System

Abstract

To provide a system for making a proposal on an optimal infertility treatment plan to an individual patient.SOLUTION: A system includes a collection part, an analysis part, a proposal part, and a provision part. The collection part collects genetic information of a patient, reproductive medicine data, and statistical data. The analysis part analyzes the data collected by the collection part, and specifies an infertility cause of the patient. The proposal part proposes a treatment plan based on an analysis result acquired by the analysis part. The provision part provides information related to a medical treatment choice or options on the basis of the treatment plan proposed by the proposal part.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 is difficult to propose optimal treatment plans for individual patients in infertility treatment, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal infertility treatment plan for each individual patient. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects genetic information, reproductive medicine data, and statistical data of a patient. The analysis unit analyzes the data collected by the collection unit and identifies the cause of the patient's infertility. The proposal unit proposes a treatment plan based on the analysis result obtained by the analysis unit. The provision unit provides information on treatment options or options based on the treatment plan proposed by the proposal unit. Effect of the Invention

[0007] The system according to the embodiment can propose an optimal infertility treatment plan for an individual patient. [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 the 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 infertility treatment support system according to the embodiment of the present invention is a system that analyzes the genetic information, reproductive medicine data, and statistical data of a patient, and proposes an optimal treatment plan based on the characteristics and medical history of each individual patient. This infertility treatment support system collects the genetic information, reproductive medicine data, statistical data, etc. of the patient, and AI analyzes these data to identify the cause of the patient's infertility. Furthermore, AI creates a predictive model that improves the success rate of treatment and proposes an optimal treatment plan for each individual patient. AI also provides information on infertility treatment options and options. For example, the infertility treatment support system collects the genetic information, reproductive medicine data, statistical data, etc. of the patient. At this time, information on the patient's medical history and lifestyle is also included. For example, data such as the patient's age, past treatment history, and lifestyle habits are collected. This allows for a comprehensive understanding of the patient's overall picture. Next, the AI ​​analyzes the collected data. The AI ​​combines knowledge of genetics, reproductive medicine, and statistics to identify the cause of the patient's infertility. For example, genetic factors are identified from genetic information, and physiological factors are identified from reproductive medicine data. This allows for a comprehensive understanding of the cause of the patient's infertility. Furthermore, AI creates a predictive model that improves the success rate of treatment. AI builds a model that predicts the success rate of treatment based on past treatment data and statistical data. For example, it can predict how effective a particular treatment will be for a particular patient. This makes it possible to propose the optimal treatment plan for the patient. AI also provides information on infertility treatment options. For example, it provides information on the type of treatment, its effects, side effects, etc. This allows patients to select the treatment that is best suited for them. This maximizes the treatment effect for individual patients and solves social issues related to infertility. For example, the success rate of treatment improves and patient satisfaction increases. In addition, treatment options are expanded, allowing patients to select the optimal treatment. This makes it possible to solve social issues related to infertility. This allows the infertility treatment support system to propose the optimal treatment plan for individual patients and maximize the treatment effect.

[0029] The infertility treatment support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects genetic information, reproductive medicine data, and statistical data of the patient. For example, the collection unit collects the genetic information of the patient as DNA sequence data or gene mutation information. The collection unit can also collect the reproductive medicine data as hormone levels or sperm / egg quality. The collection unit can also collect statistical data as demographics or treatment success rates. For example, the collection unit collects the genetic information of the patient as DNA sequence data and analyzes the gene mutation information. The collection unit can also measure hormone levels and collect them as reproductive medicine data. The collection unit can also collect the treatment success rate as statistical data and use it for analysis. The analysis unit analyzes the data collected by the collection unit to identify the cause of the patient's infertility. The analysis unit performs analysis by combining knowledge of, for example, genetics, reproductive medicine, and statistics. For example, the analysis unit identifies genetic factors from the genetic information and physiological factors from the reproductive medicine data. The analysis unit can also use statistical data to comprehensively grasp the cause of the patient's infertility. For example, the analysis unit analyzes genetic information and identifies the effect of gene mutations. The analysis unit can also analyze reproductive medical data and evaluate the effect of hormone therapy. The analysis unit can also analyze statistical data and predict the success rate of treatment. The proposal unit proposes a treatment plan based on the analysis results obtained by the analysis unit. The proposal unit, for example, builds a model that predicts the success rate of treatment based on past treatment data and statistical data. For example, the proposal unit can predict how effective a specific treatment is for a specific patient. The proposal unit can also take into account information on the patient's medical history and lifestyle when proposing a treatment plan. For example, the proposal unit predicts the success rate of a specific treatment based on past treatment data. The proposal unit can also propose an optimal treatment plan based on the patient's medical history and lifestyle. The provision unit provides information on treatment options and options based on the treatment plan proposed by the proposal unit. The provision unit provides information such as the type of treatment, its effect, and side effects. For example, the provision unit provides the type of treatment, such as drug therapy, surgery, and lifestyle changes.The providing unit can also provide information on the effects and side effects of the treatment. For example, the providing unit provides information on the effects and side effects of drug therapy. The providing unit can also provide information on the effects and risks of surgery. As a result, the infertility treatment support system according to the embodiment can propose an optimal treatment plan for an individual patient and maximize the treatment effect. A part or all of the above-mentioned processing in the proposing unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposing unit can propose a treatment plan using an AI model that inputs past treatment data and statistical data and outputs a treatment plan. A part or all of the above-mentioned processing in the collecting unit, the analyzing unit, the proposing unit, and the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs a treatment plan and outputs information on treatment options and options.

[0030] The collection unit collects the genetic information, reproductive medicine data, and statistical data of patients. Specifically, the genetic information of patients is collected as DNA sequence data and gene mutation information. DNA sequence data is obtained with high accuracy using next-generation sequencing technology, and gene mutation information is obtained by detailed analysis of the presence or absence of mutations in specific gene regions and their effects. In addition, the collection unit collects reproductive medicine data as hormone levels and sperm / egg quality. Hormone levels are measured through blood and urine tests, and the concentrations of hormones related to reproduction (e.g., estrogen, progesterone, testosterone, etc.) are particularly important. Sperm / egg quality is evaluated using microscopic observation and specific staining techniques, taking into account morphological abnormalities, motility, maturity, etc. In addition, the collection unit collects statistical data as demographics and treatment success rates. Demographic data includes information such as age, sex, medical history, and lifestyle habits, and treatment success rates are calculated based on past treatment data. For example, the collection unit collects the genetic information of patients as DNA sequence data and analyzes gene mutation information. The collection unit can also measure hormone levels and collect them as reproductive medical data. Furthermore, the collection unit can collect treatment success rates as statistical data and use them for analysis. This allows the collection unit to centrally collect a wide range of data and build detailed patient profiles. The collected data is stored in a cloud-based database and made accessible to the analysis unit and proposal unit. The frequency and accuracy of data collection are adjusted according to the patient's condition and the progress of treatment, and data can be updated in real time. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit to identify the cause of the patient's infertility. The analysis combines knowledge of genetics, reproductive medicine, and statistics. Specifically, genetic factors are identified from genetic information, and physiological factors are identified from reproductive medicine data. Machine learning algorithms using AI are used to analyze genetic information, and the effects of genetic mutations can be identified with high accuracy. For example, if a specific genetic mutation is related to infertility, the presence or absence of the mutation and its degree of impact are analyzed. In addition, the analysis of reproductive medicine data includes evaluation of fluctuations in hormone levels and the quality of sperm and eggs. AI analyzes these data and evaluates the effectiveness of hormone therapy and the possibility of improving the quality of sperm and eggs. In addition, the analysis unit can use statistical data to comprehensively grasp the cause of the patient's infertility. For example, based on past treatment data, it predicts the success rate and risk of a specific treatment. This allows the analysis unit to quickly and accurately analyze the collected data and identify the cause of the patient's infertility. In addition, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past treatment data, it predicts the success rate and risk of a specific treatment, and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal unit proposes a treatment plan based on the analysis results obtained by the analysis unit. The proposal unit builds a model that predicts the success rate of treatment based on past treatment data and statistical data. Specifically, a machine learning model using AI predicts how effective a particular treatment is for a particular patient. For example, if a particular hormone treatment shows a high success rate for patients with a particular gene mutation, that treatment is preferentially proposed. In addition, the proposal unit also takes into account information about the patient's medical history and lifestyle when proposing a treatment plan. For example, the proposal unit proposes an optimal treatment plan by considering the patient's past treatment history, lifestyle habits, stress level, etc. The proposal unit uses AI to analyze these data and simulate multiple treatment scenarios to identify the most effective treatment plan. Furthermore, the proposal unit can not only propose a treatment plan, but also monitor the progress of the treatment and revise the plan as necessary. For example, the treatment progress and the patient's response can be monitored in real time and the treatment plan can be appropriately revised to maximize the treatment effect. In addition, the proposal unit places importance on communication with the patient, and can reflect the patient's opinions and wishes when proposing a treatment plan. This enables the proposal unit to propose an optimal treatment plan for each individual patient, maximizing the therapeutic effect.

[0033] The providing unit provides information on treatment options and options based on the treatment plan proposed by the proposing unit. Specifically, the providing unit provides information on the type of treatment, its effects, side effects, etc. For example, the providing unit provides types of treatment, such as drug therapy, surgery, and lifestyle changes. For drug therapy, the providing unit provides detailed explanations of the type of drug used, the administration method, expected effects, and risks of side effects. For surgery, the providing unit provides information on the type of surgery, the procedure of surgery, risks and effects, and post-operative care. For lifestyle changes, the providing unit provides specific suggestions for improvements in daily life, such as diet, exercise, and stress management. The providing unit can also provide information on the effects and side effects of treatment. For example, the providing unit provides information on the effects and side effects of drug therapy. The providing unit can also provide information on the effects and risks of surgery. In this way, the providing unit provides information necessary for patients to select a treatment method, and supports patients to receive treatment with confidence. Furthermore, the providing unit can collect patient feedback and continuously improve the accuracy and effectiveness of the information provided. For example, the providing unit reviews the content and format of the information provided based on feedback from patients who have received treatment, and provides more understandable and useful information. In addition, the provider can reliably transmit information using multiple communication methods. For example, important information can be delivered reliably by using email, SMS, telephone, video call, etc. in combination. This allows the provider to provide information to patients quickly and reliably, maximizing the effectiveness of treatment.

[0034] The collection unit can collect information on the patient's medical history or lifestyle. The collection unit, for example, collects the patient's medical history. For example, the collection unit can collect past diagnosis results and treatment history. The collection unit can also collect information on the patient's lifestyle. For example, the collection unit can collect information such as the patient's eating habits, exercise habits, and whether or not the patient smokes or drinks alcohol. This allows the collection unit to grasp the overall picture of the patient. Some 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 information using an AI model that inputs information on the patient's medical history and lifestyle and collects data.

[0035] The analysis unit can identify the cause of the patient's infertility by combining knowledge of genetics, reproductive medicine, and statistics. The analysis unit, for example, uses knowledge of genetics to analyze the patient's genetic information. For example, the analysis unit can perform gene analysis and identify the effect of gene mutations. The analysis unit can also analyze reproductive medicine data by using knowledge of reproductive medicine. For example, the analysis unit can evaluate the effect of hormone therapy and identify physiological factors. Furthermore, the analysis unit can analyze statistical data by using knowledge of statistics. For example, the analysis unit can perform regression analysis and statistical hypothesis testing to predict the success rate of treatment. This allows the analysis unit to comprehensively grasp the cause of the patient's infertility by combining knowledge of genetics, reproductive medicine, and statistics. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs genetic information, reproductive medicine data, and statistical data and identifies the cause of infertility.

[0036] The proposal unit can build a model that predicts the success rate of treatment based on past treatment data or statistical data. The proposal unit, for example, builds a model that predicts the success rate of treatment based on past treatment data. For example, the proposal unit can use a machine learning algorithm to learn past treatment data and create a model that predicts the success rate of treatment. The proposal unit can also predict the success rate of treatment based on statistical data. For example, the proposal unit can build a model that predicts the success rate of treatment using a training data set. This allows the proposal unit to propose an optimal treatment plan for the patient based on past treatment data and statistical data. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can propose a treatment plan using a generation AI model that inputs past treatment data and statistical data and predicts the success rate of treatment.

[0037] The providing unit can provide information such as the type of treatment or its effects and side effects. The providing unit provides, for example, the type of treatment. For example, the providing unit can provide the type of treatment such as drug therapy, surgery, and lifestyle changes. The providing unit can also provide information on the effects and side effects of the treatment. For example, the providing unit provides information on the effects and side effects of drug therapy. The providing unit can also provide information on the effects and risks of surgery. This allows the providing unit to provide information for the patient to select the optimal treatment. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can provide information using a generating AI model that inputs a treatment plan and outputs information on the type of treatment, its effects, and side effects.

[0038] The collection unit can analyze the patient's past medical history and select the optimal data collection method. The collection unit, for example, analyzes the patient's past treatment history. For example, the collection unit can analyze past diagnosis results and treatment history and select the most effective data collection method. The collection unit can also analyze the patient's past diagnosis results and determine a method for efficiently collecting necessary data. Furthermore, the collection unit can determine whether a specific data collection method is effective from the patient's past medical history and select it. For example, the collection unit selects the most effective data collection method based on the past treatment history. The collection unit can also analyze past diagnosis results and determine a method for efficiently collecting necessary data. Thereby, the collection unit can analyze the patient's past medical history and select the optimal data collection method. 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 select the data collection method using an AI model that inputs the patient's past medical history and selects the optimal data collection method.

[0039] The collection unit can perform filtering based on the patient's current health condition and lifestyle habits when collecting data. The collection unit, for example, takes into account the patient's current health condition and collects only necessary data. For example, the collection unit can evaluate the patient's health condition and preferentially collect highly relevant data. The collection unit can also preferentially collect highly relevant data based on the patient's lifestyle habits. For example, the collection unit can preferentially collect highly relevant data based on the patient's eating habits and exercise habits. Furthermore, the collection unit can adjust the range of data collection according to the patient's health condition and lifestyle habits. For example, the collection unit can adjust the range of data collection based on the patient's health condition and lifestyle habits and efficiently collect only necessary data. Thereby, the collection unit can filter data based on the patient's current health condition and lifestyle habits and efficiently collect only necessary data. 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 input data regarding the patient's health condition and lifestyle habits to the generation AI and cause the generation AI to perform filtering of the data.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient when collecting data. The collection unit, for example, preferentially collects region-specific health data based on the patient's place of residence. For example, the collection unit can collect region-specific health data based on the patient's place of residence and use it for analysis. The collection unit can also collect data of related medical facilities in consideration of the patient's current location information. For example, the collection unit can collect data of related medical facilities based on the patient's current location information and use it for analysis. Furthermore, the collection unit can also preferentially collect data related to environmental factors based on the patient's geographical location information. For example, the collection unit can collect data related to environmental factors based on the patient's geographical location information and use it for analysis. Thereby, the collection unit can preferentially collect highly relevant data in consideration of the patient's geographical location information and perform more appropriate data collection. 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 input the patient's geographical location information to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0041] The collection unit can analyze the social media activity of the patient at the time of data collection and collect related data. The collection unit can collect health-related information from, for example, the social media posts of the patient. For example, the collection unit can analyze the social media posts of the patient and collect information related to health. The collection unit can also analyze the social media activity of the patient and collect data related to lifestyle habits. For example, the collection unit can analyze the social media activity of the patient and collect data related to lifestyle habits. Furthermore, the collection unit can take into account the social media friendships of the patient and collect data related to health. For example, the collection unit can analyze the social media friendships of the patient and collect data related to health. This allows the collection unit to analyze the social media activity of the patient and efficiently collect related data. A 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 input the social media activity data of the patient to the generation AI and cause the generation AI to execute collection of related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, performs a detailed analysis on important data. For example, the analysis unit can perform a detailed analysis on important data based on the reliability and relevance of the data. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data and perform efficient analysis. 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 perform the analysis using an AI model that uses the importance of the data as an input and adjusts the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit applies a genetic analysis algorithm to genetic information, for example. For example, the analysis unit can apply a genetic analysis algorithm to analyze genetic information to identify the effect of gene mutation. The analysis unit can also apply a reproductive medicine analysis algorithm to reproductive medicine data. For example, the analysis unit can apply a reproductive medicine analysis algorithm to analyze reproductive medicine data to evaluate the effect of hormone therapy. The analysis unit can also apply a statistical analysis algorithm to statistical data. For example, the analysis unit can apply a statistical analysis algorithm to analyze statistical data to predict the success rate of treatment. This allows the analysis unit to apply different analysis algorithms depending on the category of data and perform more accurate analysis. 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 perform analysis using an AI model that inputs the category of data and applies different analysis algorithms.

[0044] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit can prioritize the analysis of the latest data based on the collection date of the data. The analysis unit can also analyze the latest data while referring to past data. For example, the analysis unit can analyze the latest data while referring to past data and predict the treatment success rate. Furthermore, the analysis unit can adjust the order of analysis according to the collection time of the data. For example, the analysis unit can adjust the order of analysis based on the collection time of the data and perform efficient analysis. Thereby, the analysis unit can determine the priority of analysis based on the collection time of the data and perform efficient analysis. 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 perform analysis using an AI model that inputs the collection time of the data and determines the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can perform correlation analysis of data and prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can use a co-occurrence network of data to postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of data. For example, the analysis unit can adjust the order of analysis based on the relevance of data and perform efficient analysis. Thereby, the analysis unit can adjust the order of analysis based on the relevance of data and perform efficient analysis. 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 perform analysis using an AI model that inputs the relevance of data and adjusts the order of analysis.

[0046] The suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment when making a proposal. For example, the suggestion unit makes a detailed proposal for an important treatment. For example, the suggestion unit can make a detailed proposal for an important treatment based on the treatment effect and the patient's condition. In addition, the suggestion unit can make a simplified proposal for a treatment with a low importance. For example, the suggestion unit can adjust the depth of the proposal according to the importance of the treatment. Thereby, the suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment and make an efficient proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the importance of the treatment and adjusts the level of detail of the proposal.

[0047] The proposal unit can apply different proposal algorithms according to the category of the treatment method when making a proposal. For example, the proposal unit applies a proposal algorithm specialized for drug therapy to drug therapy. For example, when proposing drug therapy, the proposal unit can apply a proposal algorithm specialized for drug therapy to propose an optimal drug therapy. The proposal unit can also apply a proposal algorithm specialized for surgical therapy to surgical therapy. For example, when proposing surgical therapy, the proposal unit can apply a proposal algorithm specialized for surgical therapy to propose an optimal surgical therapy. The proposal unit can also apply a proposal algorithm specialized for assisted reproductive technology to assisted reproductive technology. For example, when proposing assisted reproductive technology, the proposal unit can apply a proposal algorithm specialized for assisted reproductive technology to propose an optimal assisted reproductive technology. Thereby, the proposal unit can apply different proposal algorithms according to the category of the treatment method and make a more accurate proposal. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can make a proposal using an AI model that inputs a category of the treatment method and applies different proposal algorithms.

[0048] The suggestion unit can determine the priority of the proposal based on the submission date of the treatment method at the time of proposal. The suggestion unit, for example, preferentially proposes the latest treatment method. For example, the suggestion unit can preferentially propose the latest treatment method based on the submission date of the treatment method. The suggestion unit can also propose the latest treatment method while referring to past treatment methods. For example, the suggestion unit can propose the latest treatment method while referring to past treatment methods, thereby improving the treatment success rate. Furthermore, the suggestion unit can adjust the order of proposals according to the submission date of the treatment method. For example, the suggestion unit can adjust the order of proposals based on the submission date of the treatment method and make efficient proposals. Thereby, the suggestion unit can determine the priority of proposals based on the submission date of the treatment method and make efficient proposals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the submission date of the treatment method and determines the priority of proposals.

[0049] The suggestion unit can adjust the order of the suggestions based on the relevance of the treatments at the time of suggestion. The suggestion unit, for example, preferentially suggests treatments with high relevance. For example, the suggestion unit can perform a correlation analysis of the treatments and preferentially suggest treatments with high relevance. The suggestion unit can also postpone the suggestion of treatments with low relevance. For example, the suggestion unit can use a co-occurrence network of the treatments to postpone the suggestion of treatments with low relevance. Furthermore, the suggestion unit can adjust the order of the suggestions according to the relevance of the treatments. For example, the suggestion unit can adjust the order of the suggestions based on the relevance of the treatments and make efficient suggestions. Thereby, the suggestion unit can adjust the order of the suggestions based on the relevance of the treatments and make efficient suggestions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the relevance of the treatments and adjusts the order of the suggestions.

[0050] The providing unit can provide optimal information by referring to the patient's past treatment history when providing information. The providing unit, for example, refers to the patient's past treatment history. For example, the providing unit can provide related information by referring to past diagnosis results and treatment history. The providing unit can also provide optimal information by referring to the patient's past diagnosis results. Furthermore, the providing unit can select and provide necessary information from the patient's past treatment history. For example, the providing unit provides related information based on the past treatment history. The providing unit can also provide optimal information by referring to past diagnosis results. Thereby, the providing unit can provide optimal information by referring to the patient's past treatment history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the patient's past treatment history and provides optimal information.

[0051] The providing unit can customize the means of providing information based on the patient's current health condition when providing information. The providing unit, for example, considers the patient's current health condition and selects the optimal means of providing information. For example, the providing unit can evaluate the patient's health condition and select the optimal means of providing information. The providing unit can also adjust the method of providing information according to the patient's health condition. For example, the providing unit can adjust the method of providing information based on the patient's health condition and customize and provide necessary information. Furthermore, the providing unit can customize and provide necessary information based on the patient's current health condition. For example, the providing unit can customize the means of providing information based on the patient's health condition and provide optimal information. Thereby, the providing unit can customize the means of providing information based on the patient's current health condition and provide more appropriate information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the patient's health condition and customizes the means of providing information.

[0052] The providing unit can provide optimal information by considering the geographical location information of the patient when providing information. The providing unit provides area-specific medical information based on, for example, the patient's place of residence. For example, the providing unit can collect and provide area-specific medical information based on the patient's place of residence. The providing unit can also provide information on related medical facilities by considering the current location information of the patient. For example, the providing unit can collect and provide information on related medical facilities based on the current location information of the patient. Furthermore, the providing unit can also provide information related to environmental factors based on the geographical location information of the patient. For example, the providing unit can collect and provide information related to environmental factors based on the geographical location information of the patient. Thereby, the providing unit can provide optimal information by considering the geographical location information of the patient and provide more appropriate information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the patient to the generating AI and cause the generating AI to execute the processing to provide optimal information.

[0053] The providing unit can analyze the social media activity of the patient and suggest a means of providing the information when providing the information. The providing unit provides information on health, for example, from the social media posts of the patient. For example, the providing unit can analyze the social media posts of the patient and provide information related to health. The providing unit can also analyze the social media activity of the patient and provide information related to lifestyle habits. For example, the providing unit can analyze the social media activity of the patient and provide information related to lifestyle habits. Furthermore, the providing unit can provide information related to health, taking into account the social media friendships of the patient. For example, the providing unit can analyze the social media friendships of the patient and provide information related to health. Thereby, the providing unit can analyze the social media activity of the patient and suggest a more appropriate means of providing the information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the patient to the generating AI and cause the generating AI to execute the suggesting means of providing the information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can analyze data on environmental factors in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the analysis unit can collect environmental data (air quality, water quality, noise level, etc.) of the patient's place of residence and use it for analysis. The analysis unit can also collect data on the patient's work environment and living environment and use it for analysis. Furthermore, the analysis unit can identify how these environmental factors affect the cause of the patient's infertility and reflect it in the treatment plan. This allows the analysis unit to perform analysis that takes environmental factors into account and propose a more accurate treatment plan.

[0056] The suggestion unit can propose a treatment plan taking into consideration data on the patient's lifestyle in addition to the patient's genetic information, reproductive medicine data, and statistical data. For example, the suggestion unit can collect data such as the patient's eating habits, exercise habits, and sleep patterns, and use the collected data for analysis. The suggestion unit can also collect data on the patient's stress management and mental health, and reflect the data in the treatment plan. Furthermore, the suggestion unit can customize the treatment plan based on the patient's lifestyle and propose a more effective treatment method. This allows the suggestion unit to propose a treatment plan that takes into consideration the patient's lifestyle, maximizing the treatment effect.

[0057] The collection unit can collect data on the patient's social background in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the collection unit can collect data such as the patient's occupation, income, and education level, and use it for analysis. The collection unit can also collect data on the patient's home environment and social support network, and use it for analysis. Furthermore, the collection unit can identify how these social backgrounds affect the cause of the patient's infertility and reflect them in the treatment plan. This allows the collection unit to collect data that takes social backgrounds into account and propose a more comprehensive treatment plan.

[0058] The proposal unit can propose a treatment plan taking into consideration data on the patient's living environment in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the proposal unit can collect environmental data (air quality, water quality, noise level, etc.) of the patient's place of residence and use it for analysis. The proposal unit can also collect data on the patient's working environment and living environment and reflect it in the treatment plan. Furthermore, the proposal unit can identify how these environmental factors affect the cause of the patient's infertility and reflect it in the treatment plan. This allows the proposal unit to propose a treatment plan that takes environmental factors into consideration and maximize the treatment effect.

[0059] The collection unit can collect data from the patient's digital devices in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the collection unit can collect data such as daily activity levels, sleep patterns, and heart rates from the patient's smartphone or wearable device, and use the data for analysis. The collection unit can also collect data from the patient's digital devices in real time and reflect the data in treatment plans. Furthermore, the collection unit can analyze the data from these digital devices and comprehensively grasp the patient's health condition. This allows the collection unit to utilize the data from the digital devices to propose more accurate treatment plans.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The collection department collects the patient’s genetic information, reproductive medicine data, and statistical data. For example, the patient’s genetic information is collected as DNA sequence data and gene mutation information, reproductive medicine data is collected as hormone levels and sperm / egg quality, and statistical data is collected as demographics and treatment success rate. Step 2: The analysis department analyzes the data collected by the collection department and identifies the cause of the patient's infertility. For example, the analysis combines knowledge of genetics, reproductive medicine, and statistics to identify genetic factors from genetic information and physiological factors from reproductive medicine data. In addition, statistical data is used to comprehensively grasp the cause of the patient's infertility. Step 3: The proposal unit proposes a treatment plan based on the analysis results obtained by the analysis unit. For example, it builds a model that predicts the success rate of treatment based on past treatment data and statistical data, and predicts how effective a specific treatment will be for a specific patient. It also proposes an optimal treatment plan based on the patient's medical history and lifestyle. Step 4: The provider provides information on treatment options based on the treatment plan proposed by the suggester, such as the type of treatment, its effectiveness, and side effects, and provides the type of treatment, such as drug therapy, surgery, and lifestyle changes. It also provides information on the effectiveness and side effects of the treatment.

[0062] (Example 2) The infertility treatment support system according to the embodiment of the present invention is a system that analyzes the genetic information, reproductive medicine data, and statistical data of a patient, and proposes an optimal treatment plan based on the characteristics and medical history of each individual patient. This infertility treatment support system collects the genetic information, reproductive medicine data, statistical data, etc. of the patient, and AI analyzes these data to identify the cause of the patient's infertility. Furthermore, AI creates a predictive model that improves the success rate of treatment and proposes an optimal treatment plan for each individual patient. AI also provides information on infertility treatment options and options. For example, the infertility treatment support system collects the genetic information, reproductive medicine data, statistical data, etc. of the patient. At this time, information on the patient's medical history and lifestyle is also included. For example, data such as the patient's age, past treatment history, and lifestyle habits are collected. This allows for a comprehensive understanding of the patient's overall picture. Next, the AI ​​analyzes the collected data. The AI ​​combines knowledge of genetics, reproductive medicine, and statistics to identify the cause of the patient's infertility. For example, genetic factors are identified from genetic information, and physiological factors are identified from reproductive medicine data. This allows for a comprehensive understanding of the cause of the patient's infertility. Furthermore, AI creates a predictive model that improves the success rate of treatment. AI builds a model that predicts the success rate of treatment based on past treatment data and statistical data. For example, it can predict how effective a particular treatment will be for a particular patient. This makes it possible to propose the optimal treatment plan for the patient. AI also provides information on infertility treatment options. For example, it provides information on the type of treatment, its effects, side effects, etc. This allows patients to select the treatment that is best suited for them. This maximizes the treatment effect for individual patients and solves social issues related to infertility. For example, the success rate of treatment improves and patient satisfaction increases. In addition, treatment options are expanded, allowing patients to select the optimal treatment. This makes it possible to solve social issues related to infertility. This allows the infertility treatment support system to propose the optimal treatment plan for individual patients and maximize the treatment effect.

[0063] The infertility treatment support system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects genetic information, reproductive medicine data, and statistical data of the patient. For example, the collection unit collects the genetic information of the patient as DNA sequence data or gene mutation information. The collection unit can also collect the reproductive medicine data as hormone levels or sperm / egg quality. The collection unit can also collect statistical data as demographics or treatment success rates. For example, the collection unit collects the genetic information of the patient as DNA sequence data and analyzes the gene mutation information. The collection unit can also measure hormone levels and collect them as reproductive medicine data. The collection unit can also collect the treatment success rate as statistical data and use it for analysis. The analysis unit analyzes the data collected by the collection unit to identify the cause of the patient's infertility. The analysis unit performs analysis by combining knowledge of, for example, genetics, reproductive medicine, and statistics. For example, the analysis unit identifies genetic factors from the genetic information and physiological factors from the reproductive medicine data. The analysis unit can also use statistical data to comprehensively grasp the cause of the patient's infertility. For example, the analysis unit analyzes genetic information and identifies the effect of gene mutations. The analysis unit can also analyze reproductive medical data and evaluate the effect of hormone therapy. The analysis unit can also analyze statistical data and predict the success rate of treatment. The proposal unit proposes a treatment plan based on the analysis results obtained by the analysis unit. The proposal unit, for example, builds a model that predicts the success rate of treatment based on past treatment data and statistical data. For example, the proposal unit can predict how effective a specific treatment is for a specific patient. The proposal unit can also take into account information on the patient's medical history and lifestyle when proposing a treatment plan. For example, the proposal unit predicts the success rate of a specific treatment based on past treatment data. The proposal unit can also propose an optimal treatment plan based on the patient's medical history and lifestyle. The provision unit provides information on treatment options and options based on the treatment plan proposed by the proposal unit. The provision unit provides information such as the type of treatment, its effect, and side effects. For example, the provision unit provides the type of treatment, such as drug therapy, surgery, and lifestyle changes.The providing unit can also provide information on the effects and side effects of the treatment. For example, the providing unit provides information on the effects and side effects of drug therapy. The providing unit can also provide information on the effects and risks of surgery. As a result, the infertility treatment support system according to the embodiment can propose an optimal treatment plan for an individual patient and maximize the treatment effect. A part or all of the above-mentioned processing in the proposing unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposing unit can propose a treatment plan using an AI model that inputs past treatment data and statistical data and outputs a treatment plan. A part or all of the above-mentioned processing in the collecting unit, the analyzing unit, the proposing unit, and the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs a treatment plan and outputs information on treatment options and options.

[0064] The collection unit collects the genetic information, reproductive medicine data, and statistical data of patients. Specifically, the genetic information of patients is collected as DNA sequence data and gene mutation information. DNA sequence data is obtained with high accuracy using next-generation sequencing technology, and gene mutation information is obtained by detailed analysis of the presence or absence of mutations in specific gene regions and their effects. In addition, the collection unit collects reproductive medicine data as hormone levels and sperm / egg quality. Hormone levels are measured through blood and urine tests, and the concentrations of hormones related to reproduction (e.g., estrogen, progesterone, testosterone, etc.) are particularly important. Sperm / egg quality is evaluated using microscopic observation and specific staining techniques, taking into account morphological abnormalities, motility, maturity, etc. In addition, the collection unit collects statistical data as demographics and treatment success rates. Demographic data includes information such as age, sex, medical history, and lifestyle habits, and treatment success rates are calculated based on past treatment data. For example, the collection unit collects the genetic information of patients as DNA sequence data and analyzes gene mutation information. The collection unit can also measure hormone levels and collect them as reproductive medical data. Furthermore, the collection unit can collect treatment success rates as statistical data and use them for analysis. This allows the collection unit to centrally collect a wide range of data and build detailed patient profiles. The collected data is stored in a cloud-based database and made accessible to the analysis unit and proposal unit. The frequency and accuracy of data collection are adjusted according to the patient's condition and the progress of treatment, and data can be updated in real time. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0065] The analysis unit analyzes the data collected by the collection unit to identify the cause of the patient's infertility. The analysis combines knowledge of genetics, reproductive medicine, and statistics. Specifically, genetic factors are identified from genetic information, and physiological factors are identified from reproductive medicine data. Machine learning algorithms using AI are used to analyze genetic information, and the effects of genetic mutations can be identified with high accuracy. For example, if a specific genetic mutation is related to infertility, the presence or absence of the mutation and its degree of impact are analyzed. In addition, the analysis of reproductive medicine data includes evaluation of fluctuations in hormone levels and the quality of sperm and eggs. AI analyzes these data and evaluates the effectiveness of hormone therapy and the possibility of improving the quality of sperm and eggs. In addition, the analysis unit can use statistical data to comprehensively grasp the cause of the patient's infertility. For example, based on past treatment data, it predicts the success rate and risk of a specific treatment. This allows the analysis unit to quickly and accurately analyze the collected data and identify the cause of the patient's infertility. In addition, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past treatment data, it predicts the success rate and risk of a specific treatment, and plans future measures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings, allowing the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0066] The proposal unit proposes a treatment plan based on the analysis results obtained by the analysis unit. The proposal unit builds a model that predicts the success rate of treatment based on past treatment data and statistical data. Specifically, a machine learning model using AI predicts how effective a particular treatment is for a particular patient. For example, if a particular hormone treatment shows a high success rate for patients with a particular gene mutation, that treatment is preferentially proposed. In addition, the proposal unit also takes into account information about the patient's medical history and lifestyle when proposing a treatment plan. For example, the proposal unit proposes an optimal treatment plan by considering the patient's past treatment history, lifestyle habits, stress level, etc. The proposal unit uses AI to analyze these data and simulate multiple treatment scenarios to identify the most effective treatment plan. Furthermore, the proposal unit can not only propose a treatment plan, but also monitor the progress of the treatment and revise the plan as necessary. For example, the treatment progress and the patient's response can be monitored in real time and the treatment plan can be appropriately revised to maximize the treatment effect. In addition, the proposal unit places importance on communication with the patient, and can reflect the patient's opinions and wishes when proposing a treatment plan. This enables the proposal unit to propose an optimal treatment plan for each individual patient, maximizing the therapeutic effect.

[0067] The providing unit provides information on treatment options and options based on the treatment plan proposed by the proposing unit. Specifically, the providing unit provides information on the type of treatment, its effects, side effects, etc. For example, the providing unit provides types of treatment, such as drug therapy, surgery, and lifestyle changes. For drug therapy, the providing unit provides detailed explanations of the type of drug used, the administration method, expected effects, and risks of side effects. For surgery, the providing unit provides information on the type of surgery, the procedure of surgery, risks and effects, and post-operative care. For lifestyle changes, the providing unit provides specific suggestions for improvements in daily life, such as diet, exercise, and stress management. The providing unit can also provide information on the effects and side effects of treatment. For example, the providing unit provides information on the effects and side effects of drug therapy. The providing unit can also provide information on the effects and risks of surgery. In this way, the providing unit provides information necessary for patients to select a treatment method, and supports patients to receive treatment with confidence. Furthermore, the providing unit can collect patient feedback and continuously improve the accuracy and effectiveness of the information provided. For example, the providing unit reviews the content and format of the information provided based on feedback from patients who have received treatment, and provides more understandable and useful information. In addition, the provider can reliably transmit information using multiple communication methods. For example, important information can be delivered reliably by using email, SMS, telephone, video call, etc. in combination. This allows the provider to provide information to patients quickly and reliably, maximizing the effectiveness of treatment.

[0068] The collection unit can collect information on the patient's medical history or lifestyle. The collection unit, for example, collects the patient's medical history. For example, the collection unit can collect past diagnosis results and treatment history. The collection unit can also collect information on the patient's lifestyle. For example, the collection unit can collect information such as the patient's eating habits, exercise habits, and whether or not the patient smokes or drinks alcohol. This allows the collection unit to grasp the overall picture of the patient. Some 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 information using an AI model that inputs information on the patient's medical history and lifestyle and collects data.

[0069] The analysis unit can identify the cause of the patient's infertility by combining knowledge of genetics, reproductive medicine, and statistics. The analysis unit, for example, uses knowledge of genetics to analyze the patient's genetic information. For example, the analysis unit can perform gene analysis and identify the effect of gene mutations. The analysis unit can also analyze reproductive medicine data by using knowledge of reproductive medicine. For example, the analysis unit can evaluate the effect of hormone therapy and identify physiological factors. Furthermore, the analysis unit can analyze statistical data by using knowledge of statistics. For example, the analysis unit can perform regression analysis and statistical hypothesis testing to predict the success rate of treatment. This allows the analysis unit to comprehensively grasp the cause of the patient's infertility by combining knowledge of genetics, reproductive medicine, and statistics. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs genetic information, reproductive medicine data, and statistical data and identifies the cause of infertility.

[0070] The proposal unit can build a model that predicts the success rate of treatment based on past treatment data or statistical data. The proposal unit, for example, builds a model that predicts the success rate of treatment based on past treatment data. For example, the proposal unit can use a machine learning algorithm to learn past treatment data and create a model that predicts the success rate of treatment. The proposal unit can also predict the success rate of treatment based on statistical data. For example, the proposal unit can build a model that predicts the success rate of treatment using a training data set. This allows the proposal unit to propose an optimal treatment plan for the patient based on past treatment data and statistical data. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the proposal unit can propose a treatment plan using a generation AI model that inputs past treatment data and statistical data and predicts the success rate of treatment.

[0071] The providing unit can provide information such as the type of treatment or its effects and side effects. The providing unit provides, for example, the type of treatment. For example, the providing unit can provide the type of treatment such as drug therapy, surgery, and lifestyle changes. The providing unit can also provide information on the effects and side effects of the treatment. For example, the providing unit provides information on the effects and side effects of drug therapy. The providing unit can also provide information on the effects and risks of surgery. This allows the providing unit to provide information for the patient to select the optimal treatment. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can provide information using a generating AI model that inputs a treatment plan and outputs information on the type of treatment, its effects, and side effects.

[0072] The collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient's emotions. The collection unit, for example, estimates the patient's emotions. For example, the collection unit can analyze the patient's facial expressions using facial expression recognition technology to estimate the emotions. The collection unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate the emotions. Furthermore, the collection unit can estimate the patient's emotions using self-reports. For example, the collection unit has the patient self-report their emotional state and estimates the emotions based on the information. Next, the collection unit adjusts the timing of data collection based on the estimated patient's emotions. For example, if the patient is feeling stressed, the timing of collection is adjusted to collect data in a relaxed state. Also, if the patient is relaxed, data collection can be started immediately and information can be acquired efficiently. Furthermore, if the patient is feeling anxious, the timing of collection can be adjusted to suit the patient in order to give a sense of security. This allows the collection unit to adjust the timing of data collection according to the patient's emotions and perform more appropriate data collection. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0073] The collection unit can analyze the patient's past medical history and select the optimal data collection method. The collection unit, for example, analyzes the patient's past treatment history. For example, the collection unit can analyze past diagnosis results and treatment history and select the most effective data collection method. The collection unit can also analyze the patient's past diagnosis results and determine a method for efficiently collecting necessary data. Furthermore, the collection unit can determine whether a specific data collection method is effective from the patient's past medical history and select it. For example, the collection unit selects the most effective data collection method based on the past treatment history. The collection unit can also analyze past diagnosis results and determine a method for efficiently collecting necessary data. Thereby, the collection unit can analyze the patient's past medical history and select the optimal data collection method. 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 select the data collection method using an AI model that inputs the patient's past medical history and selects the optimal data collection method.

[0074] The collection unit can perform filtering based on the patient's current health condition and lifestyle habits when collecting data. The collection unit, for example, takes into account the patient's current health condition and collects only necessary data. For example, the collection unit can evaluate the patient's health condition and preferentially collect highly relevant data. The collection unit can also preferentially collect highly relevant data based on the patient's lifestyle habits. For example, the collection unit can preferentially collect highly relevant data based on the patient's eating habits and exercise habits. Furthermore, the collection unit can adjust the range of data collection according to the patient's health condition and lifestyle habits. For example, the collection unit can adjust the range of data collection based on the patient's health condition and lifestyle habits and efficiently collect only necessary data. Thereby, the collection unit can filter data based on the patient's current health condition and lifestyle habits and efficiently collect only necessary data. 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 input data regarding the patient's health condition and lifestyle habits to the generation AI and cause the generation AI to perform filtering of the data.

[0075] The collection unit can estimate the patient's emotions and determine the priority of data to be collected based on the estimated patient's emotions. The collection unit, for example, estimates the patient's emotions. For example, the collection unit can analyze the patient's facial expressions using facial expression recognition technology to estimate emotions. The collection unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate emotions. Furthermore, the collection unit can estimate the patient's emotions using self-reports. For example, the collection unit has the patient self-report their emotional state and estimates emotions based on the information. Next, the collection unit determines the priority of data to be collected based on the estimated patient's emotions. For example, if the patient is stressed, data related to stress is preferentially collected. Also, if the patient is relaxed, overall health data can be collected in a balanced manner. Furthermore, if the patient is anxious, data related to anxiety can also be preferentially collected. This allows the collection unit to determine the priority of data to be collected according to the patient's emotions and perform more appropriate data collection. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0076] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient when collecting data. The collection unit, for example, preferentially collects region-specific health data based on the patient's place of residence. For example, the collection unit can collect region-specific health data based on the patient's place of residence and use it for analysis. The collection unit can also collect data of related medical facilities in consideration of the patient's current location information. For example, the collection unit can collect data of related medical facilities based on the patient's current location information and use it for analysis. Furthermore, the collection unit can also preferentially collect data related to environmental factors based on the patient's geographical location information. For example, the collection unit can collect data related to environmental factors based on the patient's geographical location information and use it for analysis. Thereby, the collection unit can preferentially collect highly relevant data in consideration of the patient's geographical location information and perform more appropriate data collection. 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 input the patient's geographical location information to the generation AI and cause the generation AI to execute collection of highly relevant data.

[0077] The collection unit can analyze the social media activity of the patient at the time of data collection and collect related data. The collection unit can collect health-related information from, for example, the social media posts of the patient. For example, the collection unit can analyze the social media posts of the patient and collect information related to health. The collection unit can also analyze the social media activity of the patient and collect data related to lifestyle habits. For example, the collection unit can analyze the social media activity of the patient and collect data related to lifestyle habits. Furthermore, the collection unit can take into account the social media friendships of the patient and collect data related to health. For example, the collection unit can analyze the social media friendships of the patient and collect data related to health. This allows the collection unit to analyze the social media activity of the patient and efficiently collect related data. A 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 input the social media activity data of the patient to the generation AI and cause the generation AI to execute collection of related data.

[0078] The analysis unit can estimate the patient's emotions and adjust the expression method of the analysis based on the estimated patient's emotions. The analysis unit, for example, estimates the patient's emotions. For example, the analysis unit can analyze the patient's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate emotions. Furthermore, the analysis unit can estimate the patient's emotions using self-reports. For example, the analysis unit has the patient self-report their emotional state and estimates emotions based on the information. Next, the analysis unit adjusts the expression method of the analysis based on the estimated patient's emotions. For example, if the patient is feeling stressed, a simple and easy-to-understand analysis result can be provided. Also, if the patient is relaxed, a detailed analysis result can be provided. Furthermore, if the patient is feeling anxious, an analysis result that gives a sense of security can be provided. This allows the analysis unit to adjust the expression method of the analysis according to the patient's emotions and provide a more appropriate analysis result. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit, for example, performs a detailed analysis on important data. For example, the analysis unit can perform a detailed analysis on important data based on the reliability and relevance of the data. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can adjust the depth of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data and perform efficient analysis. 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 perform the analysis using an AI model that uses the importance of the data as an input and adjusts the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit applies a genetic analysis algorithm to genetic information, for example. For example, the analysis unit can apply a genetic analysis algorithm to analyze genetic information to identify the effect of gene mutation. The analysis unit can also apply a reproductive medicine analysis algorithm to reproductive medicine data. For example, the analysis unit can apply a reproductive medicine analysis algorithm to analyze reproductive medicine data to evaluate the effect of hormone therapy. The analysis unit can also apply a statistical analysis algorithm to statistical data. For example, the analysis unit can apply a statistical analysis algorithm to analyze statistical data to predict the success rate of treatment. This allows the analysis unit to apply different analysis algorithms depending on the category of data and perform more accurate analysis. 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 perform analysis using an AI model that inputs the category of data and applies different analysis algorithms.

[0081] The analysis unit can estimate the patient's emotions and adjust the length of the analysis based on the estimated patient's emotions. The analysis unit, for example, estimates the patient's emotions. For example, the analysis unit can analyze the patient's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate emotions. Furthermore, the analysis unit can estimate the patient's emotions using self-reports. For example, the analysis unit has the patient self-report their emotional state and estimates the emotions based on the information. Next, the analysis unit adjusts the length of the analysis based on the estimated patient's emotions. For example, if the patient is in a hurry, a short and to the point analysis result can be provided. Also, if the patient is relaxed, a detailed analysis result can be provided. Furthermore, if the patient is excited, a visually stimulating analysis result can be provided. This allows the analysis unit to adjust the length of the analysis according to the patient's emotions and provide a more appropriate analysis result. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0082] The analysis unit can determine the priority of the analysis based on the collection time of the data at the time of analysis. The analysis unit, for example, prioritizes the analysis of the latest data. For example, the analysis unit can prioritize the analysis of the latest data based on the collection date of the data. The analysis unit can also analyze the latest data while referring to past data. For example, the analysis unit can analyze the latest data while referring to past data and predict the treatment success rate. Furthermore, the analysis unit can adjust the order of analysis according to the collection time of the data. For example, the analysis unit can adjust the order of analysis based on the collection time of the data and perform efficient analysis. Thereby, the analysis unit can determine the priority of analysis based on the collection time of the data and perform efficient analysis. 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 perform analysis using an AI model that inputs the collection time of the data and determines the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can perform correlation analysis of data and prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit can use a co-occurrence network of data to postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of data. For example, the analysis unit can adjust the order of analysis based on the relevance of data and perform efficient analysis. Thereby, the analysis unit can adjust the order of analysis based on the relevance of data and perform efficient analysis. 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 perform analysis using an AI model that inputs the relevance of data and adjusts the order of analysis.

[0084] The suggestion unit can estimate the patient's emotion and adjust the expression method of the proposal based on the estimated patient's emotion. The suggestion unit, for example, estimates the patient's emotion. For example, the suggestion unit can analyze the patient's facial expression using facial expression recognition technology to estimate the emotion. The suggestion unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate the emotion. Furthermore, the suggestion unit can estimate the patient's emotion using a self-report. For example, the suggestion unit has the patient self-report their emotional state and estimates the emotion based on the information. Next, the suggestion unit adjusts the expression method of the proposal based on the estimated patient's emotion. For example, if the patient is feeling stressed, a simple and easy-to-understand suggestion can be made. Also, if the patient is relaxed, a detailed suggestion can be made. Furthermore, if the patient is feeling anxious, a suggestion that gives a sense of security can be made. As a result, the suggestion unit can adjust the expression method of the proposal according to the patient's emotion and make a more appropriate suggestion. 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the proposing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the proposing unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0085] The suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment when making a proposal. For example, the suggestion unit makes a detailed proposal for an important treatment. For example, the suggestion unit can make a detailed proposal for an important treatment based on the treatment effect and the patient's condition. In addition, the suggestion unit can make a simplified proposal for a treatment with a low importance. For example, the suggestion unit can adjust the depth of the proposal according to the importance of the treatment. Thereby, the suggestion unit can adjust the level of detail of the proposal based on the importance of the treatment and make an efficient proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the importance of the treatment and adjusts the level of detail of the proposal.

[0086] The proposal unit can apply different proposal algorithms according to the category of the treatment method when making a proposal. For example, the proposal unit applies a proposal algorithm specialized for drug therapy to drug therapy. For example, when proposing drug therapy, the proposal unit can apply a proposal algorithm specialized for drug therapy to propose an optimal drug therapy. The proposal unit can also apply a proposal algorithm specialized for surgical therapy to surgical therapy. For example, when proposing surgical therapy, the proposal unit can apply a proposal algorithm specialized for surgical therapy to propose an optimal surgical therapy. The proposal unit can also apply a proposal algorithm specialized for assisted reproductive technology to assisted reproductive technology. For example, when proposing assisted reproductive technology, the proposal unit can apply a proposal algorithm specialized for assisted reproductive technology to propose an optimal assisted reproductive technology. Thereby, the proposal unit can apply different proposal algorithms according to the category of the treatment method and make a more accurate proposal. Part or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can make a proposal using an AI model that inputs a category of the treatment method and applies different proposal algorithms.

[0087] The suggestion unit can estimate the patient's emotion and adjust the length of the proposal based on the estimated patient's emotion. The suggestion unit, for example, estimates the patient's emotion. For example, the suggestion unit can analyze the patient's facial expression using facial expression recognition technology to estimate the emotion. The suggestion unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate the emotion. Furthermore, the suggestion unit can estimate the patient's emotion using a self-report. For example, the suggestion unit has the patient self-report their emotional state and estimates the emotion based on the information. Next, the suggestion unit adjusts the length of the proposal based on the estimated patient's emotion. For example, if the patient is in a hurry, a short and to the point proposal can be made. Also, if the patient is relaxed, a detailed proposal can be made. Furthermore, if the patient is excited, a visually stimulating proposal can be made. This allows the suggestion unit to adjust the length of the proposal according to the patient's emotion and make a more appropriate proposal. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the proposing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the proposing unit may input facial expression data of a patient to the generation AI and cause the generation AI to estimate emotions.

[0088] The suggestion unit can determine the priority of the proposal based on the submission date of the treatment method at the time of proposal. The suggestion unit, for example, preferentially proposes the latest treatment method. For example, the suggestion unit can preferentially propose the latest treatment method based on the submission date of the treatment method. The suggestion unit can also propose the latest treatment method while referring to past treatment methods. For example, the suggestion unit can propose the latest treatment method while referring to past treatment methods, thereby improving the treatment success rate. Furthermore, the suggestion unit can adjust the order of proposals according to the submission date of the treatment method. For example, the suggestion unit can adjust the order of proposals based on the submission date of the treatment method and make efficient proposals. Thereby, the suggestion unit can determine the priority of proposals based on the submission date of the treatment method and make efficient proposals. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make a proposal using an AI model that inputs the submission date of the treatment method and determines the priority of proposals.

[0089] The suggestion unit can adjust the order of the suggestions based on the relevance of the treatments at the time of suggestion. The suggestion unit, for example, preferentially suggests treatments with high relevance. For example, the suggestion unit can perform a correlation analysis of the treatments and preferentially suggest treatments with high relevance. The suggestion unit can also postpone the suggestion of treatments with low relevance. For example, the suggestion unit can use a co-occurrence network of the treatments to postpone the suggestion of treatments with low relevance. Furthermore, the suggestion unit can adjust the order of the suggestions according to the relevance of the treatments. For example, the suggestion unit can adjust the order of the suggestions based on the relevance of the treatments and make efficient suggestions. Thereby, the suggestion unit can adjust the order of the suggestions based on the relevance of the treatments and make efficient suggestions. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can make suggestions using an AI model that inputs the relevance of the treatments and adjusts the order of the suggestions.

[0090] The providing unit can estimate the patient's emotion and adjust the method of providing information based on the estimated patient's emotion. The providing unit, for example, estimates the patient's emotion. For example, the providing unit can analyze the patient's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate the emotion. Furthermore, the providing unit can estimate the patient's emotion using a self-report. For example, the providing unit has the patient self-report their emotional state and estimates the emotion based on the information. Next, the providing unit adjusts the method of providing information based on the estimated patient's emotion. For example, when the patient is feeling stressed, simple and easy-to-understand information is provided. In addition, when the patient is relaxed, detailed information can be provided. Furthermore, when the patient is feeling anxious, information that gives a sense of security can be provided. As a result, the providing unit can adjust the method of providing information according to the patient's emotion and provide more appropriate information. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input facial expression data of a patient to the generating AI and cause the generating AI to estimate emotions.

[0091] The providing unit can provide optimal information by referring to the patient's past treatment history when providing information. The providing unit, for example, refers to the patient's past treatment history. For example, the providing unit can provide related information by referring to past diagnosis results and treatment history. The providing unit can also provide optimal information by referring to the patient's past diagnosis results. Furthermore, the providing unit can select and provide necessary information from the patient's past treatment history. For example, the providing unit provides related information based on the past treatment history. The providing unit can also provide optimal information by referring to past diagnosis results. Thereby, the providing unit can provide optimal information by referring to the patient's past treatment history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the patient's past treatment history and provides optimal information.

[0092] The providing unit can customize the means of providing information based on the patient's current health condition when providing information. The providing unit, for example, considers the patient's current health condition and selects the optimal means of providing information. For example, the providing unit can evaluate the patient's health condition and select the optimal means of providing information. The providing unit can also adjust the method of providing information according to the patient's health condition. For example, the providing unit can adjust the method of providing information based on the patient's health condition and customize and provide necessary information. Furthermore, the providing unit can customize and provide necessary information based on the patient's current health condition. For example, the providing unit can customize the means of providing information based on the patient's health condition and provide optimal information. Thereby, the providing unit can customize the means of providing information based on the patient's current health condition and provide more appropriate information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that inputs the patient's health condition and customizes the means of providing information.

[0093] The providing unit can estimate the patient's emotions and determine the priority of information provision based on the estimated patient's emotions. The providing unit, for example, estimates the patient's emotions. For example, the providing unit can analyze the patient's facial expressions using facial expression recognition technology to estimate the emotions. The providing unit can also analyze the tone and speed of the patient's voice using voice analysis technology to estimate the emotions. Furthermore, the providing unit can estimate the patient's emotions using self-reports. For example, the providing unit has the patient self-report their emotional state and estimates the emotions based on the information. Next, the providing unit determines the priority of information provision based on the estimated patient's emotions. For example, if the patient is feeling stressed, information related to stress reduction can be provided preferentially. Also, if the patient is relaxed, overall health information can be provided in a balanced manner. Furthermore, if the patient is feeling anxious, information related to anxiety reduction can be provided preferentially. As a result, the providing unit can determine the priority of information provision according to the patient's emotions and provide more appropriate information. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input facial expression data of a patient to the generating AI and cause the generating AI to estimate emotions.

[0094] The providing unit can provide optimal information by considering the geographical location information of the patient when providing information. The providing unit provides area-specific medical information based on, for example, the patient's place of residence. For example, the providing unit can collect and provide area-specific medical information based on the patient's place of residence. The providing unit can also provide information on related medical facilities by considering the current location information of the patient. For example, the providing unit can collect and provide information on related medical facilities based on the current location information of the patient. Furthermore, the providing unit can also provide information related to environmental factors based on the geographical location information of the patient. For example, the providing unit can collect and provide information related to environmental factors based on the geographical location information of the patient. Thereby, the providing unit can provide optimal information by considering the geographical location information of the patient and provide more appropriate information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the patient to the generating AI and cause the generating AI to execute the processing to provide optimal information.

[0095] The providing unit can analyze the social media activity of the patient and suggest a means of providing the information when providing the information. The providing unit provides information on health, for example, from the social media posts of the patient. For example, the providing unit can analyze the social media posts of the patient and provide information related to health. The providing unit can also analyze the social media activity of the patient and provide information related to lifestyle habits. For example, the providing unit can analyze the social media activity of the patient and provide information related to lifestyle habits. Furthermore, the providing unit can provide information related to health, taking into account the social media friendships of the patient. For example, the providing unit can analyze the social media friendships of the patient and provide information related to health. Thereby, the providing unit can analyze the social media activity of the patient and suggest a more appropriate means of providing the information. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the patient to the generating AI and cause the generating AI to execute the suggesting means of providing the information.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The collection unit can collect data on the patient's psychological state in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the collection unit can collect data using questionnaires and sensors to evaluate the patient's stress level and psychological health. The collection unit can also conduct regular counseling sessions to evaluate the patient's psychological state and collect the results as data. Furthermore, the collection unit can analyze data on the patient's psychological state and reflect the results in the treatment plan. This allows the collection unit to collect data that takes into account the patient's psychological state and propose a more comprehensive treatment plan.

[0098] The analysis unit can analyze data on environmental factors in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the analysis unit can collect environmental data (air quality, water quality, noise level, etc.) of the patient's place of residence and use it for analysis. The analysis unit can also collect data on the patient's work environment and living environment and use it for analysis. Furthermore, the analysis unit can identify how these environmental factors affect the cause of the patient's infertility and reflect it in the treatment plan. This allows the analysis unit to perform analysis that takes environmental factors into account and propose a more accurate treatment plan.

[0099] The suggestion unit can propose a treatment plan taking into consideration data on the patient's lifestyle in addition to the patient's genetic information, reproductive medicine data, and statistical data. For example, the suggestion unit can collect data such as the patient's eating habits, exercise habits, and sleep patterns, and use the collected data for analysis. The suggestion unit can also collect data on the patient's stress management and mental health, and reflect the data in the treatment plan. Furthermore, the suggestion unit can customize the treatment plan based on the patient's lifestyle and propose a more effective treatment method. This allows the suggestion unit to propose a treatment plan that takes into consideration the patient's lifestyle, maximizing the treatment effect.

[0100] The providing unit can adjust the method of providing information based on the patient's genetic information, reproductive medical data, statistical data, and the patient's emotional state. For example, when the patient is feeling stressed, the providing unit can provide simple and easy-to-understand information. When the patient is relaxed, the providing unit can provide detailed information. Furthermore, when the patient is feeling anxious, the providing unit can provide information that gives a sense of security. In this way, the providing unit can adjust the method of providing information according to the patient's emotional state and provide more appropriate information.

[0101] The collection unit can collect data on the patient's social background in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the collection unit can collect data such as the patient's occupation, income, and education level, and use it for analysis. The collection unit can also collect data on the patient's home environment and social support network, and use it for analysis. Furthermore, the collection unit can identify how these social backgrounds affect the cause of the patient's infertility and reflect them in the treatment plan. This allows the collection unit to collect data that takes social backgrounds into account and propose a more comprehensive treatment plan.

[0102] The analysis unit can adjust the way the analysis is presented based on the patient's genetic information, reproductive medical data, statistical data, and the patient's emotional state. For example, if the patient is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the patient is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the patient is anxious, the analysis unit can provide analysis results that give a sense of security. This allows the analysis unit to adjust the way the analysis is presented according to the patient's emotional state and provide more appropriate analysis results.

[0103] The proposal unit can propose a treatment plan taking into consideration data on the patient's living environment in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the proposal unit can collect environmental data (air quality, water quality, noise level, etc.) of the patient's place of residence and use it for analysis. The proposal unit can also collect data on the patient's working environment and living environment and reflect it in the treatment plan. Furthermore, the proposal unit can identify how these environmental factors affect the cause of the patient's infertility and reflect it in the treatment plan. This allows the proposal unit to propose a treatment plan that takes environmental factors into consideration and maximize the treatment effect.

[0104] The providing unit can determine the priority of information provision based on the patient's genetic information, reproductive medical data, statistical data, and the patient's emotional state. For example, if the patient is feeling stressed, the providing unit can provide information related to stress reduction with priority. Also, if the patient is relaxed, the providing unit can provide overall health information in a balanced manner. Furthermore, if the patient is feeling anxious, the providing unit can provide information related to anxiety reduction with priority. In this way, the providing unit can determine the priority of information provision according to the patient's emotional state and provide more appropriate information.

[0105] The collection unit can collect data from the patient's digital devices in addition to the patient's genetic information, reproductive medical data, and statistical data. For example, the collection unit can collect data such as daily activity levels, sleep patterns, and heart rates from the patient's smartphone or wearable device, and use the data for analysis. The collection unit can also collect data from the patient's digital devices in real time and reflect the data in treatment plans. Furthermore, the collection unit can analyze the data from these digital devices and comprehensively grasp the patient's health condition. This allows the collection unit to utilize the data from the digital devices to propose more accurate treatment plans.

[0106] The analysis unit can determine the priority of analysis based on the patient's genetic information, reproductive medical data, statistical data, and the patient's emotional state. For example, if the patient is feeling stressed, the analysis unit can prioritize analysis of stress-related data. If the patient is relaxed, the analysis unit can also analyze overall health data in a balanced manner. If the patient is feeling anxious, the analysis unit can also prioritize analysis of anxiety-related data. This allows the analysis unit to determine the priority of analysis according to the patient's emotional state and perform more appropriate analysis.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The collection department collects the patient’s genetic information, reproductive medicine data, and statistical data. For example, the patient’s genetic information is collected as DNA sequence data and gene mutation information, reproductive medicine data is collected as hormone levels and sperm / egg quality, and statistical data is collected as demographics and treatment success rate. Step 2: The analysis department analyzes the data collected by the collection department and identifies the cause of the patient's infertility. For example, the analysis combines knowledge of genetics, reproductive medicine, and statistics to identify genetic factors from genetic information and physiological factors from reproductive medicine data. In addition, statistical data is used to comprehensively grasp the cause of the patient's infertility. Step 3: The proposal unit proposes a treatment plan based on the analysis results obtained by the analysis unit. For example, it builds a model that predicts the success rate of treatment based on past treatment data and statistical data, and predicts how effective a specific treatment will be for a specific patient. It also proposes an optimal treatment plan based on the patient's medical history and lifestyle. Step 4: The provider provides information on treatment options based on the treatment plan proposed by the suggester, such as the type of treatment, its effectiveness, and side effects, and provides the type of treatment, such as drug therapy, surgery, and lifestyle changes. It also provides information on the effectiveness and side effects of the treatment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and provision 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 collects genetic information and reproductive medicine data of the patient using the camera 42 and microphone 38B of the smart device 14, and collects statistical data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the cause of the patient's infertility. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a treatment plan based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides information on treatment options and options. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and provision 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 collects genetic information and reproductive medicine data of the patient using the camera 42 and microphone 238 of the smart glasses 214, and collects statistical data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the cause of the patient's infertility. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a treatment plan based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides information on treatment options and options. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and provision 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 collects genetic information and reproductive medicine data of the patient using the camera 42 and microphone 238 of the headset type terminal 314, and collects statistical data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the cause of the patient's infertility. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a treatment plan based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides information on treatment options and options. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects genetic information and reproductive medicine data of the patient using the camera 42 and microphone 238 of the robot 414, and collects statistical data by the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to identify the cause of the patient's infertility. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes a treatment plan based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides information on treatment options and options. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Appendix 1) a collection department for collecting genetic information, reproductive medical data, and statistical data of patients; An analysis unit that analyzes the data collected by the collection unit and identifies a cause of infertility in a patient; a proposal unit that proposes a treatment plan based on the analysis result obtained by the analysis unit; A providing unit that provides information regarding treatment choices or options based on the treatment plan proposed by the suggesting unit. A system characterized by: (Appendix 2) The collecting unit includes: Gathering information about a patient's medical history or lifestyle 2. The system of claim 1, (Appendix 3) The analysis unit is Combining knowledge of genetics, reproductive medicine, and statistics to identify the causes of infertility in patients 2. The system of claim 1, (Appendix 4) The suggestion unit, Build models to predict treatment success rates based on past treatment data or statistical data 2. The system of claim 1, (Appendix 5) The providing unit is Providing information on types of treatment, their effects, side effects, etc. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Inferring patient emotions and adjusting the timing of data collection based on the inferred patient emotions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze patients' past medical history and choose the most appropriate data collection method 2. The system of claim 1, (Appendix 8) The collecting unit includes: Filter data collection based on the patient's current health and lifestyle habits 2. The system of claim 1, (Appendix 9) The collecting unit includes: Estimate patient emotions and prioritize data collection based on the estimated patient emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, consider the geographic location of the patient to prioritize collecting the most relevant data 2. The system of claim 1, (Appendix 11) The collecting unit includes: During data collection, analyze patients' social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The analysis unit is Infer patient emotion and adjust analysis presentation based on the inferred emotion 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 15) The analysis unit is Inferring patient sentiment and adjusting the length of the analysis based on the estimated patient sentiment 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The suggestion unit, Inferring the patient's emotions and adjusting the way the suggestions are presented based on the inferred patient emotions 2. The system of claim 1, (Appendix 19) The suggestion unit, When making a recommendation, adjust the level of detail in the recommendation based on the importance of the treatment 2. The system of claim 1, (Appendix 20) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of treatment. 2. The system of claim 1, (Appendix 21) The suggestion unit, Estimating patient sentiment and adjusting the length of suggestions based on the estimated patient sentiment 2. The system of claim 1, (Appendix 22) The suggestion unit, At the time of submission, proposals will be prioritized based on when the treatment was submitted. 2. The system of claim 1, (Appendix 23) The suggestion unit, When making suggestions, adjust the order of suggestions based on the relevance of the treatment 2. The system of claim 1, (Appendix 24) The providing unit is Infer the patient's feelings and adjust the way information is provided based on the inferred feelings 2. The system of claim 1, (Appendix 25) The providing unit is When providing information, provide the most appropriate information by referring to the patient's past medical history 2. The system of claim 1, (Appendix 26) The providing unit is When providing information, tailor the information delivery method based on the patient's current health status 2. The system of claim 1, (Appendix 27) The providing unit is Estimate the patient's feelings and determine the priority of providing information based on the estimated patient's feelings 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, provide the most appropriate information by taking into account the patient's geographic location 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, analyze the patient's social media activity to suggest a means of providing the information. 2. The system of claim 1, [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department for collecting genetic information, reproductive medical data, and statistical data of patients; An analysis unit that analyzes the data collected by the collection unit and identifies a cause of infertility in a patient; a proposal unit that proposes a treatment plan based on the analysis result obtained by the analysis unit; A providing unit that provides information regarding treatment choices or options based on the treatment plan proposed by the suggesting unit. A system characterized by:

2. The collecting unit includes: Gathering information about a patient's medical history or lifestyle 2. The system of claim 1 .

3. The analysis unit is Combining knowledge of genetics, reproductive medicine, and statistics to identify the causes of infertility in patients 2. The system of claim 1 .

4. The suggestion unit, Build models to predict treatment success rates based on past treatment data or statistical data 2. The system of claim 1 .

5. The providing unit is Providing information on types of treatment, their effects, side effects, etc.

2. The system of claim 1 .

6. The collecting unit includes: Inferring patient emotions and adjusting the timing of data collection based on the inferred patient emotions 2. The system of claim 1 .

7. The collecting unit includes: Analyze patients' past medical history and choose the most appropriate data collection method 2. The system of claim 1 .

8. The collecting unit includes: Filter data collection based on the patient's current health and lifestyle habits 2. The system of claim 1 .

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