System

Abstract

To provide a system capable of proposing optimal timing therapy without the need for hospital visits.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects genetic data. The analysis unit analyzes the genetic data collected by the collection unit. The proposal unit proposes a timing therapy based on the analysis result obtained by the analysis unit.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, infertility treatment required patients to visit a hospital, making it difficult for dual-income households to adjust their schedules.

[0005] The system according to the embodiment aims to propose optimal timing therapy without the need for hospital visits. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects genetic data. The analysis unit analyzes the genetic data collected by the collection unit. The proposal unit proposes a timing therapy based on the analysis result obtained by the analysis unit. Effect of the Invention

[0007] The system according to the embodiment can suggest optimal timing therapy without the need for hospital visits. [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 for utilizing a generation AI to make infertility treatment more efficient and address the declining population in Japan. This infertility treatment support system collects genetic data of patients and inputs it into the generation AI. The generation AI analyzes the input genetic data and proposes optimal timing therapy. This allows patients to proceed with treatment at home without visiting a hospital. Next, the generation AI identifies genes for increasing the implantation rate during in vitro fertilization or intracytoplasmic sperm injection. Furthermore, the generation AI analyzes genetic data with the aim of giving birth with a low risk of disability. This makes it possible to give birth to a healthy child with a low risk of disability. For example, the patient's genetic data is collected and input into the generation AI. The generation AI analyzes the input genetic data and proposes optimal timing therapy. For example, it is possible to propose optimal intercourse timing taking into account the timing of ovulation and hormone fluctuations. This allows patients to proceed with treatment at home without visiting a hospital. Next, the generation AI identifies genes for increasing the implantation rate during in vitro fertilization or intracytoplasmic sperm injection. Specifically, it analyzes the genetic data of the embryo and proposes optimal conditions for increasing the implantation rate. For example, by adjusting the expression level of certain genes and the balance of hormones, the implantation rate can be improved. Furthermore, the generative AI analyzes genetic data with the aim of achieving births with a low risk of disability. Specifically, it detects genetic mutations and abnormalities and provides information for selecting embryos with a low risk. This makes it possible to give birth to healthy children with a low risk of disability. In this way, the use of generative AI is expected to improve the efficiency and success rate of infertility treatment. Patients can proceed with treatment at home without having to go to the hospital, making it easier for dual-income households to receive treatment. In addition, by improving the implantation rate and achieving births with a low risk of disability, it will help address Japan's declining population. This allows the infertility treatment support system to analyze patients' genetic data and propose optimal treatment methods.

[0029] The infertility treatment support system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects genetic data of a patient. The genetic data includes, for example, DNA sequence data, RNA sequence data, and the like, but is not limited to such examples. The collection unit, for example, collects a blood sample of the patient and extracts genetic data. The collection unit can also collect genetic data using a saliva sample. For example, the collection unit collects the patient's saliva with a dedicated kit and uses it as a sample for analyzing the genetic data. Furthermore, the collection unit can also collect the patient's skin cells and collect the genetic data. For example, the collection unit collects the skin cells with a cotton swab and uses it as a sample for analyzing the genetic data. The analysis unit uses a generation AI to analyze the genetic data collected by the collection unit. The analysis is performed, for example, based on a gene mutation detection method and a data analysis algorithm, but is not limited to such examples. For example, the analysis unit uses a generation AI to detect a mutation in the genetic data. The analysis unit can also detect an abnormality in the genetic data using the generation AI. Furthermore, the analysis unit can also analyze the expression level of the genetic data using the generation AI. For example, the analysis unit uses the generation AI to analyze the expression level of a specific gene and propose a treatment method based on the result. The proposal unit uses the generation AI to propose an optimal timing therapy based on the analysis result obtained by the analysis unit. The proposal is made, for example, based on the timing of ovulation and the measurement method of hormone levels, but is not limited to such an example. For example, the proposal unit uses the generation AI to identify the timing of ovulation and propose the optimal timing of intercourse. The proposal unit can also use the generation AI to analyze fluctuations in hormone levels and propose the optimal treatment timing. Furthermore, the proposal unit can also use the generation AI to propose the optimal treatment method based on the patient's lifestyle. For example, the proposal unit takes into account the patient's lifestyle and stress level and proposes the optimal treatment timing. As a result, the infertility treatment support system according to the embodiment can be expected to improve the efficiency and success rate of infertility treatment by collecting and analyzing the patient's genetic data and proposing the optimal timing therapy.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 make a proposal using an AI model that inputs the analysis results obtained by the analyzing unit and proposes the optimal timing therapy.

[0030] The collecting unit collects genetic data of the patient. The genetic data includes, for example, DNA sequence data, RNA sequence data, and the like, but is not limited to such examples. For example, the collecting unit collects a blood sample of the patient and extracts genetic data. Specifically, the blood sample is collected using a dedicated blood collection kit, and the collected blood is refrigerated and stored. After that, DNA and RNA are extracted in a specialized laboratory, and sequence analysis is performed. The collecting unit can also collect genetic data using a saliva sample. For example, the collecting unit collects saliva of the patient using a dedicated kit and uses it as a sample for analyzing genetic data. Since the saliva sample can be easily collected by the patient at home, it is non-invasive and can reduce the burden on the patient. Furthermore, the collecting unit can collect skin cells of the patient and collect genetic data. For example, the collecting unit collects skin cells with a cotton swab and uses them as a sample for analyzing genetic data. Since the collection of skin cells is simple and does not require special equipment, it can be performed quickly. This allows the collecting unit to collect genetic data from various samples and comprehensively grasp the genetic information of the patient. Furthermore, the collection unit centrally manages these data and allows the analysis unit and proposal unit to access them. For example, the collected genetic data is stored in a cloud server and can be accessed by the analysis unit in real time. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit uses the generation AI to analyze the genetic data collected by the collection unit. The analysis is performed, for example, based on a gene mutation detection method and a data analysis algorithm, but is not limited to such examples. Specifically, the generation AI analyzes the genetic data using a deep learning model to detect gene mutations and abnormalities with high accuracy. For example, the analysis unit uses the generation AI to detect mutations in the genetic data. The generation AI has the ability to learn a huge genetic data set and identify specific gene mutation patterns. The analysis unit can also use the generation AI to detect abnormalities in the genetic data. The generation AI compares normal genetic data with abnormal genetic data to identify abnormal patterns. Furthermore, the analysis unit can also analyze the expression level of the genetic data using the generation AI. For example, the analysis unit uses the generation AI to analyze the expression level of a specific gene and propose a treatment method based on the results. The generation AI can analyze the gene expression data and quantitatively evaluate the degree to which a specific gene is expressed. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the patient's genetic information in detail. In addition, the analysis unit can also use past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past genetic data, it can predict how a particular genetic mutation will progress and evaluate future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows 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 suggestion unit uses the generation AI to suggest an optimal timing therapy based on the analysis results obtained by the analysis unit. The suggestion is made, for example, based on the timing of ovulation and the method of measuring hormone levels, but is not limited to such examples. Specifically, the suggestion unit uses the generation AI to identify the timing of ovulation and suggest the optimal timing of intercourse. The generation AI analyzes the hormone level and body temperature data of the patient and predicts the optimal timing of ovulation. The suggestion unit can also use the generation AI to analyze fluctuations in hormone levels and suggest the optimal treatment timing. The generation AI analyzes the hormone data of the patient and identifies the fluctuation pattern of the hormone level to suggest the optimal treatment timing. Furthermore, the suggestion unit can also use the generation AI to suggest the optimal treatment method based on the patient's lifestyle. For example, the suggestion unit takes into account the patient's lifestyle and stress level and suggests the optimal treatment timing. The generation AI can analyze the patient's lifestyle data and stress level data and individually suggest the optimal treatment method. As a result, the suggestion unit can suggest the optimal treatment method according to the individual situation of the patient, thereby improving the efficiency and success rate of infertility treatment. Furthermore, the suggestion unit can continually revise the proposal content based on data updated in real time to respond to the latest situation. For example, if there is a sudden change in hormone levels or body temperature, the suggestion unit immediately incorporates the new data and updates the proposal content. The suggestion unit can also provide more accurate treatment proposals by taking into account regional characteristics and past treatment history. This allows the suggestion unit to always provide highly accurate treatment proposals based on the latest information and support rapid and appropriate responses.

[0033] The analysis unit can identify genes for increasing the implantation rate during in vitro fertilization or intracytoplasmic sperm injection. The analysis unit, for example, analyzes genetic data of an embryo and proposes optimal conditions for increasing the implantation rate. For example, the analysis unit can improve the implantation rate by adjusting the expression level of a specific gene or the balance of hormones. The analysis unit can also use the generation AI to analyze the genetic data of an embryo and identify genes for increasing the implantation rate. For example, the analysis unit can use the generation AI to analyze the genetic data of an embryo and identify specific genetic markers. The analysis unit can also use the generation AI to analyze the genetic data of an embryo and propose optimal conditions for increasing the implantation rate. For example, the analysis unit can use the generation AI to analyze the genetic data of an embryo and improve the implantation rate by adjusting the expression level of a specific gene. This improves the success rate of in vitro fertilization or intracytoplasmic sperm injection by identifying genes for increasing the implantation rate. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input genetic data of an embryo into the generation AI and have the generation AI identify genes that will increase the implantation rate.

[0034] The analysis unit can provide information for detecting gene mutations and abnormalities and selecting an embryo with a low risk of injury. The analysis unit can, for example, detect gene mutations and abnormalities and provide information for selecting an embryo with a low risk of injury. For example, the analysis unit detects a specific gene mutation or chromosomal abnormality and selects an embryo based on the result. The analysis unit can also use the generation AI to detect gene mutations and abnormalities and provide information for selecting an embryo with a low risk of injury. For example, the analysis unit can use the generation AI to detect gene mutations and abnormalities and select an embryo based on the result. The analysis unit can also use the generation AI to detect gene mutations and abnormalities and provide information for selecting an embryo with a low risk of injury. For example, the analysis unit can use the generation AI to detect gene mutations and abnormalities and select an embryo based on the result. As a result, by providing information for selecting an embryo with a low risk of injury, it becomes possible to give birth to a healthy child. Some 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 causes the generation AI to detect gene mutations and abnormalities and selects an embryo based on the result.

[0035] The suggestion unit can suggest the timing of sexual intercourse taking into consideration the timing of ovulation and hormone fluctuations. The suggestion unit, for example, suggests the optimal timing of sexual intercourse taking into consideration the timing of ovulation and hormone fluctuations. For example, the suggestion unit uses the generation AI to identify the timing of ovulation and suggest the optimal timing of sexual intercourse. The suggestion unit can also use the generation AI to analyze fluctuations in hormone levels and suggest the optimal timing of sexual intercourse. Furthermore, the suggestion unit can also use the generation AI to suggest the optimal timing of sexual intercourse based on the lifestyle of the patient. For example, the suggestion unit considers the lifestyle and stress level of the patient and suggests the optimal timing of sexual intercourse. As a result, the success rate of the timing therapy is improved by suggesting the optimal timing of sexual intercourse. Some 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 causes the generation AI to execute an analysis of the timing of ovulation and hormone fluctuations, and suggests the timing of sexual intercourse based on the results.

[0036] The collection unit can analyze the patient's past genetic data collection history and select the optimal collection method. The collection unit, for example, analyzes the patient's past genetic data collection history and selects the optimal collection method. For example, the collection unit preferentially selects a collection method that has been successful in the past. The collection unit can also select the most efficient collection method from the past collection history. Furthermore, the collection unit can propose the optimal collection method for the patient based on the past collection history. For example, the collection unit analyzes the past collection history and selects the optimal collection method. As a result, efficient collection of genetic data is possible by selecting the optimal collection method based on the past collection history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the past genetic data collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0037] The collection unit can perform filtering based on the patient's current health condition and lifestyle habits when collecting genetic data. For example, the collection unit performs filtering based on the patient's current health condition and lifestyle habits when collecting genetic data. For example, the collection unit collects genetic data when the patient's health condition is good. The collection unit can also select the optimal collection timing based on the patient's lifestyle habits. Furthermore, the collection unit can filter the collected data taking into account the patient's health condition and lifestyle habits. For example, the collection unit collects genetic data when the patient's health condition is good. As a result, more accurate genetic data can be collected by filtering the data taking into account the patient's health condition and lifestyle habits. A 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 data on the patient's health condition and lifestyle habits to the generation AI and cause the generation AI to perform filtering.

[0038] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient when collecting genetic data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the patient when collecting genetic data. For example, when the patient is in a specific area, the collection unit preferentially collects genetic data related to the area. The collection unit can also select an optimal collection point based on the geographical location information of the patient. Furthermore, the collection unit can also determine the priority of collected data in consideration of the geographical location information of the patient. For example, when the patient is in a specific area, the collection unit preferentially collects genetic data related to the area. As a result, more relevant genetic data can be collected by collecting data in consideration of the geographical location information. 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 geographical location information of the patient to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the social media activity of the patient at the time of collecting the genetic data and collect related data. For example, the collection unit analyzes the social media activity of the patient at the time of collecting the genetic data and collects related data. For example, the collection unit collects data related to the health condition from the social media activity of the patient. The collection unit can also select the optimal collection timing based on the social media activity of the patient. Furthermore, the collection unit can analyze the social media activity of the patient and determine the priority of the collected data. For example, the collection unit collects data related to the health condition from the social media activity of the patient. In this way, data related to the health condition of the patient can be collected by analyzing the social media activity. 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 collect related data.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the genetic data during the analysis. For example, the analysis unit performs a detailed analysis on important genetic data. The analysis unit can also perform a concise analysis on genetic data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the genetic data. For example, the analysis unit performs a detailed analysis on important genetic data. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the genetic data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the genetic data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of genetic data during analysis. The analysis unit applies different analysis algorithms depending on the category of genetic data during analysis, for example. For example, the analysis unit selects an optimal analysis algorithm depending on the category of genetic data. The analysis unit can also apply different analysis algorithms for each category to improve accuracy. Furthermore, the analysis unit can adjust the analysis algorithm based on the category of genetic data. For example, the analysis unit selects an optimal analysis algorithm depending on the category of genetic data. As a result, the analysis accuracy is improved by applying the optimal analysis algorithm depending on the category of genetic data. 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 input the category of genetic data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] The analysis unit can determine the analysis priority based on the collection time of the genetic data during the analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the genetic data during the analysis. For example, the analysis unit preferentially analyzes the latest genetic data. The analysis unit can also analyze the latest data while referring to past genetic data. Furthermore, the analysis unit can also determine the analysis priority based on the collection time of the genetic data. For example, the analysis unit preferentially analyzes the latest genetic data. As a result, efficient analysis is possible by determining the analysis priority based on the collection time of the genetic data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the collection time of the genetic data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the genetic data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the genetic data during analysis. For example, the analysis unit preferentially analyzes genetic data with high relevance. The analysis unit can also postpone genetic data with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the genetic data. For example, the analysis unit preferentially analyzes genetic data with high relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the genetic data. 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 input the relevance of the genetic data to the generation AI and cause the generation AI to adjust the order of analysis.

[0044] The suggestion unit can adjust the level of detail of the proposal based on the importance of the genetic data at the time of proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the genetic data at the time of proposal. For example, the suggestion unit makes a detailed proposal for important genetic data. In addition, the suggestion unit can also make a concise proposal for genetic data with low importance. Furthermore, the suggestion unit can determine the priority of the proposal based on the importance of the genetic data. For example, the suggestion unit makes a detailed proposal for important genetic data. As a result, efficient proposals can be made by adjusting the level of detail of the proposal based on the importance of the genetic data. 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 input the importance of the genetic data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0045] The suggestion unit can apply different proposal algorithms according to the category of genetic data at the time of proposal. For example, the suggestion unit applies different proposal algorithms according to the category of genetic data at the time of proposal. For example, the suggestion unit selects an optimal proposal algorithm according to the category of genetic data. The suggestion unit can also apply different proposal algorithms for each category to improve accuracy. Furthermore, the suggestion unit can adjust the proposal algorithm based on the category of genetic data. For example, the suggestion unit selects an optimal proposal algorithm according to the category of genetic data. As a result, the proposal accuracy is improved by applying the optimal proposal algorithm according to the category of genetic data. 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 input the category of genetic data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0046] The suggestion unit can determine the priority of the proposal based on the collection time of the genetic data at the time of the proposal. The suggestion unit, for example, determines the priority of the proposal based on the collection time of the genetic data at the time of the proposal. For example, the suggestion unit makes a proposal based on the latest genetic data. The suggestion unit can also make a proposal based on the latest data while referring to past genetic data. Furthermore, the suggestion unit can also determine the priority of the proposal based on the collection time of the genetic data. For example, the suggestion unit makes a proposal based on the latest genetic data. As a result, efficient proposals can be made by determining the priority of the proposal based on the collection time of the genetic data. 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 input the collection time of the genetic data to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0047] The suggestion unit can adjust the order of the proposal based on the relevance of the genetic data at the time of proposal. For example, the suggestion unit adjusts the order of the proposal based on the relevance of the genetic data at the time of proposal. For example, the suggestion unit makes a proposal based on highly relevant genetic data. The suggestion unit can also postpone less relevant genetic data. Furthermore, the suggestion unit can adjust the order of the proposal based on the relevance of the genetic data. For example, the suggestion unit makes a proposal based on highly relevant genetic data. This enables efficient proposal by adjusting the order of the proposal based on the relevance of the genetic data. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the relevance of the genetic data to the generation AI and cause the generation AI to adjust the order of the proposal.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can make suggestions for improving the patient's lifestyle and diet based on the results of the genetic data analysis. For example, if there is a specific genetic mutation, a diet containing nutrients corresponding to that mutation can be suggested. The analysis unit can also analyze exercise aptitude from the genetic data and provide an optimal exercise plan. Furthermore, the analysis unit can suggest stress management methods based on the genetic data. In this way, by making suggestions for improving lifestyle habits based on the genetic data, the overall health of the patient can be improved.

[0050] The analysis unit can make suggestions for improving the patient's lifestyle and diet based on the results of the genetic data analysis. For example, if there is a specific genetic mutation, a diet containing nutrients corresponding to that mutation can be suggested. The analysis unit can also analyze exercise aptitude from the genetic data and provide an optimal exercise plan. Furthermore, the analysis unit can suggest stress management methods based on the genetic data. In this way, by making suggestions for improving lifestyle habits based on the genetic data, the overall health of the patient can be improved.

[0051] The analysis unit can make suggestions for improving the patient's lifestyle and diet based on the results of the genetic data analysis. For example, if there is a specific genetic mutation, a diet containing nutrients corresponding to that mutation can be suggested. The analysis unit can also analyze exercise aptitude from the genetic data and provide an optimal exercise plan. Furthermore, the analysis unit can suggest stress management methods based on the genetic data. In this way, by making suggestions for improving lifestyle habits based on the genetic data, the overall health of the patient can be improved.

[0052] The process flow of the first embodiment will be briefly described below.

[0053] Step 1: The collection unit collects genetic data of the patient. The genetic data includes, for example, DNA sequence data, RNA sequence data, etc. The collection unit collects blood samples, saliva samples, skin cells, etc. of the patient and extracts the genetic data. Step 2: The analysis unit uses the generation AI to analyze the genetic data collected by the collection unit. The analysis includes detection of genetic mutations, detection of abnormalities in the genetic data, analysis of expression levels of the genetic data, etc. Step 3: The suggestion unit uses the generative AI to suggest the optimal timing therapy based on the analysis results obtained by the analysis unit. The suggestions include ovulation timing, methods for measuring hormone levels, and treatment methods based on the patient's lifestyle.

[0054] (Example 2) The infertility treatment support system according to the embodiment of the present invention is a system for utilizing a generation AI to make infertility treatment more efficient and address the declining population in Japan. This infertility treatment support system collects genetic data of patients and inputs it into the generation AI. The generation AI analyzes the input genetic data and proposes optimal timing therapy. This allows patients to proceed with treatment at home without visiting a hospital. Next, the generation AI identifies genes for increasing the implantation rate during in vitro fertilization or intracytoplasmic sperm injection. Furthermore, the generation AI analyzes genetic data with the aim of giving birth with a low risk of disability. This makes it possible to give birth to a healthy child with a low risk of disability. For example, the patient's genetic data is collected and input into the generation AI. The generation AI analyzes the input genetic data and proposes optimal timing therapy. For example, it is possible to propose optimal intercourse timing taking into account the timing of ovulation and hormone fluctuations. This allows patients to proceed with treatment at home without visiting a hospital. Next, the generation AI identifies genes for increasing the implantation rate during in vitro fertilization or intracytoplasmic sperm injection. Specifically, it analyzes the genetic data of the embryo and proposes optimal conditions for increasing the implantation rate. For example, by adjusting the expression level of certain genes and the balance of hormones, the implantation rate can be improved. Furthermore, the generative AI analyzes genetic data with the aim of achieving births with a low risk of disability. Specifically, it detects genetic mutations and abnormalities and provides information for selecting embryos with a low risk. This makes it possible to give birth to healthy children with a low risk of disability. In this way, the use of generative AI is expected to improve the efficiency and success rate of infertility treatment. Patients can proceed with treatment at home without having to go to the hospital, making it easier for dual-income households to receive treatment. In addition, by improving the implantation rate and achieving births with a low risk of disability, it will help address Japan's declining population. This allows the infertility treatment support system to analyze patients' genetic data and propose optimal treatment methods.

[0055] The infertility treatment support system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects genetic data of a patient. The genetic data includes, for example, DNA sequence data, RNA sequence data, and the like, but is not limited to such examples. The collection unit, for example, collects a blood sample of the patient and extracts genetic data. The collection unit can also collect genetic data using a saliva sample. For example, the collection unit collects the patient's saliva with a dedicated kit and uses it as a sample for analyzing the genetic data. Furthermore, the collection unit can also collect the patient's skin cells and collect the genetic data. For example, the collection unit collects the skin cells with a cotton swab and uses it as a sample for analyzing the genetic data. The analysis unit uses a generation AI to analyze the genetic data collected by the collection unit. The analysis is performed, for example, based on a gene mutation detection method and a data analysis algorithm, but is not limited to such examples. For example, the analysis unit uses a generation AI to detect a mutation in the genetic data. The analysis unit can also detect an abnormality in the genetic data using the generation AI. Furthermore, the analysis unit can also analyze the expression level of the genetic data using the generation AI. For example, the analysis unit uses the generation AI to analyze the expression level of a specific gene and propose a treatment method based on the result. The proposal unit uses the generation AI to propose an optimal timing therapy based on the analysis result obtained by the analysis unit. The proposal is made, for example, based on the timing of ovulation and the measurement method of hormone levels, but is not limited to such an example. For example, the proposal unit uses the generation AI to identify the timing of ovulation and propose the optimal timing of intercourse. The proposal unit can also use the generation AI to analyze fluctuations in hormone levels and propose the optimal treatment timing. Furthermore, the proposal unit can also use the generation AI to propose the optimal treatment method based on the patient's lifestyle. For example, the proposal unit takes into account the patient's lifestyle and stress level and proposes the optimal treatment timing. As a result, the infertility treatment support system according to the embodiment can be expected to improve the efficiency and success rate of infertility treatment by collecting and analyzing the patient's genetic data and proposing the optimal timing therapy.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 make a proposal using an AI model that inputs the analysis results obtained by the analyzing unit and proposes the optimal timing therapy.

[0056] The collecting unit collects genetic data of the patient. The genetic data includes, for example, DNA sequence data, RNA sequence data, and the like, but is not limited to such examples. For example, the collecting unit collects a blood sample of the patient and extracts genetic data. Specifically, the blood sample is collected using a dedicated blood collection kit, and the collected blood is refrigerated and stored. After that, DNA and RNA are extracted in a specialized laboratory, and sequence analysis is performed. The collecting unit can also collect genetic data using a saliva sample. For example, the collecting unit collects saliva of the patient using a dedicated kit and uses it as a sample for analyzing genetic data. Since the saliva sample can be easily collected by the patient at home, it is non-invasive and can reduce the burden on the patient. Furthermore, the collecting unit can collect skin cells of the patient and collect genetic data. For example, the collecting unit collects skin cells with a cotton swab and uses them as a sample for analyzing genetic data. Since the collection of skin cells is simple and does not require special equipment, it can be performed quickly. This allows the collecting unit to collect genetic data from various samples and comprehensively grasp the genetic information of the patient. Furthermore, the collection unit centrally manages these data and allows the analysis unit and proposal unit to access them. For example, the collected genetic data is stored in a cloud server and can be accessed by the analysis unit in real time. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0057] The analysis unit uses the generation AI to analyze the genetic data collected by the collection unit. The analysis is performed, for example, based on a gene mutation detection method and a data analysis algorithm, but is not limited to such examples. Specifically, the generation AI analyzes the genetic data using a deep learning model to detect gene mutations and abnormalities with high accuracy. For example, the analysis unit uses the generation AI to detect mutations in the genetic data. The generation AI has the ability to learn a huge genetic data set and identify specific gene mutation patterns. The analysis unit can also use the generation AI to detect abnormalities in the genetic data. The generation AI compares normal genetic data with abnormal genetic data to identify abnormal patterns. Furthermore, the analysis unit can also analyze the expression level of the genetic data using the generation AI. For example, the analysis unit uses the generation AI to analyze the expression level of a specific gene and propose a treatment method based on the results. The generation AI can analyze the gene expression data and quantitatively evaluate the degree to which a specific gene is expressed. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the patient's genetic information in detail. In addition, the analysis unit can also use past data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past genetic data, it can predict how a particular genetic mutation will progress and evaluate future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows 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.

[0058] The suggestion unit uses the generation AI to suggest an optimal timing therapy based on the analysis results obtained by the analysis unit. The suggestion is made, for example, based on the timing of ovulation and the method of measuring hormone levels, but is not limited to such examples. Specifically, the suggestion unit uses the generation AI to identify the timing of ovulation and suggest the optimal timing of intercourse. The generation AI analyzes the hormone level and body temperature data of the patient and predicts the optimal timing of ovulation. The suggestion unit can also use the generation AI to analyze fluctuations in hormone levels and suggest the optimal treatment timing. The generation AI analyzes the hormone data of the patient and identifies the fluctuation pattern of the hormone level to suggest the optimal treatment timing. Furthermore, the suggestion unit can also use the generation AI to suggest the optimal treatment method based on the patient's lifestyle. For example, the suggestion unit takes into account the patient's lifestyle and stress level and suggests the optimal treatment timing. The generation AI can analyze the patient's lifestyle data and stress level data and individually suggest the optimal treatment method. As a result, the suggestion unit can suggest the optimal treatment method according to the individual situation of the patient, thereby improving the efficiency and success rate of infertility treatment. Furthermore, the suggestion unit can continually revise the proposal content based on data updated in real time to respond to the latest situation. For example, if there is a sudden change in hormone levels or body temperature, the suggestion unit immediately incorporates the new data and updates the proposal content. The suggestion unit can also provide more accurate treatment proposals by taking into account regional characteristics and past treatment history. This allows the suggestion unit to always provide highly accurate treatment proposals based on the latest information and support rapid and appropriate responses.

[0059] The analysis unit can identify genes for increasing the implantation rate during in vitro fertilization or microinsemination. The analysis unit, for example, analyzes genetic data of an embryo and proposes optimal conditions for increasing the implantation rate. For example, the analysis unit can improve the implantation rate by adjusting the expression level of a specific gene or the balance of hormones. The analysis unit can also use the generation AI to analyze the genetic data of an embryo and identify genes for increasing the implantation rate. For example, the analysis unit can use the generation AI to analyze the genetic data of an embryo and identify specific genetic markers. The analysis unit can also use the generation AI to analyze the genetic data of an embryo and propose optimal conditions for increasing the implantation rate. For example, the analysis unit can use the generation AI to analyze the genetic data of an embryo and improve the implantation rate by adjusting the expression level of a specific gene. This improves the success rate of in vitro fertilization or microinsemination by identifying genes for increasing the implantation rate. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input genetic data of an embryo into the generation AI and have the generation AI identify genes that will increase the implantation rate.

[0060] The analysis unit can provide information for detecting gene mutations and abnormalities and selecting an embryo with a low risk of injury. The analysis unit can, for example, detect gene mutations and abnormalities and provide information for selecting an embryo with a low risk of injury. For example, the analysis unit detects a specific gene mutation or chromosomal abnormality and selects an embryo based on the result. The analysis unit can also use the generation AI to detect gene mutations and abnormalities and provide information for selecting an embryo with a low risk of injury. For example, the analysis unit can use the generation AI to detect gene mutations and abnormalities and select an embryo based on the result. The analysis unit can also use the generation AI to detect gene mutations and abnormalities and provide information for selecting an embryo with a low risk of injury. For example, the analysis unit can use the generation AI to detect gene mutations and abnormalities and select an embryo based on the result. As a result, by providing information for selecting an embryo with a low risk of injury, it becomes possible to give birth to a healthy child. Some 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 causes the generation AI to detect gene mutations and abnormalities and selects an embryo based on the result.

[0061] The suggestion unit can suggest the timing of sexual intercourse taking into consideration the timing of ovulation and hormone fluctuations. The suggestion unit, for example, suggests the optimal timing of sexual intercourse taking into consideration the timing of ovulation and hormone fluctuations. For example, the suggestion unit uses the generation AI to identify the timing of ovulation and suggest the optimal timing of sexual intercourse. The suggestion unit can also use the generation AI to analyze fluctuations in hormone levels and suggest the optimal timing of sexual intercourse. Furthermore, the suggestion unit can also use the generation AI to suggest the optimal timing of sexual intercourse based on the lifestyle of the patient. For example, the suggestion unit considers the lifestyle and stress level of the patient and suggests the optimal timing of sexual intercourse. As a result, the success rate of the timing therapy is improved by suggesting the optimal timing of sexual intercourse. Some 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 causes the generation AI to execute an analysis of the timing of ovulation and hormone fluctuations, and suggests the timing of sexual intercourse based on the results.

[0062] The collection unit can estimate the patient's emotion and adjust the collection timing of the genetic data based on the estimated emotion of the patient. The collection unit, for example, estimates the patient's emotion and adjusts the collection timing of the genetic data based on the estimated emotion of the patient. For example, if the patient is relaxed, the collection unit immediately collects the genetic data. Also, if the patient is stressed, the collection unit can also collect the genetic data during a time period when the patient can relax. Furthermore, if the patient is tired, the collection unit can also collect the genetic data after the patient has rested. For example, the collection unit collects the genetic data during a time period when the patient is relaxed. In this way, more appropriate genetic data can be collected by adjusting the collection timing according to the emotion of the patient. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the collection timing.

[0063] The collection unit can analyze the patient's past genetic data collection history and select the optimal collection method. The collection unit, for example, analyzes the patient's past genetic data collection history and selects the optimal collection method. For example, the collection unit preferentially selects a collection method that has been successful in the past. The collection unit can also select the most efficient collection method from the past collection history. Furthermore, the collection unit can propose the optimal collection method for the patient based on the past collection history. For example, the collection unit analyzes the past collection history and selects the optimal collection method. As a result, efficient collection of genetic data is possible by selecting the optimal collection method based on the past collection history. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the past genetic data collection history to the generation AI and cause the generation AI to select the optimal collection method.

[0064] The collection unit can perform filtering based on the patient's current health condition and lifestyle habits when collecting genetic data. For example, the collection unit performs filtering based on the patient's current health condition and lifestyle habits when collecting genetic data. For example, the collection unit collects genetic data when the patient's health condition is good. The collection unit can also select the optimal collection timing based on the patient's lifestyle habits. Furthermore, the collection unit can filter the collected data taking into account the patient's health condition and lifestyle habits. For example, the collection unit collects genetic data when the patient's health condition is good. As a result, more accurate genetic data can be collected by filtering the data taking into account the patient's health condition and lifestyle habits. A 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 data on the patient's health condition and lifestyle habits to the generation AI and cause the generation AI to perform filtering.

[0065] The collection unit can estimate the patient's emotion and determine the priority of the genetic data to be collected based on the estimated emotion of the patient. The collection unit, for example, estimates the patient's emotion and determines the priority of the genetic data to be collected based on the estimated emotion of the patient. For example, when the patient is relaxed, the collection unit preferentially collects important genetic data. In addition, when the patient is stressed, the collection unit can also prioritize data that can be easily collected. Furthermore, when the patient is tired, the collection unit can also prioritize data that is less burdensome to collect. For example, when the patient is relaxed, the collection unit preferentially collects important genetic data. In this way, important genetic data can be preferentially collected by determining the priority of data according to the emotion of the patient. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the emotion data of the patient to the generation AI and cause the generation AI to execute the determination of the priority of the collected data.

[0066] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the patient when collecting genetic data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the patient when collecting genetic data. For example, when the patient is in a specific area, the collection unit preferentially collects genetic data related to the area. The collection unit can also select an optimal collection point based on the geographical location information of the patient. Furthermore, the collection unit can also determine the priority of collected data in consideration of the geographical location information of the patient. For example, when the patient is in a specific area, the collection unit preferentially collects genetic data related to the area. As a result, more relevant genetic data can be collected by collecting data in consideration of the geographical location information. 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 geographical location information of the patient to the generation AI and cause the generation AI to collect highly relevant data.

[0067] The collection unit can analyze the social media activity of the patient at the time of collecting the genetic data and collect related data. For example, the collection unit analyzes the social media activity of the patient at the time of collecting the genetic data and collects related data. For example, the collection unit collects data related to the health condition from the social media activity of the patient. The collection unit can also select the optimal collection timing based on the social media activity of the patient. Furthermore, the collection unit can analyze the social media activity of the patient and determine the priority of the collected data. For example, the collection unit collects data related to the health condition from the social media activity of the patient. In this way, data related to the health condition of the patient can be collected by analyzing the social media activity. 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 collect related data.

[0068] The analysis unit can estimate the patient's emotion and adjust the expression method of the analysis based on the estimated emotion of the patient. The analysis unit, for example, estimates the patient's emotion and adjusts the expression method of the analysis based on the estimated emotion of the patient. For example, the analysis unit provides a detailed analysis result when the patient is relaxed. Also, the analysis unit can provide a concise analysis result when the patient is stressed. Furthermore, the analysis unit can provide a visually easy-to-understand analysis result when the patient is tired. For example, the analysis unit provides a detailed analysis result when the patient is relaxed. As a result, by adjusting the expression method of the analysis according to the emotion of the patient, it is possible to provide a more understandable analysis result. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic data during the analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the genetic data during the analysis. For example, the analysis unit performs a detailed analysis on important genetic data. The analysis unit can also perform a concise analysis on genetic data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the genetic data. For example, the analysis unit performs a detailed analysis on important genetic data. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the genetic data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the genetic data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of genetic data during analysis. The analysis unit applies different analysis algorithms depending on the category of genetic data during analysis, for example. For example, the analysis unit selects an optimal analysis algorithm depending on the category of genetic data. The analysis unit can also apply different analysis algorithms for each category to improve accuracy. Furthermore, the analysis unit can adjust the analysis algorithm based on the category of genetic data. For example, the analysis unit selects an optimal analysis algorithm depending on the category of genetic data. As a result, the analysis accuracy is improved by applying the optimal analysis algorithm depending on the category of genetic data. 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 input the category of genetic data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0071] The analysis unit can estimate the patient's emotion and adjust the length of the analysis based on the estimated patient's emotion. The analysis unit, for example, estimates the patient's emotion and adjusts the length of the analysis based on the estimated patient's emotion. For example, the analysis unit performs a detailed analysis when the patient is relaxed. Also, the analysis unit can perform a brief analysis when the patient is stressed. Furthermore, the analysis unit can perform an analysis that is completed in a short time when the patient is tired. For example, the analysis unit performs a detailed analysis when the patient is relaxed. This allows for adjusting the length of the analysis according to the patient's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0072] The analysis unit can determine the analysis priority based on the collection time of the genetic data during the analysis. The analysis unit, for example, determines the analysis priority based on the collection time of the genetic data during the analysis. For example, the analysis unit preferentially analyzes the latest genetic data. The analysis unit can also analyze the latest data while referring to past genetic data. Furthermore, the analysis unit can also determine the analysis priority based on the collection time of the genetic data. For example, the analysis unit preferentially analyzes the latest genetic data. As a result, efficient analysis is possible by determining the analysis priority based on the collection time of the genetic data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the collection time of the genetic data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the genetic data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the genetic data during analysis. For example, the analysis unit preferentially analyzes genetic data with high relevance. The analysis unit can also postpone genetic data with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the genetic data. For example, the analysis unit preferentially analyzes genetic data with high relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the genetic data. 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 input the relevance of the genetic data to the generation AI and cause the generation AI to adjust the order of analysis.

[0074] The suggestion unit can estimate the patient's emotion and adjust the way of expressing the proposal based on the estimated emotion of the patient. The suggestion unit, for example, estimates the patient's emotion and adjusts the way of expressing the proposal based on the estimated emotion of the patient. For example, the suggestion unit makes a detailed suggestion when the patient is relaxed. Also, the suggestion unit can make a concise suggestion when the patient is stressed. Furthermore, the suggestion unit can make a visually easy-to-understand suggestion when the patient is tired. For example, the suggestion unit makes a detailed suggestion when the patient is relaxed. This makes it possible to provide a more understandable suggestion by adjusting the way of expressing the suggestion according to the emotion of the patient. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the emotion data of the patient to the generation AI and cause the generation AI to adjust the way of expressing the suggestion.

[0075] The suggestion unit can adjust the level of detail of the proposal based on the importance of the genetic data at the time of proposal. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the genetic data at the time of proposal. For example, the suggestion unit makes a detailed proposal for important genetic data. In addition, the suggestion unit can also make a concise proposal for genetic data with low importance. Furthermore, the suggestion unit can determine the priority of the proposal based on the importance of the genetic data. For example, the suggestion unit makes a detailed proposal for important genetic data. As a result, efficient proposals can be made by adjusting the level of detail of the proposal based on the importance of the genetic data. 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 input the importance of the genetic data to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0076] The suggestion unit can apply different proposal algorithms according to the category of genetic data at the time of proposal. For example, the suggestion unit applies different proposal algorithms according to the category of genetic data at the time of proposal. For example, the suggestion unit selects an optimal proposal algorithm according to the category of genetic data. The suggestion unit can also apply different proposal algorithms for each category to improve accuracy. Furthermore, the suggestion unit can adjust the proposal algorithm based on the category of genetic data. For example, the suggestion unit selects an optimal proposal algorithm according to the category of genetic data. As a result, the proposal accuracy is improved by applying the optimal proposal algorithm according to the category of genetic data. 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 input the category of genetic data to the generation AI and cause the generation AI to apply the proposal algorithm.

[0077] The suggestion unit can estimate the patient's emotion and adjust the length of the suggestion based on the estimated patient's emotion. The suggestion unit, for example, estimates the patient's emotion and adjusts the length of the suggestion based on the estimated patient's emotion. For example, the suggestion unit makes a detailed suggestion when the patient is relaxed. Also, the suggestion unit can make a concise suggestion when the patient is stressed. Furthermore, the suggestion unit can make a suggestion that can be completed in a short time when the patient is tired. For example, the suggestion unit makes a detailed suggestion when the patient is relaxed. This allows a more appropriate suggestion to be provided by adjusting the length of the suggestion according to the patient's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0078] The suggestion unit can determine the priority of the proposal based on the collection time of the genetic data at the time of the proposal. The suggestion unit, for example, determines the priority of the proposal based on the collection time of the genetic data at the time of the proposal. For example, the suggestion unit makes a proposal based on the latest genetic data. The suggestion unit can also make a proposal based on the latest data while referring to past genetic data. Furthermore, the suggestion unit can also determine the priority of the proposal based on the collection time of the genetic data. For example, the suggestion unit makes a proposal based on the latest genetic data. As a result, efficient proposals can be made by determining the priority of the proposal based on the collection time of the genetic data. 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 input the collection time of the genetic data to the generation AI and cause the generation AI to execute the determination of the priority of the proposal.

[0079] The suggestion unit can adjust the order of the proposal based on the relevance of the genetic data at the time of proposal. For example, the suggestion unit adjusts the order of the proposal based on the relevance of the genetic data at the time of proposal. For example, the suggestion unit makes a proposal based on highly relevant genetic data. The suggestion unit can also postpone less relevant genetic data. Furthermore, the suggestion unit can adjust the order of the proposal based on the relevance of the genetic data. For example, the suggestion unit makes a proposal based on highly relevant genetic data. This enables efficient proposal by adjusting the order of the proposal based on the relevance of the genetic data. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the relevance of the genetic data to the generation AI and cause the generation AI to adjust the order of the proposal.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The suggestion unit can estimate the patient's emotions, monitor the progress of the treatment based on the estimated emotions, and adjust the treatment plan as necessary. For example, if the patient is feeling stressed, the suggestion unit can temporarily ease the progress of the treatment and provide a relaxing environment. Also, if the patient is relaxed, the progress of the treatment can be accelerated. Furthermore, if the patient is feeling anxious, the suggestion unit can suggest counseling or support. This makes it possible to improve the success rate of the treatment by flexibly adjusting the treatment plan according to the patient's emotions.

[0082] The analysis unit can make suggestions for improving the patient's lifestyle and diet based on the results of the genetic data analysis. For example, if there is a specific genetic mutation, a diet containing nutrients corresponding to that mutation can be suggested. The analysis unit can also analyze exercise aptitude from the genetic data and provide an optimal exercise plan. Furthermore, the analysis unit can suggest stress management methods based on the genetic data. In this way, by making suggestions for improving lifestyle habits based on the genetic data, the overall health of the patient can be improved.

[0083] The collection unit can estimate the patient's emotions and adjust the genetic data collection method based on the estimated emotions. For example, if the patient is relaxed, genetic data is collected in a non-invasive manner. Also, if the patient is stressed, the collection method can be changed to collect data in a more comfortable manner. Furthermore, if the patient is tired, collection can be postponed and collected again after resting. In this way, more accurate genetic data can be collected by adjusting the collection method according to the patient's emotions.

[0084] The suggestion unit can estimate the patient's emotions, monitor the progress of the treatment based on the estimated emotions, and adjust the treatment plan as necessary. For example, if the patient is feeling stressed, the suggestion unit can temporarily ease the progress of the treatment and provide a relaxing environment. Also, if the patient is relaxed, the progress of the treatment can be accelerated. Furthermore, if the patient is feeling anxious, the suggestion unit can suggest counseling or support. This makes it possible to improve the success rate of the treatment by flexibly adjusting the treatment plan according to the patient's emotions.

[0085] The analysis unit can make suggestions for improving the patient's lifestyle and diet based on the results of the genetic data analysis. For example, if there is a specific genetic mutation, a diet containing nutrients corresponding to that mutation can be suggested. The analysis unit can also analyze exercise aptitude from the genetic data and provide an optimal exercise plan. Furthermore, the analysis unit can suggest stress management methods based on the genetic data. In this way, by making suggestions for improving lifestyle habits based on the genetic data, the overall health of the patient can be improved.

[0086] The collection unit can estimate the patient's emotions and adjust the genetic data collection method based on the estimated emotions. For example, if the patient is relaxed, genetic data is collected in a non-invasive manner. Also, if the patient is stressed, the collection method can be changed to collect data in a more comfortable manner. Furthermore, if the patient is tired, collection can be postponed and collected again after resting. In this way, more accurate genetic data can be collected by adjusting the collection method according to the patient's emotions.

[0087] The suggestion unit can estimate the patient's emotions, monitor the progress of the treatment based on the estimated emotions, and adjust the treatment plan as necessary. For example, if the patient is feeling stressed, the suggestion unit can temporarily ease the progress of the treatment and provide a relaxing environment. Also, if the patient is relaxed, the progress of the treatment can be accelerated. Furthermore, if the patient is feeling anxious, the suggestion unit can suggest counseling or support. This makes it possible to improve the success rate of the treatment by flexibly adjusting the treatment plan according to the patient's emotions.

[0088] The analysis unit can make suggestions for improving the patient's lifestyle and diet based on the results of the genetic data analysis. For example, if there is a specific genetic mutation, a diet containing nutrients corresponding to that mutation can be suggested. The analysis unit can also analyze exercise aptitude from the genetic data and provide an optimal exercise plan. Furthermore, the analysis unit can suggest stress management methods based on the genetic data. In this way, by making suggestions for improving lifestyle habits based on the genetic data, the overall health of the patient can be improved.

[0089] The collection unit can estimate the patient's emotions and adjust the genetic data collection method based on the estimated emotions. For example, if the patient is relaxed, genetic data is collected in a non-invasive manner. Also, if the patient is stressed, the collection method can be changed to collect data in a more comfortable manner. Furthermore, if the patient is tired, collection can be postponed and collected again after resting. In this way, more accurate genetic data can be collected by adjusting the collection method according to the patient's emotions.

[0090] The suggestion unit can estimate the patient's emotions, monitor the progress of the treatment based on the estimated emotions, and adjust the treatment plan as necessary. For example, if the patient is feeling stressed, the suggestion unit can temporarily ease the progress of the treatment and provide a relaxing environment. Also, if the patient is relaxed, the progress of the treatment can be accelerated. Furthermore, if the patient is feeling anxious, the suggestion unit can suggest counseling or support. This makes it possible to improve the success rate of the treatment by flexibly adjusting the treatment plan according to the patient's emotions.

[0091] The process flow of the second embodiment will be briefly described below.

[0092] Step 1: The collection unit collects genetic data of the patient. The genetic data includes, for example, DNA sequence data, RNA sequence data, etc. The collection unit collects blood samples, saliva samples, skin cells, etc. of the patient and extracts the genetic data. Step 2: The analysis unit uses the generation AI to analyze the genetic data collected by the collection unit. The analysis includes detection of genetic mutations, detection of abnormalities in the genetic data, analysis of expression levels of the genetic data, etc. Step 3: The suggestion unit uses the generative AI to suggest the optimal timing therapy based on the analysis results obtained by the analysis unit. The suggestions include ovulation timing, methods for measuring hormone levels, and treatment methods based on the patient's lifestyle.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion 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 can collect genetic data of the patient using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the genetic data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal timing therapy based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion 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 can collect genetic data of the patient using the camera 42 and the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the genetic data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal timing therapy based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[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 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.

[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. 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.

[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 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.

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

[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 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.

[0128] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion 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 can collect genetic data of the patient using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the genetic data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal timing therapy based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[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 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.

[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 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).

[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] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect genetic data of the patient using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the genetic data using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal timing therapy based on the analysis result. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Appendix 1) A collection unit that collects genetic data; an analysis unit that analyzes the genetic data collected by the collection unit; A suggestion unit that proposes a timing therapy based on the analysis result obtained by the analysis unit. A system characterized by: (Appendix 2) The analysis unit is Identifying genes that increase implantation rates during in vitro fertilization and intracytoplasmic sperm injection 2. The system of claim 1, (Appendix 3) The analysis unit is Detecting genetic mutations and abnormalities and providing information for selecting embryos with a lower risk of defects 2. The system of claim 1, (Appendix 4) The suggestion unit, Suggesting sexual timing that takes into account ovulation timing and hormone fluctuations 2. The system of claim 1, (Appendix 5) The collecting unit includes: Inferring patient emotions and adjusting the timing of genetic data collection based on the inferred patient emotions 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the patient's past genetic data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: As genetic data is collected, it will be filtered based on the patient's current health and lifestyle habits. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Estimating patient emotions and prioritizing genetic data to be collected based on the estimated patient emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting genetic data, prioritize collection of relevant data by taking into account the geographic location of the patient. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Analyzing patients' social media activity and collecting relevant data as genetic data is collected 2. The system of claim 1, (Appendix 11) The analysis unit is Infer patient emotion and adjust analysis presentation based on the inferred emotion 2. The system of claim 1, (Appendix 12) The analysis unit is During analysis, the level of detail of the analysis is adjusted based on the importance of the genetic data 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of genetic data. 2. The system of claim 1, (Appendix 14) 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 15) The analysis unit is At the time of analysis, prioritize analysis based on when genetic data was collected 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the order of analysis is adjusted based on the relevance of the genetic data. 2. The system of claim 1, (Appendix 17) 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 18) The suggestion unit, When making suggestions, the level of detail of the suggestions is adjusted based on the importance of the genetic data. 2. The system of claim 1, (Appendix 19) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of gene data. 2. The system of claim 1, (Appendix 20) 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 21) The suggestion unit, At the time of submission, proposals will be prioritized based on when the genetic data was collected. 2. The system of claim 1, (Appendix 22) The suggestion unit, At suggestion time, the order of suggestions is adjusted based on the relevance of genetic data 2. The system of claim 1, [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects genetic data; an analysis unit that analyzes the genetic data collected by the collection unit; A suggestion unit that proposes a timing therapy based on the analysis result obtained by the analysis unit. A system characterized by:

2. The analysis unit is Identifying genes that increase implantation rates during in vitro fertilization and intracytoplasmic sperm injection 2. The system of claim 1 .

3. The analysis unit is Detecting genetic mutations and abnormalities and providing information for selecting embryos with a lower risk of defects 2. The system of claim 1 .

4. The suggestion unit, Suggesting the timing of sexual intercourse taking into account ovulation timing and hormone fluctuations 2. The system of claim 1 .

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

6. The collecting unit includes: Analyze the patient's past genetic data collection history and select the optimal collection method 2. The system of claim 1 .

7. The collecting unit includes: As genetic data is collected, it will be filtered based on the patient's current health and lifestyle habits.

2. The system of claim 1 .

8. The collecting unit includes: Estimating patient emotions and prioritizing genetic data to be collected based on the estimated patient emotions 2. The system of claim 1 .

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