refer to a medical database and list based on past cases. For example, the list-up unit refers to medical literature and lists based on the latest research results. The list-up unit refers to related academic papers and lists based on highly reliable information. The list-up unit refers to a medical database and lists based on past cases. As a result, the accuracy of the list-up is improved by referring to related literature. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can improve the accuracy of the list-up by referring to related literature using an AI model.

[0051] The output unit can display the contribution rate of a specific keyword or phrase in detail at the time of output. For example, if the word "tired" contributes 50% to the determination of the disease name, the output unit displays the information in detail. In addition, the output unit can also display the information in detail if the word "painful" contributes 30% to the determination of the disease name. In addition, the output unit can display the information in detail if the word "unable to sleep" contributes 20% to the determination of the disease name. For example, the output unit displays the information in detail if the word "tired" contributes 50% to the determination of the disease name. In addition, the output unit displays the information in detail if the word "painful" contributes 30% to the determination of the disease name. In addition, the output unit displays the information in detail if the word "unable to sleep" contributes 20% to the determination of the disease name. This makes it possible to support the doctor's diagnosis by displaying the contribution rate in detail. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can display the contribution rate of a specific keyword or phrase in detail using an AI model.

[0052] The output unit can display the listed disease names and related information together at the time of output. For example, the output unit displays related symptoms and treatment methods together with the listed disease names. In addition, the output unit can also display information on related medical institutions together with the listed disease names. Furthermore, the output unit can also display information on related drugs together with the listed disease names. For example, the output unit displays related symptoms and treatment methods together with the listed disease names. In addition, information on related medical institutions together with the listed disease names. In addition, information on related drugs together with the listed disease names. This makes it possible to support the doctor's diagnosis by displaying related information together. Part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can display the listed disease names and their related information together using an AI model.

[0053] The output unit can adjust the order of output according to the time of collection of the conversation at the time of output. The output unit, for example, preferentially displays output results based on recently collected conversations. The output unit can also preferentially display output results based on conversations collected in a specific period. Furthermore, the output unit can also preferentially display important output results based on past conversation history. For example, the output unit preferentially displays output results based on recently collected conversations. The output unit preferentially displays output results based on conversations collected in a specific period. The output unit preferentially displays important output results based on past conversation history. This allows the output order to be adjusted based on the collection time, thereby providing a more appropriate output result. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can analyze the collection time of the conversation using an AI model and adjust the order of output.

[0054] The output unit can improve the accuracy of the output by referring to the related medical database at the time of output. The output unit, for example, refers to the medical database and provides an output result based on the latest research results. The output unit can also refer to related academic papers and provide an output result based on highly reliable information. Furthermore, the output unit can also refer to the medical database and provide an output result based on past cases. For example, the output unit refers to the medical database and provides an output result based on the latest research results. The output unit refers to related academic papers and provides an output result based on highly reliable information. The output unit refers to the medical database and provides an output result based on past cases. As a result, the accuracy of the output is improved by referring to the medical database. Part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can improve the accuracy of the output by referring to the related medical database using an AI model.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When collecting the conversation of the subject, the collection unit can adjust the collection timing in consideration of the subject's life rhythm. For example, if the subject lives a morning-type lifestyle, the conversation can be collected in the morning hours. Also, if the subject lives a night-type lifestyle, the conversation can be collected in the evening hours. Furthermore, if the subject tends to talk a lot on weekends, the conversation can be collected on weekends. By adjusting the collection timing based on the life rhythm, more natural conversation content can be obtained. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the life rhythm of the subject using an AI model and adjust the collection timing.

[0057] When collecting the conversation of the subject, the collection unit can monitor the health condition of the subject and adjust the collection method according to the health condition. For example, if the subject is in poor health, collection is refrained. Also, if the subject is in a healthy state, conversation can be actively collected. Furthermore, if the subject has a specific illness, conversations related to the illness can be preferentially collected. In this way, by adjusting the collection method based on the health condition, more appropriate conversation content can be obtained. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can monitor the health condition of the subject using an AI model and adjust the collection method.

[0058] When analyzing the voice data, the analysis unit can adjust the analysis algorithm taking into account the speaking speed and volume of the subject. For example, if the subject speaks quickly, an analysis algorithm corresponding to the speaking speed can be applied. Also, if the subject speaks slowly, an analysis algorithm corresponding to the speaking speed can be applied. Furthermore, if the subject speaks loudly, an analysis algorithm corresponding to the volume can be applied. In this way, by adjusting the analysis algorithm based on the speaking speed and volume, more accurate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can evaluate the speaking speed and volume of the subject using an AI model and adjust the analysis algorithm.

[0059] When listing disease names based on the extracted keywords and phrases, the list-up unit can improve the accuracy of the list by taking into account the subject's past medical history. For example, if the subject has had a specific disease in the past, disease names related to that disease are preferentially listed. In addition, if the subject has had multiple diseases in the past, disease names related to those diseases can also be listed. Furthermore, if the subject has frequently complained of a specific symptom in the past, disease names related to that symptom can also be listed. This improves the accuracy of the list by taking into account the past medical history. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can analyze the subject's past medical history using an AI model to improve the accuracy of the list.

[0060] When outputting the factors that were used as the criteria for the listed disease names and their contribution rates, the output unit can adjust the output format according to the subject's level of understanding. For example, if the subject has medical knowledge, detailed output using technical terms is performed. Also, if the subject does not have medical knowledge, concise and easy-to-understand output can be performed. Furthermore, if the subject prefers visual information, output using graphs and diagrams can be performed. This makes it possible to provide more appropriate information by adjusting the output format based on the level of understanding. Part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can evaluate the subject's level of understanding using an AI model and adjust the output format.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects the subject's everyday conversations as voice data. For example, this includes conversations at home, at work, and with friends. The collection unit records these conversations using a highly sensitive microphone or recording device and saves them as voice data. Step 2: The analysis unit analyzes the voice data collected by the collection unit and extracts specific words and phrases. The analysis is performed using voice recognition technology, natural language processing technology, and generative AI. For example, the voice data is converted into text data, and frequency analysis and semantic analysis are performed to extract specific words and phrases. Step 3: The list-up unit lists disease names based on the words and phrases extracted by the analysis unit. The list-up is done by checking against existing medical databases or using generative AI or machine learning models. For example, the extracted words and phrases are checked against a medical database to list disease names that match the subject's symptoms. Step 4: The output unit outputs the elements that were the judgment criteria and the contribution rates of the elements to the disease names listed by the listing unit. The output is in text format or graph format. For example, the words and phrases that were the judgment criteria are output in text format, and their contribution rates are displayed in graph format.

[0063] (Example 2) The disease name listing system according to the embodiment of the present invention is a system that lists possible disease names by capturing and analyzing the subject's everyday conversation as voice data. This disease name listing system collects the subject's conversation as voice data, analyzes it, and lists possible disease names. At this time, the elements (words, sentences, etc.) that were used as the judgment criteria and the contribution rate of the elements are also output. This can be used as support data for doctors when they actually visit the doctor. In addition, if the output data of this service is used by doctors, a usage fee can be charged from patients and hospitals. For example, the subject's conversation is collected as voice data. At this time, the contents of the subject's daily conversation are recorded and saved as voice data. For example, the contents of the subject's conversations with family and friends, conversations at work, etc. can be collected. Next, the collected voice data is analyzed. A generation AI is used for the analysis, and specific words and phrases are extracted from the voice data. For example, if words such as "tired," "pain," and "can't sleep" appear frequently, these words become the judgment criteria for disease names. Based on the analysis results, possible disease names are listed. The generation AI compares the collected voice data with existing medical databases and lists the names of diseases that match the subject's symptoms. For example, if words such as "tired," "pain," and "can't sleep" appear frequently, the generation AI will list the names of diseases related to those symptoms. In addition, for the listed disease names, the elements (words, sentences, etc.) that were used as judgment criteria and the contribution rate of the elements are also output. This allows the doctor to understand to what extent each element contributed to the judgment of the disease name. For example, if the word "tired" contributes 50% to the judgment of the disease name and the word "pain" contributes 30%, this information can be provided to the doctor. This system can be used as support data for the doctor when actually visiting the doctor. The doctor can make a diagnosis while referring to the listed disease names and their judgment criteria. This improves the accuracy of the diagnosis and enables appropriate treatment for the patient. In addition, if the output data of this service is used by the doctor, a usage fee can be charged from the patient and the hospital. For example, using the data of this service during a diagnosis improves the accuracy of the diagnosis and increases the satisfaction of the patient.As a result, it is expected that hospitals and patients will receive usage fees. This will enable the disease name listing system to analyze the subjects' everyday conversations and support doctors in making diagnoses.

[0064] The disease name listing system according to the embodiment includes a collection unit, an analysis unit, a list unit, and an output unit. The collection unit collects the subject's everyday conversation as voice data. The subject's everyday conversation includes, for example, conversation at home, conversation at work, conversation with friends, etc., but is not limited to such examples. The collection unit, for example, records the content that the subject speaks at home and saves it as voice data. The collection unit can also record the content that the subject speaks at work and save it as voice data. Furthermore, the collection unit can also record the content that the subject speaks with friends and save it as voice data. For example, the collection unit records the content that the subject speaks at home with a high-sensitivity microphone and saves it as voice data. For the conversation at work, the content of a specific meeting or meeting is recorded and saved as voice data. For the conversation with friends, the conversation on the go is recorded and saved as voice data. The analysis unit uses a generation AI to analyze the voice data collected by the collection unit and extract specific words and phrases. The analysis is performed, for example, using voice recognition technology or natural language processing technology, but is not limited to such examples. For example, the analysis unit converts voice data into text data using voice recognition technology and extracts specific words and phrases. The analysis unit can also analyze text data using natural language processing technology and extract specific words and phrases. Furthermore, the analysis unit can extract specific words and phrases from voice data using generation AI. For example, the analysis unit converts voice data into text data using voice recognition technology and performs frequency analysis to extract specific words and phrases. The natural language processing technology performs semantic analysis of the text data and extracts specific words and phrases. The generation AI directly analyzes the voice data and extracts specific words and phrases. The list-up unit lists the disease names based on the words and phrases extracted by the analysis unit. The list-up is performed, for example, by checking against an existing medical database, but is not limited to such examples. For example, the list-up unit checks the words and phrases extracted by the analysis unit against an existing medical database and lists the disease names that match the symptoms of the subject. The listing unit can also use a generative AI to list disease names based on the words and phrases extracted by the analysis unit.Furthermore, the list-up unit can use a machine learning model to list disease names based on the words and phrases extracted by the analysis unit. For example, the list-up unit uses an existing medical database to list disease names that match the words and phrases extracted by the analysis unit. The generation AI compares the voice data with the medical database and lists disease names that match the symptoms of the subject. The machine learning model learns past case data and lists disease names based on the words and phrases extracted by the analysis unit. The output unit outputs the elements that served as the judgment criteria for the disease names listed by the list-up unit and the contribution rate of the elements. The output is performed, for example, in a text format or a graph format, but is not limited to such examples. For example, the output unit outputs the words and phrases that served as the judgment criteria for the listed disease names in a text format. The output unit can also output the contribution rate of the words and phrases that served as the judgment criteria for the listed disease names in a graph format. Furthermore, the output unit can also output the contribution rate of the words and phrases that served as the judgment criteria for the listed disease names in a text format. For example, the output unit outputs the words and phrases that were the criteria for the listed disease names in text format, and displays the contribution rate in graph format. The text format output displays the words and phrases that were the criteria in list format. The graph format output displays the contribution rate in a bar graph or pie chart. In this way, the disease name listing system according to the embodiment can analyze the subject's everyday conversation and support the doctor's diagnosis. Some or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can output the listed disease names and their judgment criteria using an AI model.

[0065] The collecting unit collects the subject's everyday conversation as voice data. The subject's everyday conversation includes, for example, conversation at home, conversation at work, conversation with friends, etc., but is not limited to such examples. For example, the collecting unit records the content that the subject speaks at home and saves it as voice data. The collecting unit can also record the content that the subject speaks at work and save it as voice data. Furthermore, the collecting unit can also record the content that the subject speaks with friends and save it as voice data. For example, the collecting unit records the content that the subject speaks at home with a high-sensitivity microphone and saves it as voice data. For conversation at work, the content of a specific meeting or meeting is recorded and saved as voice data. For conversation with friends, conversations while out are recorded and saved as voice data. The collecting unit manages these voice data in a centralized manner and makes them accessible to the analyzing unit. When collecting voice data, the collecting unit takes appropriate measures to protect the privacy of the subject. For example, the collecting unit encrypts and saves the voice data, and restricts access rights. The collection unit collects voice data with the subject's consent and clearly explains the purpose of collection and how it will be used. Furthermore, the collection unit can use noise reduction technology and echo cancellation technology to ensure the quality of the voice data. This allows the collection unit to collect the subject's everyday conversations as high-quality voice data, providing a foundation for the analysis unit to perform accurate analysis.

[0066] The analysis unit uses the generation AI to analyze the voice data collected by the collection unit and extracts specific words and phrases. The analysis is performed, for example, using voice recognition technology or natural language processing technology, but is not limited to such examples. For example, the analysis unit converts the voice data into text data using voice recognition technology and extracts specific words and phrases. The analysis unit can also analyze the text data using natural language processing technology and extract specific words and phrases. Furthermore, the analysis unit can extract specific words and phrases from the voice data using the generation AI. For example, the analysis unit converts the voice data into text data using voice recognition technology and performs frequency analysis to extract specific words and phrases. The natural language processing technology performs semantic analysis of the text data and extracts specific words and phrases. The generation AI directly analyzes the voice data and extracts specific words and phrases. The analysis unit can combine these technologies to perform more accurate analysis. For example, the text data converted by the voice recognition technology is analyzed by natural language processing technology and supplemented by the generation AI, thereby improving the accuracy of extraction of specific words and phrases. In addition, the analysis unit can also analyze the context and emotions of the subject's conversation. For example, it can analyze the emotions and context when the subject talks about a specific symptom, and use that information to help create a list of illnesses. This allows the analysis unit to analyze the collected voice data from multiple angles and get a more accurate understanding of the subject's health condition.

[0067] The list-up unit lists disease names based on the words and phrases extracted by the analysis unit. The list-up is performed, for example, by checking against an existing medical database, but is not limited to such an example. For example, the list-up unit checks the words and phrases extracted by the analysis unit against an existing medical database to list disease names that match the symptoms of the subject. The list-up unit can also use a generation AI to list disease names based on the words and phrases extracted by the analysis unit. Furthermore, the list-up unit can also use a machine learning model to list disease names based on the words and phrases extracted by the analysis unit. For example, the list-up unit uses an existing medical database to list disease names that match the words and phrases extracted by the analysis unit. The generation AI checks the voice data against the medical database to list disease names that match the symptoms of the subject. The machine learning model learns past case data and lists disease names based on the words and phrases extracted by the analysis unit. The list-up unit can combine these technologies to perform a more accurate disease name list. For example, by combining an existing medical database with generative AI and machine learning models, the system can list the disease names that best match the subject's symptoms. The list-up unit can also evaluate the reliability of the listed disease names and prioritize the display of the most reliable disease names. This allows the list-up unit to list the disease names that best match the subject's symptoms with a high degree of accuracy, supporting doctors' diagnoses.

[0068] The output unit outputs the elements and the contribution rate of the elements that became the judgment criteria for the disease names listed by the list unit. The output is performed, for example, in a text format or a graph format, but is not limited to such examples. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in a text format. The output unit can also output the contribution rate of the words and phrases that became the judgment criteria for the listed disease names in a graph format. Furthermore, the output unit can output the contribution rate of the words and phrases that became the judgment criteria for the listed disease names in a text format. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in a text format, and displays the contribution rate in a graph format. The output in the text format displays the words and phrases that became the judgment criteria in a list format. The output in the graph format displays the contribution rate in a bar graph or a pie chart. The output unit can provide information that is easier to understand by combining these output formats. For example, the judgment criteria and the contribution rate of the listed disease names are visually displayed by using both the text format and the graph format. The output unit can also customize the output contents. For example, detailed information on specific disease names and diagnosis criteria can be added and displayed in response to a doctor's request. Furthermore, the output unit can link the output contents with electronic medical record systems and other medical systems. This allows the output unit to display the listed disease names and their diagnosis criteria in an easy-to-understand manner, supporting the doctor's diagnosis.

[0069] The collection unit can collect the subject's daily conversation as voice data. Daily conversation includes, for example, conversation at home, conversation at work, conversation with friends, etc., but is not limited to such examples. For example, the collection unit records the content that the subject speaks at home and saves it as voice data. The collection unit can also record the content that the subject speaks at work and save it as voice data. Furthermore, the collection unit can also record the content that the subject speaks with friends and save it as voice data. For example, the collection unit records the content that the subject speaks at home with a high-sensitivity microphone and saves it as voice data. For conversation at work, the content of a specific meeting or meeting is recorded and saved as voice data. For conversation with friends, conversations while out are recorded and saved as voice data. In this way, natural conversation content can be obtained by collecting daily conversation. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can collect the subject's daily conversation using an AI model.

[0070] The analysis unit can extract specific keywords and phrases from the voice data. The analysis unit can convert the voice data into text data using, for example, voice recognition technology, and extract specific keywords and phrases. The analysis unit can also analyze the text data using natural language processing technology and extract specific keywords and phrases. Furthermore, the analysis unit can extract specific keywords and phrases from the voice data using a generation AI. For example, the analysis unit converts the voice data into text data using voice recognition technology, and performs frequency analysis to extract specific keywords and phrases. The natural language processing technology performs semantic analysis of the text data and extracts specific keywords and phrases. The generation AI directly analyzes the voice data and extracts specific keywords and phrases. As a result, by extracting specific keywords and phrases, it is possible to clarify the criteria for determining the name of a disease. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the voice data using an AI model and extract specific keywords and phrases.

[0071] The list-up unit can list disease names based on the extracted keywords and phrases. For example, the list-up unit checks against an existing medical database to list disease names that match the extracted keywords and phrases. The list-up unit can also use a generation AI to list disease names based on the extracted keywords and phrases. Furthermore, the list-up unit can also use a machine learning model to list disease names based on the extracted keywords and phrases. For example, the list-up unit uses an existing medical database to list disease names that match the extracted keywords and phrases. The generation AI checks the voice data against the medical database to list disease names that match the symptoms of the subject. The machine learning model learns past case data and lists disease names based on the extracted keywords and phrases. This makes it possible to support the doctor's diagnosis by listing disease names. A part or all of the above-mentioned processing in the list-up unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the list-up unit can input voice data to the generation AI and cause the generation AI to execute a list of disease names.

[0072] The output unit can output the elements that became the judgment criteria and their contribution rates for the listed disease names. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in text format. The output unit can also output the contribution rates of the words and phrases that became the judgment criteria for the listed disease names in graph format. The output unit can also output the contribution rates of the words and phrases that became the judgment criteria for the listed disease names in text format. For example, the output unit outputs the words and phrases that became the judgment criteria for the listed disease names in text format and displays the contribution rates in graph format. The text format output displays the words and phrases that became the judgment criteria in list format. The graph format output displays the contribution rates in bar graphs and pie charts. This makes it possible to support the doctor's diagnosis by outputting the elements that became the judgment criteria and the contribution rates of the elements. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can output the listed disease names and their judgment criteria using an AI model.

[0073] The output unit can provide the doctor with the listed disease names and their judgment criteria. The output unit, for example, provides the doctor with the listed disease names and their judgment criteria in a text format. The output unit can also provide the doctor with the listed disease names and their judgment criteria in a graph format. The output unit can also provide the doctor with the listed disease names and their judgment criteria in a text format. For example, the output unit provides the doctor with the listed disease names and their judgment criteria in a text format, and displays the contribution rate in a graph format. The text format displays the listed disease names and their judgment criteria in a list format. The graph format displays the contribution rate in a bar graph or a pie chart. This improves the accuracy of diagnosis by providing the doctor with the listed disease names and their judgment criteria. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can provide the doctor with the listed disease names and their judgment criteria using an AI model.

[0074] The collection unit can estimate the emotion of the subject and adjust the timing of collection of the conversation based on the estimated emotion. The collection unit, for example, collects the conversation when the subject is relaxed, and obtains natural conversation content. The collection unit can also refrain from collection when the subject is stressed, and resume collection when the emotion is stable. The collection unit can also collect the conversation when the subject is excited, and capture the change in emotion. For example, the collection unit collects the conversation when the subject is relaxed, and obtains natural conversation content. The collection unit refrains from collection when the subject is stressed, and resumes collection when the emotion is stable. The conversation is collected when the subject is excited, and captures the change in emotion. In this way, natural conversation content can be obtained by adjusting the collection timing based on the emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the collection unit can estimate the subject's emotions using an AI model and adjust the timing of collection.

[0075] The collection unit can analyze the subject's past conversation history and select an appropriate collection method. The collection unit, for example, analyzes a time period in which the subject spoke a lot in the past and collects conversations during that time period. In addition, if the subject tends to speak a lot at a specific location, the collection unit can also prioritize collection at that location. In addition, if the subject provides a lot of information when talking to a specific person, the collection unit can also prioritize collection of conversations with that person. For example, the collection unit analyzes a time period in which the subject spoke a lot in the past and collects conversations during that time period. If the subject tends to speak a lot at a specific location, collection at that location is prioritized. If the subject provides a lot of information when talking to a specific person, conversations with that person are prioritized. In this way, the optimal collection method can be selected by analyzing the past conversation history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the subject's past conversation history using an AI model and select an appropriate collection method.

[0076] When collecting conversations, the collection unit can perform filtering based on the subject's current living situation and field of interest. For example, if the subject is interested in health, the collection unit preferentially collects conversations related to health. In addition, if the subject talks a lot about work, the collection unit can also collect mainly on that topic. In addition, if the subject talks a lot about home, the collection unit can also collect the contents of the home with a focus. For example, if the subject is interested in health, the collection unit preferentially collects conversations related to health. If the subject talks a lot about work, the collection unit collects mainly on that topic. If the subject talks a lot about home, the collection unit collects the contents of the home with a focus. In this way, by filtering based on the living situation and field of interest, highly relevant conversations can be collected. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze the living situation and field of interest of the subject using an AI model and perform filtering.

[0077] The collection unit can estimate the emotion of the subject and determine the priority of the conversation to be collected based on the estimated emotion. For example, when the subject is sad, the collection unit preferentially collects a conversation related to the emotion. In addition, when the subject is happy, the collection unit can also preferentially collect a conversation related to the emotion. In addition, when the subject is anxious, the collection unit can also preferentially collect a conversation related to the emotion. For example, when the subject is sad, the collection unit preferentially collects a conversation related to the emotion. When the subject is happy, the collection unit preferentially collects a conversation related to the emotion. When the subject is anxious, the collection unit preferentially collects a conversation related to the emotion. In this way, by determining the priority of the conversation to be collected based on the emotion, it is possible to preferentially collect important conversation. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can use an AI model to estimate the subject's emotions and determine the priorities of conversations to collect.

[0078] When collecting conversations, the collection unit can preferentially collect conversations with high relevance based on the geographical location information of the subject. For example, when the subject is in a hospital, the collection unit preferentially collects conversations related to health. In addition, when the subject is at work, the collection unit can also preferentially collect conversations related to work. Furthermore, when the subject is at home, the collection unit can also preferentially collect conversations related to home. For example, when the subject is in a hospital, the collection unit preferentially collects conversations related to health. When the subject is at work, the collection unit preferentially collects conversations related to work. When the subject is at home, the collection unit preferentially collects conversations related to home. In this way, by taking the geographical location information into consideration, highly relevant conversations can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the geographical location information of the subject using an AI model and preferentially collect conversations with high relevance.

[0079] The collection unit can analyze the social media activity of the subject at the time of collecting the conversation, and collect related conversation. For example, when the subject posts a lot on health on social media, the collection unit collects conversation related to the content. In addition, when the subject posts a lot on work, the collection unit can also collect conversation related to the content. In addition, when the subject posts a lot on home, the collection unit can also collect conversation related to the content. For example, when the subject posts a lot on health on social media, the collection unit collects conversation related to the content. When the subject posts a lot on work, the collection unit collects conversation related to the content. When the subject posts a lot on home, the collection unit collects conversation related to the content. In this way, by analyzing the social media activity, related conversation can be collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the social media activity of the subject using an AI model and collect related conversation.

[0080] The analysis unit can estimate the emotion of the subject and adjust the expression method of the analysis based on the estimated emotion. For example, when the subject is relaxed, the analysis unit provides a detailed analysis result. In addition, when the subject is stressed, the analysis unit can also provide a concise analysis result. In addition, when the subject is excited, the analysis unit can also provide a visually easy-to-understand analysis result. For example, when the subject is relaxed, the analysis unit provides a detailed analysis result. When the subject is stressed, the analysis unit provides a concise analysis result. When the subject is excited, the analysis unit provides a visually easy-to-understand analysis result. Thereby, by adjusting the expression method of the analysis based on the emotion, a more appropriate analysis result can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can estimate the emotion of the subject using an AI model and adjust the expression method of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of a specific keyword or phrase during analysis. For example, when the word "painful" appears frequently, the analysis unit performs a detailed analysis related to the word. In addition, when the word "tired" appears frequently, the analysis unit can also perform a detailed analysis related to the word. In addition, when the word "can't sleep" appears frequently, the analysis unit can also perform a detailed analysis related to the word. For example, when the word "painful" appears frequently, the analysis unit performs a detailed analysis related to the word. When the word "tired" appears frequently, the analysis unit performs a detailed analysis related to the word. When the word "can't sleep" appears frequently, the analysis unit performs a detailed analysis related to the word. This allows the level of detail of the analysis to be adjusted based on the importance, thereby providing a more accurate analysis result. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the importance of a specific keyword or phrase using an AI model and adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms based on the category of the conversation during analysis. For example, in the case of a conversation about health, the analysis unit applies a health-related analysis algorithm. In addition, in the case of a conversation about work, the analysis unit can also apply a work-related analysis algorithm. In addition, in the case of a conversation about home, the analysis unit can also apply a home-related analysis algorithm. For example, in the case of a conversation about health, the analysis unit applies a health-related analysis algorithm. In the case of a conversation about work, the analysis unit applies a work-related analysis algorithm. In the case of a conversation about home, the analysis unit applies a home-related analysis algorithm. In this way, by applying different analysis algorithms according to the category, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can classify the category of the conversation using an AI model and apply different analysis algorithms.

[0083] The analysis unit can estimate the emotion of the subject and adjust the length of the analysis based on the estimated emotion. For example, when the subject is relaxed, the analysis unit provides a detailed analysis result. In addition, when the subject is stressed, the analysis unit can also provide a concise analysis result. In addition, when the subject is excited, the analysis unit can also provide a visually easy-to-understand analysis result. For example, when the subject is relaxed, the analysis unit provides a detailed analysis result. When the subject is stressed, the analysis unit provides a concise analysis result. When the subject is excited, the analysis unit provides a visually easy-to-understand analysis result. This makes it possible to provide a more appropriate analysis result by adjusting the length of the analysis based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can estimate the emotion of the subject using an AI model and adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis according to the time of collection of the conversation during the analysis. For example, the analysis unit preferentially analyzes the conversation collected recently. The analysis unit can also preferentially analyze the conversation collected in a specific period. Furthermore, the analysis unit can also preferentially analyze important conversations based on the past conversation history. For example, the analysis unit preferentially analyzes the conversation collected recently. The analysis unit preferentially analyzes the conversation collected in a specific period. The analysis unit preferentially analyzes important conversations based on the past conversation history. In this way, by determining the priority of the analysis based on the collection time, it is possible to preferentially analyze important conversations. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the collection time of the conversation using an AI model and determine the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the conversation during analysis. For example, the analysis unit preferentially analyzes conversations related to health. The analysis unit can also preferentially analyze conversations related to work. Furthermore, the analysis unit can also preferentially analyze conversations related to home. For example, the analysis unit preferentially analyzes conversations related to health. The analysis unit preferentially analyzes conversations related to work. The analysis unit preferentially analyzes conversations related to home. By adjusting the order of analysis based on the relevance, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can evaluate the relevance of the conversation using an AI model and adjust the order of analysis.

[0086] The list-up unit can estimate the emotion of the subject and adjust the criteria for listing based on the estimated emotion. For example, when the subject is relaxed, the list-up unit provides a detailed listing result. In addition, when the subject is stressed, the list-up unit can also provide a concise listing result. In addition, when the subject is excited, the list-up unit can also provide a visually easy-to-understand listing result. For example, when the subject is relaxed, the list-up unit provides a detailed listing result. When the subject is stressed, the list-up unit provides a concise listing result. When the subject is excited, the list-up unit provides a visually easy-to-understand listing result. This allows the listing criteria to be adjusted based on the emotion, thereby providing a more appropriate listing result. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the list-up unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the listing unit can estimate the subject's emotions using an AI model and adjust the listing criteria.

[0087] The list-up unit can improve the accuracy of the list-up by considering the interrelationship between specific keywords and phrases when listing. For example, when the words "pain" and "tired" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. In addition, when the words "can't sleep" and "anxiety" appear frequently, the list-up unit can also perform the list-up by considering the interrelationship between them. In addition, when the words "no appetite" and "lost weight" appear frequently, the list-up unit can also perform the list-up by considering the interrelationship between them. For example, when the words "pain" and "tired" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. When the words "can't sleep" and "anxiety" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. When the words "no appetite" and "lost weight" appear frequently, the list-up unit performs the list-up by considering the interrelationship between them. As a result, the accuracy of the list-up is improved by considering the interrelationship. A part or all of the above-mentioned processing in the list-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the listing unit can use AI models to evaluate the interrelationships between certain keywords or phrases to improve the accuracy of the listings.

[0088] The list-up unit can perform the listing based on the attribute information of the subject at the time of listing. For example, the list-up unit lists the disease names related to the age of the subject, taking into consideration the age of the subject. In addition, the list-up unit can also list the disease names related to the sex of the subject, taking into consideration the sex of the subject. Furthermore, the list-up unit can also list the disease names related to the occupation of the subject, taking into consideration the occupation of the subject. For example, the list-up unit lists the disease names related to the age of the subject. Taking into consideration the sex of the subject, the list-up unit lists the disease names related to the sex. Taking into consideration the occupation of the subject, the list-up unit lists the disease names related to the occupation. In this way, by taking into consideration the attribute information, it is possible to provide a more appropriate listing result. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can analyze the attribute information of the subject using an AI model and perform the listing.

[0089] The list-up unit can estimate the emotion of the subject and adjust the order of displaying the list-up results based on the estimated emotion. For example, when the subject is relaxed, the list-up unit provides a detailed list-up result. In addition, when the subject is stressed, the list-up unit can also provide a concise list-up result. In addition, when the subject is excited, the list-up unit can also provide a visually easy-to-understand list-up result. For example, when the subject is relaxed, the list-up unit provides a detailed list-up result. When the subject is stressed, the list-up unit provides a concise list-up result. When the subject is excited, the list-up unit provides a visually easy-to-understand list-up result. This makes it possible to provide a more appropriate list-up result by adjusting the display order based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the list-up unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the listing unit can estimate the subject's emotions using an AI model and adjust the order in which the listing results are displayed.

[0090] The list-up unit can perform the listing taking into consideration the geographical distribution of the conversation when listing. For example, when the subject speaks a lot in a specific area, the list-up unit lists the disease names related to the area. In addition, when the subject speaks a lot in a specific place, the list-up unit can also list the disease names related to the place. In addition, when the subject speaks a lot in a specific country, the list-up unit can also list the disease names related to the country. For example, when the subject speaks a lot in a specific area, the list-up unit lists the disease names related to the area. When the subject speaks a lot in a specific place, the list-up unit lists the disease names related to the place. When the subject speaks a lot in a specific country, the list-up unit lists the disease names related to the country. In this way, by taking the geographical distribution into consideration, a more appropriate listing result can be provided. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can analyze the geographical distribution of the conversation using an AI model and perform the listing.

[0091] The list-up unit can improve the accuracy of the list-up by referring to related literature when listing. The list-up unit, for example, refers to medical literature and lists based on the latest research results. The list-up unit can also refer to related academic papers and list based on highly reliable information. Furthermore, the list-up unit can also refer to a medical database and list based on past cases. For example, the list-up unit refers to medical literature and lists based on the latest research results. The list-up unit refers to related academic papers and lists based on highly reliable information. The list-up unit refers to a medical database and lists based on past cases. As a result, the accuracy of the list-up is improved by referring to related literature. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can improve the accuracy of the list-up by referring to related literature using an AI model.

[0092] The output unit can estimate the emotion of the subject and adjust the display method of the output based on the estimated emotion. For example, when the subject is relaxed, the output unit provides a detailed output result. In addition, when the subject is stressed, the output unit can also provide a concise output result. In addition, when the subject is excited, the output unit can also provide a visually easy-to-understand output result. For example, when the subject is relaxed, the output unit provides a detailed output result. When the subject is stressed, the output unit provides a concise output result. When the subject is excited, the output unit provides a visually easy-to-understand output result. This makes it possible to provide a more appropriate output result by adjusting the display method based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the output unit can estimate the emotion of the subject using an AI model and adjust the display method of the output.

[0093] The output unit can display the contribution rate of a specific keyword or phrase in detail at the time of output. For example, if the word "tired" contributes 50% to the determination of the disease name, the output unit displays the information in detail. In addition, the output unit can also display the information in detail if the word "painful" contributes 30% to the determination of the disease name. In addition, the output unit can display the information in detail if the word "unable to sleep" contributes 20% to the determination of the disease name. For example, the output unit displays the information in detail if the word "tired" contributes 50% to the determination of the disease name. In addition, the output unit displays the information in detail if the word "painful" contributes 30% to the determination of the disease name. In addition, the output unit displays the information in detail if the word "unable to sleep" contributes 20% to the determination of the disease name. This makes it possible to support the doctor's diagnosis by displaying the contribution rate in detail. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can display the contribution rate of a specific keyword or phrase in detail using an AI model.

[0094] The output unit can display the listed disease names and related information together at the time of output. For example, the output unit displays related symptoms and treatment methods together with the listed disease names. In addition, the output unit can also display information on related medical institutions together with the listed disease names. Furthermore, the output unit can also display information on related drugs together with the listed disease names. For example, the output unit displays related symptoms and treatment methods together with the listed disease names. In addition, information on related medical institutions together with the listed disease names. In addition, information on related drugs together with the listed disease names. This makes it possible to support the doctor's diagnosis by displaying related information together. Part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can display the listed disease names and their related information together using an AI model.

[0095] The output unit can estimate the emotion of the subject and determine the priority of the output based on the estimated emotion. For example, when the subject is relaxed, the output unit preferentially displays a detailed output result. In addition, when the subject is stressed, the output unit can also preferentially display a concise output result. In addition, when the subject is excited, the output unit can also preferentially display a visually easy-to-understand output result. For example, when the subject is relaxed, the output unit preferentially displays a detailed output result. When the subject is stressed, the output unit preferentially displays a concise output result. When the subject is excited, the output unit preferentially displays a visually easy-to-understand output result. In this way, by determining the priority based on the emotion, a more appropriate output result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the output unit can estimate the emotion of the subject using an AI model and determine the priority of the output.

[0096] The output unit can adjust the order of output according to the time of collection of the conversation at the time of output. The output unit, for example, preferentially displays output results based on recently collected conversations. The output unit can also preferentially display output results based on conversations collected in a specific period. Furthermore, the output unit can also preferentially display important output results based on past conversation history. For example, the output unit preferentially displays output results based on recently collected conversations. The output unit preferentially displays output results based on conversations collected in a specific period. The output unit preferentially displays important output results based on past conversation history. This allows the output order to be adjusted based on the collection time, thereby providing a more appropriate output result. A part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can analyze the collection time of the conversation using an AI model and adjust the order of output.

[0097] The output unit can improve the accuracy of the output by referring to the related medical database at the time of output. The output unit, for example, refers to the medical database and provides an output result based on the latest research results. The output unit can also refer to related academic papers and provide an output result based on highly reliable information. Furthermore, the output unit can also refer to the medical database and provide an output result based on past cases. For example, the output unit refers to the medical database and provides an output result based on the latest research results. The output unit refers to related academic papers and provides an output result based on highly reliable information. The output unit refers to the medical database and provides an output result based on past cases. As a result, the accuracy of the output is improved by referring to the medical database. Part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can improve the accuracy of the output by referring to the related medical database using an AI model.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] When collecting the conversation of the subject, the collection unit can adjust the collection timing in consideration of the subject's life rhythm. For example, if the subject lives a morning-type lifestyle, the conversation can be collected in the morning hours. Also, if the subject lives a night-type lifestyle, the conversation can be collected in the evening hours. Furthermore, if the subject tends to talk a lot on weekends, the conversation can be collected on weekends. By adjusting the collection timing based on the life rhythm, more natural conversation content can be obtained. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze the life rhythm of the subject using an AI model and adjust the collection timing.

[0100] When collecting the conversation of the subject, the collection unit can monitor the health condition of the subject and adjust the collection method according to the health condition. For example, if the subject is in poor health, collection is refrained. Also, if the subject is in a healthy state, conversation can be actively collected. Furthermore, if the subject has a specific illness, conversations related to the illness can be preferentially collected. In this way, by adjusting the collection method based on the health condition, more appropriate conversation content can be obtained. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can monitor the health condition of the subject using an AI model and adjust the collection method.

[0101] When analyzing the voice data, the analysis unit can adjust the analysis algorithm taking into account the speaking speed and volume of the subject. For example, if the subject speaks quickly, an analysis algorithm corresponding to the speaking speed can be applied. Also, if the subject speaks slowly, an analysis algorithm corresponding to the speaking speed can be applied. Furthermore, if the subject speaks loudly, an analysis algorithm corresponding to the volume can be applied. In this way, by adjusting the analysis algorithm based on the speaking speed and volume, more accurate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can evaluate the speaking speed and volume of the subject using an AI model and adjust the analysis algorithm.

[0102] When listing disease names based on the extracted keywords and phrases, the list-up unit can improve the accuracy of the list by taking into account the subject's past medical history. For example, if the subject has had a specific disease in the past, disease names related to that disease are preferentially listed. In addition, if the subject has had multiple diseases in the past, disease names related to those diseases can also be listed. Furthermore, if the subject has frequently complained of a specific symptom in the past, disease names related to that symptom can also be listed. This improves the accuracy of the list by taking into account the past medical history. Part or all of the above-mentioned processing in the list-up unit may be performed, for example, using AI, or may be performed without using AI. For example, the list-up unit can analyze the subject's past medical history using an AI model to improve the accuracy of the list.

[0103] When outputting the factors that were used as the criteria for the listed disease names and their contribution rates, the output unit can adjust the output format according to the subject's level of understanding. For example, if the subject has medical knowledge, detailed output using technical terms is performed. Also, if the subject does not have medical knowledge, concise and easy-to-understand output can be performed. Furthermore, if the subject prefers visual information, output using graphs and diagrams can be performed. This makes it possible to provide more appropriate information by adjusting the output format based on the level of understanding. Part or all of the above-mentioned processing in the output unit may be performed, for example, using AI, or may be performed without using AI. For example, the output unit can evaluate the subject's level of understanding using an AI model and adjust the output format.

[0104] The collection unit can estimate the emotion of the subject and adjust the timing of conversation collection based on the estimated emotion. For example, the collection unit collects the conversation when the subject is relaxed to obtain natural conversation content. The collection unit can also refrain from collection when the subject is stressed and resume collection when the emotion is stable. Furthermore, the collection unit can also collect the conversation when the subject is excited and capture the change in emotion. As a result, natural conversation content can be obtained by adjusting the collection timing based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can estimate the emotion of the subject using an AI model and adjust the collection timing.

[0105] The analysis unit can estimate the emotion of the subject and adjust the expression method of the analysis based on the estimated emotion. For example, when the subject is relaxed, a detailed analysis result is provided. In addition, when the subject is stressed, the analysis unit can also provide a concise analysis result. Furthermore, when the subject is excited, the analysis unit can also provide a visually easy-to-understand analysis result. As a result, by adjusting the expression method of the analysis based on the emotion, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can estimate the emotion of the subject using an AI model and adjust the expression method of the analysis.

[0106] The list-up unit can estimate the emotion of the subject and adjust the listing criteria based on the estimated emotion. For example, when the subject is relaxed, a detailed listing result is provided. In addition, when the subject is stressed, the list-up unit can also provide a concise listing result. Furthermore, when the subject is excited, the list-up unit can also provide a visually easy-to-understand listing result. This allows a more appropriate listing result to be provided by adjusting the listing criteria based on the emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the list-up unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the list-up unit can estimate the emotion of the subject using an AI model and adjust the listing criteria.

[0107] The output unit can estimate the emotion of the subject and adjust the display method of the output based on the estimated emotion. For example, when the subject is relaxed, a detailed output result is provided. In addition, when the subject is stressed, the output unit can also provide a concise output result. Furthermore, when the subject is excited, the output unit can also provide a visually easy-to-understand output result. This makes it possible to provide a more appropriate output result by adjusting the display method based on the emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the output unit can estimate the emotion of the subject using an AI model and adjust the display method of the output.

[0108] The output unit can estimate the emotion of the subject and determine the priority of the output based on the estimated emotion. For example, when the subject is relaxed, the output unit preferentially displays a detailed output result. In addition, when the subject is stressed, the output unit can also preferentially display a concise output result. Furthermore, when the subject is excited, the output unit can also preferentially display a visually easy-to-understand output result. In this way, by determining the priority based on the emotion, a more appropriate output result can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the output unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the output unit can estimate the emotion of the subject using an AI model and determine the priority of the output.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects the subject's everyday conversations as voice data. For example, this includes conversations at home, at work, and with friends. The collection unit records these conversations using a highly sensitive microphone or recording device and saves them as voice data. Step 2: The analysis unit analyzes the voice data collected by the collection unit and extracts specific words and phrases. The analysis is performed using voice recognition technology, natural language processing technology, and generative AI. For example, the voice data is converted into text data, and frequency analysis and semantic analysis are performed to extract specific words and phrases. Step 3: The list-up unit lists disease names based on the words and phrases extracted by the analysis unit. The list-up is done by checking against existing medical databases or using generative AI or machine learning models. For example, the extracted words and phrases are checked against a medical database to list disease names that match the subject's symptoms. Step 4: The output unit outputs the elements that were the judgment criteria and the contribution rates of the elements to the disease names listed by the listing unit. The output is in text format or graph format. For example, the words and phrases that were the judgment criteria are output in text format, and their contribution rates are displayed in graph format.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, list unit, and output unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit records the conversation of the subject using a high-sensitivity microphone of the smart device 14 and saves it as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to extract specific words and phrases. The list unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lists the disease names based on the words and phrases extracted by the analysis unit. The output unit is realized, for example, by the control unit 46A of the smart device 14, and outputs the listed disease names and their judgment criteria in text format or graph format. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, list unit, and output unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit records the conversation of the subject using the microphone 238 of the smart glasses 214 and saves it as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to extract specific words and phrases. The list unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lists the disease names based on the words and phrases extracted by the analysis unit. The output unit is realized, for example, by the control unit 46A of the smart glasses 214, and outputs the listed disease names and their judgment criteria in text format or graph format. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 a voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, list-up unit, and output unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit records the conversation of the subject using the microphone 238 of the headset type terminal 314 and saves it as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to extract specific words and phrases. The list-up unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lists the disease names based on the words and phrases extracted by the analysis unit. The output unit is realized, for example, by the control unit 46A of the headset type terminal 314, and outputs the listed disease names and their judgment criteria in text format or graph format. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, list-up unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit records the conversation of the subject using the microphone 238 of the robot 414 and saves it as voice data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the voice data using a generation AI to extract specific words and phrases. The list-up unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lists the disease names based on the words and phrases extracted by the analysis unit. The output unit is realized, for example, by the control unit 46A of the robot 414, and outputs the listed disease names and their judgment criteria in text format or graph format. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects voice data; an analysis unit that analyzes the voice data collected by the collection unit and extracts specific keywords and phrases; a listing unit that lists disease names based on the keywords and phrases extracted by the analysis unit; and an output unit that outputs the factors that are the criteria for the disease names listed by the list unit and their contribution rates. A system characterized by: (Appendix 2) The collecting unit includes: Collecting audio data from subjects' everyday conversations 2. The system of claim 1, (Appendix 3) The analysis unit is Extracting specific keywords or phrases from audio data 2. The system of claim 1, (Appendix 4) The list-up unit includes: List disease names based on extracted keywords and phrases 2. The system of claim 1, (Appendix 5) The output unit is Output the factors that were the criteria for the listed disease names and their contribution rates 2. The system of claim 1, (Appendix 6) The output unit is Providing doctors with a list of diseases and their diagnosis criteria 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate the target person's emotions and adjust the timing of conversation collection based on the estimated emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the target person's past conversation history and select the appropriate collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: As conversations are collected, they are filtered based on the person’s current life situation and areas of interest. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate the emotions of your audience and prioritize which conversations to collect based on those emotions 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting conversations, prioritize collection of relevant conversations based on the target person's geographic location 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting conversations, analyze the subjects’ social media activity and collect related conversations. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer the subject's emotions and adjust the presentation of the analysis based on the inferred emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the granularity of the analysis based on the importance of certain keywords or phrases 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms based on the conversation category 2. The system of claim 1, (Appendix 16) The analysis unit is Infer the subject's emotions and adjust the length of the analysis based on the inferred emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the conversations were collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on conversation relevance 2. The system of claim 1, (Appendix 19) The list-up unit includes: Infer the target person's emotions and adjust the listing criteria based on the inferred emotions 2. The system of claim 1, (Appendix 20) The list-up unit includes: When creating a list, the accuracy of the list is improved by considering the correlation of certain keywords and phrases. 2. The system of claim 1, (Appendix 21) The list-up unit includes: When making a list, make the list based on the target person's attribute information. 2. The system of claim 1, (Appendix 22) The list-up unit includes: Infer the target audience's emotions and adjust the order in which list results are displayed based on the estimated emotions. 2. The system of claim 1, (Appendix 23) The list-up unit includes: When creating a list, create a list based on the geographic distribution of conversations. 2. The system of claim 1, (Appendix 24) The list-up unit includes: When creating a list, improve the accuracy of the list based on related literature 2. The system of claim 1, (Appendix 25) The output unit is Infer the emotions of your audience and adjust how the output is displayed based on those emotions 2. The system of claim 1, (Appendix 26) The output unit is Display detailed contributions of specific keywords and phrases during output 2. The system of claim 1, (Appendix 27) The output unit is When outputting, the listed disease names and related information are displayed together. 2. The system of claim 1, (Appendix 28) The output unit is Estimate the target person's emotions and prioritize outputs based on the estimated emotions 2. The system of claim 1, (Appendix 29) The output unit is At the time of output, adjust the output order depending on when the conversation was collected 2. The system of claim 1, (Appendix 30) The output unit is At the time of output, the accuracy of the output is improved based on the relevant medical database 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects voice data; an analysis unit that analyzes the voice data collected by the collection unit and extracts specific keywords and phrases; a listing unit that lists disease names based on the keywords and phrases extracted by the analysis unit; and an output unit that outputs the factors that are the criteria for the disease names listed by the list unit and their contribution rates. A system characterized by:

2. The collecting unit includes: Collecting audio data from subjects' everyday conversations 2. The system of claim 1 .

3. The output unit is Provide doctors with a list of diseases and their diagnosis criteria 2. The system of claim 1 .

4. The collecting unit includes: Estimate the target person's emotions and adjust the timing of conversation collection based on the estimated emotions.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the target person's past conversation history and select the appropriate collection method 2. The system of claim 1 .

6. The collecting unit includes: As conversations are collected, they are filtered based on the person’s current life situation and areas of interest.

2. The system of claim 1 .

7. The collecting unit includes: Estimate the emotions of your audience and prioritize which conversations to collect based on those emotions 2. The system of claim 1 .

Images