system

Abstract

The system according to this embodiment aims to analyze gene expression data, predict disease mechanisms, and generate new research hypotheses. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a hypothesis generation unit, a treatment method proposal unit, and a report generation unit. The acquisition unit acquires gene expression data. The analysis unit analyzes the gene expression data acquired by the acquisition unit. The prediction unit predicts the disease mechanism based on the data analyzed by the analysis unit. The hypothesis generation unit generates a new research hypothesis based on the prediction results obtained by the prediction unit. The treatment method proposal unit proposes the optimal treatment method based on the hypothesis generated by the hypothesis generation unit. The report generation unit generates an experimental result as a report based on the treatment method proposed by the treatment method proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the process of predicting a disease mechanism based on gene expression data and generating a new research hypothesis was not sufficiently efficient.

[0005] The system according to the embodiment aims to analyze gene expression data, predict a disease mechanism, and generate a new research hypothesis.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a hypothesis generation unit, a treatment method proposal unit, and a report generation unit. The acquisition unit acquires gene expression data. The analysis unit analyzes the gene expression data acquired by the acquisition unit. The prediction unit predicts the disease mechanism based on the data analyzed by the analysis unit. The hypothesis generation unit generates a new research hypothesis based on the prediction results obtained by the prediction unit. The treatment method proposal unit proposes the optimal treatment method based on the hypothesis generated by the hypothesis generation unit. The report generation unit generates a report of the experimental results based on the treatment method proposed by the treatment method proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze gene expression data, predict disease mechanisms, and generate new research hypotheses. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The scientific discovery platform according to an embodiment of the present invention is a system that integrates generative AI and spatial transcriptomics technology. This system spatially visualizes the gene expression patterns of individual cells within tissue and uses generative AI to analyze and predict complex biological processes. This provides an innovative approach to elucidating the mechanisms of complex diseases such as cancer and neurodegenerative diseases, and to realizing personalized medicine. For example, the scientific discovery platform acquires comprehensive gene expression information from pathological tissue sections. Next, it uses generative AI to analyze the acquired gene expression data, analyze complex gene expression patterns, and create an intuitively understandable 3D visualization model. Furthermore, the generative AI learns in combination with a large-scale biological database to predict new gene interactions and disease mechanisms. Based on existing scientific literature and experimental data, the generative AI proposes new research hypotheses and presents the optimal experimental design to verify them. This significantly accelerates the research process and expands the possibilities for new scientific discoveries. Based on the gene expression profile of each patient, the generative AI proposes the optimal treatment method, automatically analyzes experimental results, and generates a report in the form of a scientific paper. This system will enable medical researchers, pharmaceutical company R&D departments, and biotechnology companies to provide innovative approaches to elucidating the mechanisms of complex diseases and realizing personalized medicine. For example, in cancer research, it is expected to significantly shorten and streamline the process of understanding heterogeneity within tumors, which currently takes an average of 5-10 years of research and millions of dollars in costs. In this way, the scientific discovery platform can provide innovative approaches to elucidating the mechanisms of complex diseases and realizing personalized medicine.

[0029] The scientific discovery platform according to this embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a hypothesis generation unit, a treatment proposal unit, and a report generation unit. The acquisition unit acquires gene expression data. The acquisition unit can acquire comprehensive gene expression information from pathological tissue sections using, for example, spatial transcriptomics technology. The acquisition unit can acquire gene expression data in various formats, such as RNA-Seq data and microarray data. The analysis unit analyzes the gene expression data acquired by the acquisition unit using a generation AI. The analysis unit can, for example, analyze complex gene expression patterns and create an intuitively understandable 3D visualization model. The analysis unit can use various analysis methods, such as data preprocessing and the application of analysis algorithms. The prediction unit predicts disease mechanisms based on the data analyzed by the analysis unit. The prediction unit can, for example, learn by combining it with a large-scale biological database to predict new gene interactions and disease mechanisms. The prediction unit can make predictions based on the model and evaluation criteria used. The hypothesis generation unit generates new research hypotheses based on the prediction results obtained by the prediction unit. The hypothesis generation unit proposes new research hypotheses based on existing scientific literature and experimental data, and presents the optimal experimental design for verifying them. The hypothesis generation unit can generate hypotheses based on the type of data used and the generation algorithm. The treatment proposal unit proposes the optimal treatment based on the hypotheses generated by the hypothesis generation unit. The treatment proposal unit proposes the optimal treatment based on the individual gene expression profile of each patient, for example. The treatment proposal unit can select treatments based on evaluation criteria for treatment effectiveness and the patient's condition. The report generation unit generates a report of the experimental results based on the treatment proposed by the treatment proposal unit. The report generation unit can automatically analyze the experimental results and generate a report in the format of a scientific paper, for example. The report generation unit can create a report based on the structure of the report and the information it contains. As a result, the scientific discovery platform according to the embodiment can efficiently perform a series of processes from gene expression data acquisition to analysis, prediction, hypothesis generation, treatment proposal, and report generation.

[0030] The acquisition unit acquires gene expression data. For example, the acquisition unit can acquire comprehensive gene expression information from pathological tissue sections using spatial transcriptomics technology. Spatial transcriptomics technology is a method for spatially analyzing gene expression within tissues, allowing for a detailed understanding of gene expression patterns in specific cells or tissues. This enables highly accurate analysis of abnormalities in pathological tissues and the progression of diseases. The acquisition unit can acquire gene expression data in various formats, such as RNA-Seq data and microarray data. RNA-Seq is a method that analyzes the entire transcriptome using next-generation sequencing technology and is effective for quantifying gene expression and discovering novel transcripts. Microarrays are a method that analyzes gene expression using probes based on known gene sequences, allowing for rapid acquisition of expression profiles for specific gene groups. By combining these technologies, the acquisition unit efficiently collects a wide range of gene expression data and provides a foundation for analysis. Furthermore, the acquisition unit also performs data quality control and preprocessing to ensure the reliability of the data provided to the analysis unit. For example, it performs noise reduction and normalization of the acquired data and converts it into a format suitable for analysis. This allows the acquisition unit to consistently perform everything from gene expression data collection to preprocessing, supporting the efficient data analysis of the analysis unit.

[0031] The analysis unit uses generative AI to analyze gene expression data acquired by the acquisition unit. Generative AI is an advanced analysis method based on deep learning technology, analyzing complex gene expression patterns and creating intuitively understandable 3D visualization models. Specifically, the generative AI performs data normalization and missing value imputation as preprocessing for the gene expression data. Next, it uses a deep learning model to cluster gene expression patterns and detect anomalies. For example, it uses an autoencoder to compress high-dimensional data to a lower dimension and extract features. Furthermore, the generative AI constructs interaction networks between genes and analyzes the correlations of gene expression. This allows for the identification of gene groups and pathways associated with specific diseases. In addition, the generative AI outputs the analysis results as a 3D visualization model, enabling researchers to intuitively understand the data. For example, it plots the gene expression clustering results in 3D space, visually showing the characteristics of each cluster. This allows the analysis unit to highly analyze the acquired gene expression data and provide it in an intuitively understandable format. Furthermore, the analysis unit shares the analysis results with other departments to support the prediction and hypothesis generation units. This allows the analysis unit to play a central role in data analysis and support the efficient operation of the entire scientific discovery platform.

[0032] The prediction unit predicts disease mechanisms based on data analyzed by the analysis unit. The prediction unit learns from, for example, large-scale biological databases to predict novel gene interactions and disease mechanisms. Specifically, the prediction unit acquires gene interaction network and pathway information from existing biological databases and integrates it with gene expression data provided by the analysis unit. Next, it uses machine learning algorithms to predict novel gene interactions and mechanisms related to disease. For example, it uses random forests or support vector machines to extract features of disease-related genes and construct a predictive model. Furthermore, the prediction unit uses deep learning models to analyze complex gene interaction networks and discover novel disease mechanisms. This enables the prediction unit to make highly accurate predictions of disease mechanisms based on data provided by the analysis unit. In addition, the prediction unit evaluates the prediction results and provides reliable predictions. For example, it uses cross-validation or bootstrap methods to evaluate the accuracy of the prediction model and select the optimal model. This allows the prediction unit to make highly reliable predictions of disease mechanisms and support the hypothesis generation unit and the treatment proposal unit.

[0033] The hypothesis generation unit generates new research hypotheses based on the prediction results obtained by the prediction unit. For example, the hypothesis generation unit proposes new research hypotheses based on existing scientific literature and experimental data, and presents the optimal experimental design for verifying them. Specifically, the hypothesis generation unit searches relevant scientific literature based on the disease mechanism prediction results provided by the prediction unit and compares them with existing knowledge. Next, it uses generation AI to integrate the prediction results and information from the scientific literature to generate new research hypotheses. For example, it uses natural language processing technology to extract relevant information from scientific literature and proposes a new hypothesis in combination with the prediction results. The hypothesis generation unit also presents the optimal experimental design for verifying the generated hypotheses. For example, it designs the purpose and conditions of the experiment, the reagents and equipment to be used in detail, and ensures the reproducibility and reliability of the experiment. In this way, the hypothesis generation unit can efficiently generate new research hypotheses based on the prediction results provided by the prediction unit and support the design of experiments. Furthermore, the hypothesis generation unit evaluates the validity of the generated hypotheses and provides highly reliable hypotheses. For example, it verifies the generated hypotheses by comparing them with existing experimental data and selects highly reliable hypotheses. This allows the hypothesis generation unit to efficiently generate and test new research hypotheses, thereby improving the overall research efficiency of the scientific discovery platform.

[0034] The Treatment Proposal Unit proposes the optimal treatment based on hypotheses generated by the Hypothesis Generation Unit. For example, the Treatment Proposal Unit proposes the optimal treatment based on the individual patient's gene expression profile. Specifically, the Treatment Proposal Unit analyzes the patient's gene expression data based on the research hypotheses provided by the Hypothesis Generation Unit and selects the optimal treatment from the perspective of personalized medicine. For example, it proposes the optimal drug or treatment based on specific gene mutations or expression patterns. The Treatment Proposal Unit also selects treatments based on evaluation criteria for treatment effectiveness and the patient's condition. For example, it uses a treatment effectiveness prediction model to simulate the effects of the proposed treatments and select the optimal treatment. Furthermore, the Treatment Proposal Unit provides the information necessary for implementing the proposed treatments and supports their implementation in clinical settings. For example, it provides detailed information on the procedures for implementing the treatments, precautions, and methods for managing side effects, enabling healthcare professionals to perform treatments appropriately. In this way, the Treatment Proposal Unit can propose the optimal treatment from the perspective of personalized medicine based on hypotheses provided by the Hypothesis Generation Unit and support their implementation in clinical settings. Furthermore, the Treatment Proposal Unit continuously monitors the effects of the proposed treatments and revises the treatments as needed. This allows the treatment proposal department to support the proposal and implementation of flexible treatment methods tailored to the patient's condition, thereby maximizing treatment effectiveness.

[0035] The report generation unit generates reports based on experimental results derived from treatment methods proposed by the treatment proposal unit. For example, the report generation unit automatically analyzes experimental results and generates reports in the format of scientific papers. Specifically, the report generation unit collects the results of treatment implementation provided by the treatment proposal unit, analyzes the data, and creates reports. For example, it analyzes data on treatment effectiveness and the occurrence of side effects to evaluate the efficacy and safety of the treatment methods. Furthermore, the report generation unit uses AI to automatically generate reports. For example, it uses natural language generation technology to create reports in the format of scientific papers based on the analysis results. This allows the report generation unit to quickly and accurately compile experimental results into reports. In addition, the report generation unit creates reports based on their structure and the information they contain. For example, it appropriately structures sections such as the purpose, background, methods, results, discussion, and conclusions, and creates reports in an easy-to-read format. This allows the report generation unit to create scientifically reliable reports based on information provided by the treatment proposal unit and provide them to researchers and medical professionals. Furthermore, the report generation unit continuously updates the report content to reflect the latest information. This allows the report generation department to always provide reports based on the latest information, supporting decision-making in research and medical settings.

[0036] The analysis unit can analyze complex gene expression patterns and create intuitively understandable 3D visualization models. For example, the analysis unit can analyze gene expression data and visualize the results as a 3D model. The analysis unit can create 3D models based on the software and visualization criteria used. For example, the analysis unit can analyze the expression fluctuations and correlations of specific genes and display them as a 3D model. The analysis unit can also simultaneously analyze the expression patterns of multiple genes and integrate them into a 3D model. This makes it easier for researchers to understand the data by visualizing gene expression patterns as intuitively understandable 3D models. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input gene expression data into a generative AI and have the generative AI generate a 3D visualization model.

[0037] The prediction unit can learn by combining it with a large-scale biological database and predict new gene interactions and disease mechanisms. For example, the prediction unit can use a large-scale biological database to predict gene interactions and disease mechanisms. The prediction unit can make predictions based on the name of the database and the method of data acquisition. For example, the prediction unit can predict the interaction of specific genes and elucidate disease mechanisms based on that. The prediction unit can also simultaneously predict the interaction of multiple genes and analyze disease mechanisms based on that. This makes it possible to make more accurate predictions by utilizing a large-scale database. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without generative AI. For example, the prediction unit can input data from a biological database into a generative AI and have the generative AI perform predictions of gene interactions and disease mechanisms.

[0038] The hypothesis generation unit can propose new research hypotheses based on existing scientific literature and experimental data, and present optimal experimental designs to verify them. For example, the hypothesis generation unit can refer to existing scientific literature and propose new research hypotheses. The hypothesis generation unit can refer to literature based on literature databases and search methods. For example, the hypothesis generation unit can propose a hypothesis regarding the function of a specific gene and present an experimental design to verify it. The hypothesis generation unit can also integrate multiple literatures to generate new hypotheses and create experimental designs based on them. This improves the efficiency and accuracy of research by proposing new research hypotheses and presenting optimal experimental designs. Some or all of the above-described processes in the hypothesis generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the hypothesis generation unit can input scientific literature and experimental data into a generation AI, and have the generation AI perform the generation of new hypotheses and present experimental designs.

[0039] The treatment proposal unit can propose the optimal treatment based on each patient's individual gene expression profile. For example, the treatment proposal unit can analyze the patient's gene expression profile and propose the optimal treatment based on it. The treatment proposal unit can select a treatment based on the method of creating the profile and the data used. For example, the treatment proposal unit can propose a treatment based on the expression pattern of a specific gene. The treatment proposal unit can also propose a treatment by integrating the expression profiles of multiple genes. This allows for the proposal of the optimal treatment for each patient, towards the realization of personalized medicine. Some or all of the above-described processes in the treatment proposal unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the treatment proposal unit can input the patient's gene expression profile into a generative AI and have the generative AI propose the optimal treatment.

[0040] The report generation unit can automatically analyze experimental results and generate reports in the format of scientific papers. For example, the report generation unit can analyze experimental results and generate those results as a report in the format of a scientific paper. The report generation unit can create reports based on the structure and information to be included. For example, the report generation unit can analyze specific experimental results in detail and compile those results into a report. The report generation unit can also integrate multiple experimental results to create a report. This reduces the burden on researchers and enables efficient reporting by automatically analyzing experimental results and generating reports. Some or all of the above processes in the report generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the report generation unit can input experimental result data into a generation AI and have the generation AI perform the report generation.

[0041] The data acquisition unit can analyze past gene expression data and select the optimal acquisition method. For example, the acquisition unit can identify the most accurate acquisition method from past data and prioritize its use. Based on past data, the acquisition unit can select the optimal acquisition method under specific conditions. Furthermore, the acquisition unit can analyze past data, find areas for improvement in the acquisition method, and optimize it. This improves the accuracy of data acquisition by utilizing past data to select the optimal acquisition method. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input past gene expression data into a generating AI and have the generating AI select the optimal acquisition method.

[0042] The data acquisition unit can filter gene expression data based on the state of the target tissue and environmental conditions. For example, the data acquisition unit can filter the data to be acquired based on the health status of the target tissue. The data acquisition unit can also filter the data to be acquired based on environmental conditions (temperature, humidity, etc.). Furthermore, the data acquisition unit can also filter the data to be acquired based on the growth stage of the target tissue. This allows for more accurate data acquisition by filtering the data based on the state of the target tissue and environmental conditions. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the state of the target tissue and environmental conditions into a generating AI and have the generating AI perform data filtering.

[0043] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring gene expression data, taking into account the geographical location information of the target tissue. For example, the acquisition unit can prioritize the acquisition of highly relevant data based on the geographical location information of the target tissue. The acquisition unit can prioritize the acquisition of data from a specific region based on geographical location information. Furthermore, the acquisition unit can also prioritize the acquisition of data suitable for environmental conditions, taking geographical location information into consideration. This allows for the efficient collection of highly relevant data by acquiring data while considering geographical location information. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the geographical location information of the target tissue into a generating AI and cause the generating AI to prioritize the acquisition of highly relevant data.

[0044] The data acquisition unit can analyze the social media activities of a target organization and acquire relevant data when acquiring gene expression data. For example, the data acquisition unit can analyze the social media activities of a target organization and acquire relevant gene expression data. Based on social media activities, the data acquisition unit can prioritize the acquisition of data related to specific topics. The data acquisition unit can also acquire data related to trends, taking social media activities into consideration. This allows for the efficient collection of highly relevant data by analyzing social media activities and acquiring data accordingly. Some or all of the above-described processes in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the social media activities of a target organization into a generating AI and have the generating AI acquire the relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the gene expression data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the gene expression data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the gene expression data during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the category of the gene expression data. The analysis unit can improve accuracy by applying different analysis algorithms for each category. The analysis unit can also adjust the parameters of the analysis algorithm based on the category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the category of the gene expression data into a generative AI and have the generative AI select and apply the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the acquisition timing of gene expression data during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can determine the priority of analysis based on the acquisition timing. The analysis unit can also prioritize the analysis of the most recent data, postponing the analysis of older data. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the acquisition timing of gene expression data into the generation AI and have the generation AI perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of gene expression data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can adjust the order of analysis based on relevance. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. In this way, by adjusting the order of analysis based on the relevance of the data, important data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance of gene expression data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0049] The prediction unit can improve the accuracy of predictions by considering the interrelationships of gene expression data during prediction. For example, the prediction unit can analyze the interrelationships of gene expression data to improve prediction accuracy. The prediction unit can optimize the prediction algorithm based on these interrelationships. The prediction unit can also determine the priority of predictions by considering these interrelationships. This improves the accuracy of predictions by considering the interrelationships of gene expression data. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the interrelationships of gene expression data into a generative AI and have the generative AI perform the task of improving prediction accuracy.

[0050] The prediction unit can make predictions while considering the attribute information of the submitter of the gene expression data. For example, the prediction unit can make predictions based on the submitter's age and gender. The prediction unit can make predictions based on the submitter's health status. Furthermore, the prediction unit can also make predictions based on the submitter's lifestyle. This allows for more personalized predictions by considering the submitter's attribute information. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the submitter's attribute information into a generative AI and have the generative AI perform the prediction.

[0051] The prediction unit can perform predictions while considering the geographical distribution of gene expression data. For example, the prediction unit performs predictions based on geographical distribution. The prediction unit can optimize the prediction algorithm by considering geographical distribution. The prediction unit can also determine the priority of predictions based on geographical distribution. This makes it possible to perform region-specific predictions by considering geographical distribution. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without using generative AI. For example, the prediction unit can input the geographical distribution of gene expression data into the generative AI and have the generative AI perform the prediction.

[0052] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on gene expression data during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature. The prediction unit can optimize its prediction algorithm based on the relevant literature. The prediction unit can also determine the priority of predictions by considering the relevant literature. As a result, the accuracy of predictions is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input relevant literature on gene expression data into a generative AI and have the generative AI perform the prediction.

[0053] The hypothesis generation unit can optimize its hypothesis generation algorithm by referring to past hypothesis data during hypothesis generation. For example, the hypothesis generation unit can refer to past hypothesis data and select the optimal hypothesis generation algorithm. The hypothesis generation unit can adjust the parameters of the hypothesis generation algorithm based on past hypothesis data. Furthermore, the hypothesis generation unit can analyze past hypothesis data to find areas for improvement in the hypothesis generation algorithm and optimize it. This improves the accuracy of the hypothesis generation algorithm by referring to past hypothesis data. Some or all of the above processes in the hypothesis generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the hypothesis generation unit can input past hypothesis data into a generation AI and have the generation AI perform the optimization of the hypothesis generation algorithm.

[0054] The hypothesis generation unit can apply different hypothesis generation methods to each category of gene expression data during hypothesis generation. For example, the hypothesis generation unit can select the optimal hypothesis generation method according to the category of gene expression data. The hypothesis generation unit can improve accuracy by applying different hypothesis generation methods to each category. The hypothesis generation unit can also adjust the parameters of the hypothesis generation method based on the category. This improves the accuracy of the hypothesis by applying the optimal hypothesis generation method according to the data category. Some or all of the above processing in the hypothesis generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the hypothesis generation unit can input the gene expression data category into the generation AI and have the generation AI select and apply the hypothesis generation method.

[0055] The hypothesis generation unit can weight hypotheses based on the submission timing of gene expression data during hypothesis generation. For example, the hypothesis generation unit weights hypotheses based on the latest data. The hypothesis generation unit can adjust the hypothesis weighting based on the submission timing. The hypothesis generation unit can also prioritize the generation of hypotheses based on the latest data, prioritizing older data. By weighting hypotheses based on the data submission timing, hypotheses based on the latest data can be generated preferentially. Some or all of the above processing in the hypothesis generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the hypothesis generation unit can input the gene expression data submission timing into the generation AI and have the generation AI perform the hypothesis weighting.

[0056] The hypothesis generation unit can generate hypotheses by referring to relevant market data for gene expression data during hypothesis generation. For example, the hypothesis generation unit can generate hypotheses by referring to relevant market data. The hypothesis generation unit can optimize the hypothesis generation algorithm based on the relevant market data. The hypothesis generation unit can also determine the priority of hypotheses by considering the relevant market data. This allows for the generation of more practical hypotheses by referring to relevant market data. Some or all of the above processing in the hypothesis generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the hypothesis generation unit can input relevant market data for gene expression data into a generation AI and have the generation AI perform hypothesis generation.

[0057] The treatment proposal unit can analyze the patient's past treatment history to select the optimal treatment method when proposing a treatment method. For example, the treatment proposal unit can analyze the patient's past treatment history and select the optimal treatment method. Based on the past treatment history, the treatment proposal unit can predict the effectiveness of a treatment method and propose the optimal treatment method. The treatment proposal unit can also determine the priority of treatment methods by considering the past treatment history. In this way, by analyzing the past treatment history, the optimal treatment method can be proposed to the patient. Some or all of the above processing in the treatment proposal unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the treatment proposal unit can input the patient's past treatment history into a generative AI and have the generative AI perform the selection of the optimal treatment method.

[0058] The treatment suggestion unit can customize treatment methods based on the patient's current health condition when suggesting a treatment method. For example, the treatment suggestion unit customizes treatment methods based on the patient's current health condition. The treatment suggestion unit can predict the effectiveness of treatment methods, taking into account the current health condition, and propose the optimal treatment method. The treatment suggestion unit can also determine the priority of treatment methods based on the current health condition. This allows for the proposal of more effective treatment methods by customizing treatment methods based on the current health condition. Some or all of the above-described processes in the treatment suggestion unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the treatment suggestion unit can input the patient's current health condition into a generative AI and have the generative AI perform the customization of treatment methods.

[0059] The treatment proposal unit can select the optimal treatment method by considering the patient's geographical location information when proposing a treatment method. For example, the treatment proposal unit can select the optimal treatment method based on the patient's geographical location information. The treatment proposal unit can predict the effectiveness of a treatment method by considering geographical location information and propose the optimal treatment method. Furthermore, the treatment proposal unit can also determine the priority of treatment methods based on geographical location information. This allows for the proposal of region-specific treatment methods by considering geographical location information. Some or all of the above-described processes in the treatment proposal unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the treatment proposal unit can input the patient's geographical location information into a generative AI and have the generative AI perform the selection of the optimal treatment method.

[0060] The treatment suggestion unit can analyze a patient's social media activity and propose treatments when suggesting a treatment. For example, the treatment suggestion unit can analyze a patient's social media activity and propose the most suitable treatment. Based on social media activity, the treatment suggestion unit can prioritize suggesting treatments related to specific topics. The treatment suggestion unit can also consider social media activity and propose treatments related to trends. In this way, by analyzing social media activity, it is possible to propose treatments relevant to the patient. Some or all of the above processing in the treatment suggestion unit may be performed using generative AI, or not. For example, the treatment suggestion unit can input data on the patient's social media activity into a generative AI and have the generative AI execute the treatment suggestion.

[0061] The report generation unit can optimize its report generation algorithm by referring to past report data when generating a report. For example, the report generation unit can refer to past report data and select the optimal report generation algorithm. The report generation unit can adjust the parameters of the report generation algorithm based on past report data. The report generation unit can also analyze past report data to find areas for improvement in the report generation algorithm and optimize it. As a result, the accuracy of the report generation algorithm is improved by referring to past report data. Some or all of the above processes in the report generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the report generation unit can input past report data into a generation AI and have the generation AI perform the optimization of the report generation algorithm.

[0062] The report generation unit can apply different report generation methods to each category of gene expression data when generating a report. For example, the report generation unit can select the optimal report generation method according to the category of gene expression data. The report generation unit can improve accuracy by applying different report generation methods to each category. The report generation unit can also adjust the parameters of the report generation method based on the category. This improves the accuracy of the report by applying the optimal report generation method according to the data category. Some or all of the above processing in the report generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the report generation unit can input the gene expression data category into the generation AI and have the generation AI select and apply the report generation method.

[0063] The report generation unit can weight reports based on the submission timing of gene expression data when generating reports. For example, the report generation unit can weight reports based on the latest data. The report generation unit can adjust the weighting of reports based on the submission timing. The report generation unit can also prioritize the generation of reports based on the latest data, prioritizing older data. By weighting reports based on the data submission timing, reports based on the latest data can be generated preferentially. Some or all of the above processing in the report generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the report generation unit can input the submission timing of gene expression data into the generation AI and have the generation AI perform the report weighting.

[0064] The report generation unit can generate reports by referencing relevant market data for gene expression data during report generation. For example, the report generation unit can generate reports by referencing relevant market data. The report generation unit can optimize the report generation algorithm based on the relevant market data. The report generation unit can also determine the priority of reports by considering the relevant market data. This allows for the generation of more practical reports by referencing relevant market data. Some or all of the above processing in the report generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the report generation unit can input relevant market data for gene expression data into a generation AI and have the generation AI perform report generation.

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

[0066] The data acquisition unit can analyze past gene expression data and select the optimal acquisition method. For example, the acquisition unit can identify the most accurate acquisition method from past data and prioritize its use. Based on past data, the acquisition unit can select the optimal acquisition method under specific conditions. Furthermore, the acquisition unit can analyze past data, find areas for improvement in the acquisition method, and optimize it. This improves the accuracy of data acquisition by utilizing past data to select the optimal acquisition method. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input past gene expression data into a generating AI and have the generating AI select the optimal acquisition method.

[0067] The data acquisition unit can filter gene expression data based on the state of the target tissue and environmental conditions. For example, the data acquisition unit can filter the data to be acquired based on the health status of the target tissue. The data acquisition unit can also filter the data to be acquired based on environmental conditions (temperature, humidity, etc.). Furthermore, the data acquisition unit can also filter the data to be acquired based on the growth stage of the target tissue. This allows for more accurate data acquisition by filtering the data based on the state of the target tissue and environmental conditions. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the state of the target tissue and environmental conditions into a generating AI and have the generating AI perform data filtering.

[0068] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring gene expression data, taking into account the geographical location information of the target tissue. For example, the acquisition unit can prioritize the acquisition of highly relevant data based on the geographical location information of the target tissue. The acquisition unit can prioritize the acquisition of data from a specific region based on geographical location information. Furthermore, the acquisition unit can also prioritize the acquisition of data suitable for environmental conditions, taking geographical location information into consideration. This allows for the efficient collection of highly relevant data by acquiring data while considering geographical location information. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the geographical location information of the target tissue into a generating AI and cause the generating AI to prioritize the acquisition of highly relevant data.

[0069] The data acquisition unit can analyze the social media activities of a target organization and acquire relevant data when acquiring gene expression data. For example, the data acquisition unit can analyze the social media activities of a target organization and acquire relevant gene expression data. Based on social media activities, the data acquisition unit can prioritize the acquisition of data related to specific topics. The data acquisition unit can also acquire data related to trends, taking social media activities into consideration. This allows for the efficient collection of highly relevant data by analyzing social media activities and acquiring data accordingly. Some or all of the above-described processes in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the social media activities of a target organization into a generating AI and have the generating AI acquire the relevant data.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the gene expression data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the gene expression data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0071] The analysis unit can apply different analysis algorithms depending on the category of the gene expression data during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the category of the gene expression data. The analysis unit can improve accuracy by applying different analysis algorithms for each category. The analysis unit can also adjust the parameters of the analysis algorithm based on the category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the category of the gene expression data into a generative AI and have the generative AI select and apply the analysis algorithm.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The acquisition unit acquires gene expression data. The acquisition unit can acquire comprehensive gene expression information from pathological tissue sections using, for example, spatial transcriptomics technology. It can also acquire gene expression data in various formats, such as RNA-Seq data and microarray data. Step 2: The analysis unit uses a generation AI to analyze the gene expression data acquired by the acquisition unit. For example, the analysis unit analyzes complex gene expression patterns and creates an intuitively understandable 3D visualization model. Various analysis methods can be used, such as data preprocessing and the application of analysis algorithms. Step 3: The prediction unit predicts disease mechanisms based on the data analyzed by the analysis unit. The prediction unit learns from, for example, a large biological database to predict new gene interactions and disease mechanisms. Predictions can be made based on the model and evaluation criteria used. Step 4: The hypothesis generation unit generates new research hypotheses based on the prediction results obtained by the prediction unit. For example, the hypothesis generation unit proposes new research hypotheses based on existing scientific literature and experimental data, and presents the optimal experimental design to verify them. Hypotheses can be generated based on the type of data used and the generation algorithm. Step 5: The treatment proposal unit proposes the optimal treatment based on the hypotheses generated by the hypothesis generation unit. For example, the treatment proposal unit proposes the optimal treatment based on the individual patient's gene expression profile. Treatment can be selected based on evaluation criteria for treatment effectiveness and the patient's condition. Step 6: The report generation unit generates a report of the experimental results based on the treatment proposed by the treatment proposal unit. The report generation unit can, for example, automatically analyze the experimental results and generate a report in the format of a scientific paper. The report can be created based on its structure and the information it contains.

[0074] (Example of form 2) The scientific discovery platform according to an embodiment of the present invention is a system that integrates generative AI and spatial transcriptomics technology. This system spatially visualizes the gene expression patterns of individual cells within tissue and uses generative AI to analyze and predict complex biological processes. This provides an innovative approach to elucidating the mechanisms of complex diseases such as cancer and neurodegenerative diseases, and to realizing personalized medicine. For example, the scientific discovery platform acquires comprehensive gene expression information from pathological tissue sections. Next, it uses generative AI to analyze the acquired gene expression data, analyze complex gene expression patterns, and create an intuitively understandable 3D visualization model. Furthermore, the generative AI learns in combination with a large-scale biological database to predict new gene interactions and disease mechanisms. Based on existing scientific literature and experimental data, the generative AI proposes new research hypotheses and presents the optimal experimental design to verify them. This significantly accelerates the research process and expands the possibilities for new scientific discoveries. Based on the gene expression profile of each patient, the generative AI proposes the optimal treatment method, automatically analyzes experimental results, and generates a report in the form of a scientific paper. This system will enable medical researchers, pharmaceutical company R&D departments, and biotechnology companies to provide innovative approaches to elucidating the mechanisms of complex diseases and realizing personalized medicine. For example, in cancer research, it is expected to significantly shorten and streamline the process of understanding heterogeneity within tumors, which currently takes an average of 5-10 years of research and millions of dollars in costs. In this way, the scientific discovery platform can provide innovative approaches to elucidating the mechanisms of complex diseases and realizing personalized medicine.

[0075] The scientific discovery platform according to this embodiment comprises an acquisition unit, an analysis unit, a prediction unit, a hypothesis generation unit, a treatment proposal unit, and a report generation unit. The acquisition unit acquires gene expression data. The acquisition unit can acquire comprehensive gene expression information from pathological tissue sections using, for example, spatial transcriptomics technology. The acquisition unit can acquire gene expression data in various formats, such as RNA-Seq data and microarray data. The analysis unit analyzes the gene expression data acquired by the acquisition unit using generative AI. The analysis unit can, for example, analyze complex gene expression patterns and create an intuitively understandable 3D visualization model. The analysis unit can use various analytical methods, such as data preprocessing and the application of analytical algorithms. The prediction unit predicts disease mechanisms based on the data analyzed by the analysis unit. The prediction unit can, for example, learn by combining it with a large-scale biological database to predict new gene interactions and disease mechanisms. The prediction unit can make predictions based on the model and evaluation criteria used. The hypothesis generation unit generates new research hypotheses based on the prediction results obtained by the prediction unit. The hypothesis generation unit proposes new research hypotheses based on existing scientific literature and experimental data, and presents the optimal experimental design for verifying them. The hypothesis generation unit can generate hypotheses based on the type of data used and the generation algorithm. The treatment proposal unit proposes the optimal treatment based on the hypotheses generated by the hypothesis generation unit. The treatment proposal unit proposes the optimal treatment based on the individual gene expression profile of each patient, for example. The treatment proposal unit can select treatments based on evaluation criteria for treatment effectiveness and the patient's condition. The report generation unit generates a report of the experimental results based on the treatment proposed by the treatment proposal unit. The report generation unit can automatically analyze the experimental results and generate a report in the format of a scientific paper, for example. The report generation unit can create a report based on the structure of the report and the information it contains. As a result, the scientific discovery platform according to the embodiment can efficiently perform a series of processes from gene expression data acquisition to analysis, prediction, hypothesis generation, treatment proposal, and report generation.

[0076] The acquisition unit acquires gene expression data. For example, the acquisition unit can acquire comprehensive gene expression information from pathological tissue sections using spatial transcriptomics technology. Spatial transcriptomics technology is a method for spatially analyzing gene expression within tissues, allowing for a detailed understanding of gene expression patterns in specific cells or tissues. This enables highly accurate analysis of abnormalities in pathological tissues and the progression of diseases. The acquisition unit can acquire gene expression data in various formats, such as RNA-Seq data and microarray data. RNA-Seq is a method that analyzes the entire transcriptome using next-generation sequencing technology and is effective for quantifying gene expression and discovering novel transcripts. Microarrays are a method that analyzes gene expression using probes based on known gene sequences, allowing for rapid acquisition of expression profiles for specific gene groups. By combining these technologies, the acquisition unit efficiently collects a wide range of gene expression data and provides a foundation for analysis. Furthermore, the acquisition unit also performs data quality control and preprocessing to ensure the reliability of the data provided to the analysis unit. For example, it performs noise reduction and normalization of the acquired data and converts it into a format suitable for analysis. This allows the acquisition unit to consistently perform everything from gene expression data collection to preprocessing, supporting the efficient data analysis of the analysis unit.

[0077] The analysis unit uses generative AI to analyze gene expression data acquired by the acquisition unit. Generative AI is an advanced analysis method based on deep learning technology, analyzing complex gene expression patterns and creating intuitively understandable 3D visualization models. Specifically, the generative AI performs data normalization and missing value imputation as preprocessing for the gene expression data. Next, it uses a deep learning model to cluster gene expression patterns and detect anomalies. For example, it uses an autoencoder to compress high-dimensional data to a lower dimension and extract features. Furthermore, the generative AI constructs interaction networks between genes and analyzes the correlations of gene expression. This allows for the identification of gene groups and pathways associated with specific diseases. In addition, the generative AI outputs the analysis results as a 3D visualization model, enabling researchers to intuitively understand the data. For example, it plots the gene expression clustering results in 3D space, visually showing the characteristics of each cluster. This allows the analysis unit to highly analyze the acquired gene expression data and provide it in an intuitively understandable format. Furthermore, the analysis unit shares the analysis results with other departments to support the prediction and hypothesis generation units. This allows the analysis unit to play a central role in data analysis and support the efficient operation of the entire scientific discovery platform.

[0078] The prediction unit predicts disease mechanisms based on data analyzed by the analysis unit. The prediction unit learns from, for example, large-scale biological databases to predict novel gene interactions and disease mechanisms. Specifically, the prediction unit acquires gene interaction network and pathway information from existing biological databases and integrates it with gene expression data provided by the analysis unit. Next, it uses machine learning algorithms to predict novel gene interactions and mechanisms related to disease. For example, it uses random forests or support vector machines to extract features of disease-related genes and construct a predictive model. Furthermore, the prediction unit uses deep learning models to analyze complex gene interaction networks and discover novel disease mechanisms. This enables the prediction unit to make highly accurate predictions of disease mechanisms based on data provided by the analysis unit. In addition, the prediction unit evaluates the prediction results and provides reliable predictions. For example, it uses cross-validation or bootstrap methods to evaluate the accuracy of the prediction model and select the optimal model. This allows the prediction unit to make highly reliable predictions of disease mechanisms and support the hypothesis generation unit and the treatment proposal unit.

[0079] The hypothesis generation unit generates new research hypotheses based on the prediction results obtained by the prediction unit. For example, the hypothesis generation unit proposes new research hypotheses based on existing scientific literature and experimental data, and presents the optimal experimental design for verifying them. Specifically, the hypothesis generation unit searches relevant scientific literature based on the disease mechanism prediction results provided by the prediction unit and compares them with existing knowledge. Next, it uses generation AI to integrate the prediction results and information from the scientific literature to generate new research hypotheses. For example, it uses natural language processing technology to extract relevant information from scientific literature and proposes a new hypothesis in combination with the prediction results. The hypothesis generation unit also presents the optimal experimental design for verifying the generated hypotheses. For example, it designs the purpose and conditions of the experiment, the reagents and equipment to be used in detail, and ensures the reproducibility and reliability of the experiment. In this way, the hypothesis generation unit can efficiently generate new research hypotheses based on the prediction results provided by the prediction unit and support the design of experiments. Furthermore, the hypothesis generation unit evaluates the validity of the generated hypotheses and provides highly reliable hypotheses. For example, it verifies the generated hypotheses by comparing them with existing experimental data and selects highly reliable hypotheses. This allows the hypothesis generation unit to efficiently generate and test new research hypotheses, thereby improving the overall research efficiency of the scientific discovery platform.

[0080] The Treatment Proposal Unit proposes the optimal treatment based on hypotheses generated by the Hypothesis Generation Unit. For example, the Treatment Proposal Unit proposes the optimal treatment based on the individual patient's gene expression profile. Specifically, the Treatment Proposal Unit analyzes the patient's gene expression data based on the research hypotheses provided by the Hypothesis Generation Unit and selects the optimal treatment from the perspective of personalized medicine. For example, it proposes the optimal drug or treatment based on specific gene mutations or expression patterns. The Treatment Proposal Unit also selects treatments based on evaluation criteria for treatment effectiveness and the patient's condition. For example, it uses a treatment effectiveness prediction model to simulate the effects of the proposed treatments and select the optimal treatment. Furthermore, the Treatment Proposal Unit provides the information necessary for implementing the proposed treatments and supports their implementation in clinical settings. For example, it provides detailed information on the procedures for implementing the treatments, precautions, and methods for managing side effects, enabling healthcare professionals to perform treatments appropriately. In this way, the Treatment Proposal Unit can propose the optimal treatment from the perspective of personalized medicine based on hypotheses provided by the Hypothesis Generation Unit and support their implementation in clinical settings. Furthermore, the Treatment Proposal Unit continuously monitors the effects of the proposed treatments and revises the treatments as needed. This allows the treatment proposal department to support the proposal and implementation of flexible treatment methods tailored to the patient's condition, thereby maximizing treatment effectiveness.

[0081] The report generation unit generates reports based on experimental results derived from treatment methods proposed by the treatment proposal unit. For example, the report generation unit automatically analyzes experimental results and generates reports in the format of scientific papers. Specifically, the report generation unit collects the results of treatment implementation provided by the treatment proposal unit, analyzes the data, and creates reports. For example, it analyzes data on treatment effectiveness and the occurrence of side effects to evaluate the efficacy and safety of the treatment methods. Furthermore, the report generation unit uses AI to automatically generate reports. For example, it uses natural language generation technology to create reports in the format of scientific papers based on the analysis results. This allows the report generation unit to quickly and accurately compile experimental results into reports. In addition, the report generation unit creates reports based on their structure and the information they contain. For example, it appropriately structures sections such as the purpose, background, methods, results, discussion, and conclusions, and creates reports in an easy-to-read format. This allows the report generation unit to create scientifically reliable reports based on information provided by the treatment proposal unit and provide them to researchers and medical professionals. Furthermore, the report generation unit continuously updates the report content to reflect the latest information. This allows the report generation department to always provide reports based on the latest information, supporting decision-making in research and medical settings.

[0082] The analysis unit can analyze complex gene expression patterns and create intuitively understandable 3D visualization models. For example, the analysis unit can analyze gene expression data and visualize the results as a 3D model. The analysis unit can create 3D models based on the software and visualization criteria used. For example, the analysis unit can analyze the expression fluctuations and correlations of specific genes and display them as a 3D model. The analysis unit can also simultaneously analyze the expression patterns of multiple genes and integrate them into a 3D model. This makes it easier for researchers to understand the data by visualizing gene expression patterns as intuitively understandable 3D models. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input gene expression data into a generative AI and have the generative AI generate a 3D visualization model.

[0083] The prediction unit can learn by combining it with a large-scale biological database and predict new gene interactions and disease mechanisms. For example, the prediction unit can use a large-scale biological database to predict gene interactions and disease mechanisms. The prediction unit can make predictions based on the name of the database and the method of data acquisition. For example, the prediction unit can predict the interaction of specific genes and elucidate disease mechanisms based on that. The prediction unit can also simultaneously predict the interaction of multiple genes and analyze disease mechanisms based on that. This makes it possible to make more accurate predictions by utilizing a large-scale database. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without generative AI. For example, the prediction unit can input data from a biological database into a generative AI and have the generative AI perform predictions of gene interactions and disease mechanisms.

[0084] The hypothesis generation unit can propose new research hypotheses based on existing scientific literature and experimental data, and present optimal experimental designs to verify them. For example, the hypothesis generation unit can refer to existing scientific literature and propose new research hypotheses. The hypothesis generation unit can refer to literature based on literature databases and search methods. For example, the hypothesis generation unit can propose a hypothesis regarding the function of a specific gene and present an experimental design to verify it. The hypothesis generation unit can also integrate multiple literatures to generate new hypotheses and create experimental designs based on them. This improves the efficiency and accuracy of research by proposing new research hypotheses and presenting optimal experimental designs. Some or all of the above-described processes in the hypothesis generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the hypothesis generation unit can input scientific literature and experimental data into a generation AI, and have the generation AI perform the generation of new hypotheses and present experimental designs.

[0085] The treatment proposal unit can propose the optimal treatment based on each patient's individual gene expression profile. For example, the treatment proposal unit can analyze the patient's gene expression profile and propose the optimal treatment based on it. The treatment proposal unit can select a treatment based on the method of creating the profile and the data used. For example, the treatment proposal unit can propose a treatment based on the expression pattern of a specific gene. The treatment proposal unit can also propose a treatment by integrating the expression profiles of multiple genes. This allows for the proposal of the optimal treatment for each patient, towards the realization of personalized medicine. Some or all of the above-described processes in the treatment proposal unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the treatment proposal unit can input the patient's gene expression profile into a generative AI and have the generative AI propose the optimal treatment.

[0086] The report generation unit can automatically analyze experimental results and generate reports in the format of scientific papers. For example, the report generation unit can analyze experimental results and generate those results as a report in the format of a scientific paper. The report generation unit can create reports based on the structure and information to be included. For example, the report generation unit can analyze specific experimental results in detail and compile those results into a report. The report generation unit can also integrate multiple experimental results to create a report. This reduces the burden on researchers and enables efficient reporting by automatically analyzing experimental results and generating reports. Some or all of the above processes in the report generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the report generation unit can input experimental result data into a generation AI and have the generation AI perform the report generation.

[0087] The data acquisition unit can estimate the user's emotions and adjust the timing of gene expression data acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can delay the acquisition timing to acquire data when the user is relaxed. If the user is focused, the acquisition unit can start acquiring data immediately to acquire it at the optimal time. The acquisition unit can also adjust the timing to acquire data after the user has rested if the user is tired. By adjusting the timing of data acquisition according to the user's emotions, more appropriate data acquisition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of data acquisition timing.

[0088] The data acquisition unit can analyze past gene expression data and select the optimal acquisition method. For example, the acquisition unit can identify the most accurate acquisition method from past data and prioritize its use. Based on past data, the acquisition unit can select the optimal acquisition method under specific conditions. Furthermore, the acquisition unit can analyze past data, find areas for improvement in the acquisition method, and optimize it. This improves the accuracy of data acquisition by utilizing past data to select the optimal acquisition method. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input past gene expression data into a generating AI and have the generating AI select the optimal acquisition method.

[0089] The data acquisition unit can filter gene expression data based on the state of the target tissue and environmental conditions. For example, the data acquisition unit can filter the data to be acquired based on the health status of the target tissue. The data acquisition unit can also filter the data to be acquired based on environmental conditions (temperature, humidity, etc.). Furthermore, the data acquisition unit can also filter the data to be acquired based on the growth stage of the target tissue. This allows for more accurate data acquisition by filtering the data based on the state of the target tissue and environmental conditions. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the state of the target tissue and environmental conditions into a generating AI and have the generating AI perform data filtering.

[0090] The data acquisition unit can estimate the user's emotions and determine the priority of gene expression data to acquire based on the estimated user emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring important data. If the user is relaxed, the data acquisition unit can prioritize acquiring detailed data. Also, if the user is in a hurry, the data acquisition unit can prioritize acquiring data that can be acquired quickly. In this way, important data can be prioritized by determining the data priority according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's emotion data into the generative AI and have the generative AI perform the determination of data priority.

[0091] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring gene expression data, taking into account the geographical location information of the target tissue. For example, the acquisition unit can prioritize the acquisition of highly relevant data based on the geographical location information of the target tissue. The acquisition unit can prioritize the acquisition of data from a specific region based on geographical location information. Furthermore, the acquisition unit can also prioritize the acquisition of data suitable for environmental conditions, taking geographical location information into consideration. This allows for the efficient collection of highly relevant data by acquiring data while considering geographical location information. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the geographical location information of the target tissue into a generating AI and cause the generating AI to prioritize the acquisition of highly relevant data.

[0092] The data acquisition unit can analyze the social media activities of a target organization and acquire relevant data when acquiring gene expression data. For example, the data acquisition unit can analyze the social media activities of a target organization and acquire relevant gene expression data. Based on social media activities, the data acquisition unit can prioritize the acquisition of data related to specific topics. The data acquisition unit can also acquire data related to trends, taking social media activities into consideration. This allows for the efficient collection of highly relevant data by analyzing social media activities and acquiring data accordingly. Some or all of the above-described processes in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the social media activities of a target organization into a generating AI and have the generating AI acquire the relevant data.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more easily understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the gene expression data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the gene expression data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0095] The analysis unit can apply different analysis algorithms depending on the category of the gene expression data during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the category of the gene expression data. The analysis unit can improve accuracy by applying different analysis algorithms for each category. The analysis unit can also adjust the parameters of the analysis algorithm based on the category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the category of the gene expression data into a generative AI and have the generative AI select and apply the analysis algorithm.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0097] The analysis unit can determine the priority of analysis based on the acquisition timing of gene expression data during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can determine the priority of analysis based on the acquisition timing. The analysis unit can also prioritize the analysis of the most recent data, postponing the analysis of older data. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data acquisition timing. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the acquisition timing of gene expression data into the generation AI and have the generation AI perform the determination of the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of gene expression data during analysis. For example, the analysis unit prioritizes the analysis of highly relevant data. The analysis unit can adjust the order of analysis based on relevance. The analysis unit can also postpone the analysis of less relevant data and prioritize the analysis of important data. In this way, by adjusting the order of analysis based on the relevance of the data, important data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance of gene expression data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0099] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated emotions. For example, if the user is tense, the prediction unit can make a conservative prediction. If the user is relaxed, the prediction unit can make a detailed prediction. The prediction unit can also make a rapid prediction if the user is in a hurry. By adjusting the prediction criteria according to the user's emotions, more appropriate prediction results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using or without a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the prediction criteria.

[0100] The prediction unit can improve the accuracy of predictions by considering the interrelationships of gene expression data during prediction. For example, the prediction unit can analyze the interrelationships of gene expression data to improve prediction accuracy. The prediction unit can optimize the prediction algorithm based on these interrelationships. The prediction unit can also determine the priority of predictions by considering these interrelationships. This improves the accuracy of predictions by considering the interrelationships of gene expression data. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the interrelationships of gene expression data into a generative AI and have the generative AI perform the task of improving prediction accuracy.

[0101] The prediction unit can make predictions while considering the attribute information of the submitter of the gene expression data. For example, the prediction unit can make predictions based on the submitter's age and gender. The prediction unit can make predictions based on the submitter's health status. Furthermore, the prediction unit can also make predictions based on the submitter's lifestyle. This allows for more personalized predictions by considering the submitter's attribute information. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input the submitter's attribute information into a generative AI and have the generative AI perform the prediction.

[0102] The prediction unit can estimate the user's emotions and adjust the order in which the prediction results are displayed based on the estimated emotions. For example, if the user is nervous, the prediction unit can display important results first. If the user is relaxed, the prediction unit can display detailed results sequentially. Also, if the user is in a hurry, the prediction unit can display concise results first. By adjusting the order in which results are displayed according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using or without a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the order in which results are displayed.

[0103] The prediction unit can perform predictions while considering the geographical distribution of gene expression data. For example, the prediction unit performs predictions based on geographical distribution. The prediction unit can optimize the prediction algorithm by considering geographical distribution. The prediction unit can also determine the priority of predictions based on geographical distribution. This makes it possible to perform region-specific predictions by considering geographical distribution. Some or all of the above processing in the prediction unit may be performed using generative AI, or it may be performed without using generative AI. For example, the prediction unit can input the geographical distribution of gene expression data into the generative AI and have the generative AI perform the prediction.

[0104] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on gene expression data during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to relevant literature. The prediction unit can optimize its prediction algorithm based on the relevant literature. The prediction unit can also determine the priority of predictions by considering the relevant literature. As a result, the accuracy of predictions is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the prediction unit can input relevant literature on gene expression data into a generative AI and have the generative AI perform the prediction.

[0105] The hypothesis generation unit can estimate the user's emotions and adjust the hypothesis generation method based on the estimated user emotions. For example, if the user is nervous, the hypothesis generation unit can generate a simple hypothesis. If the user is relaxed, the hypothesis generation unit can generate a detailed hypothesis. Also, if the user is in a hurry, the hypothesis generation unit can generate a hypothesis quickly. In this way, by adjusting the hypothesis generation method according to the user's emotions, more appropriate hypotheses can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the hypothesis generation unit may be performed using the generative AI or not. For example, the hypothesis generation unit can input user emotion data into the generative AI and have the generative AI adjust the hypothesis generation method.

[0106] The hypothesis generation unit can optimize its hypothesis generation algorithm by referring to past hypothesis data during hypothesis generation. For example, the hypothesis generation unit can refer to past hypothesis data and select the optimal hypothesis generation algorithm. The hypothesis generation unit can adjust the parameters of the hypothesis generation algorithm based on past hypothesis data. Furthermore, the hypothesis generation unit can analyze past hypothesis data to find areas for improvement in the hypothesis generation algorithm and optimize it. This improves the accuracy of the hypothesis generation algorithm by referring to past hypothesis data. Some or all of the above processes in the hypothesis generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the hypothesis generation unit can input past hypothesis data into a generation AI and have the generation AI perform the optimization of the hypothesis generation algorithm.

[0107] The hypothesis generation unit can apply different hypothesis generation methods to each category of gene expression data during hypothesis generation. For example, the hypothesis generation unit can select the optimal hypothesis generation method according to the category of gene expression data. The hypothesis generation unit can improve accuracy by applying different hypothesis generation methods to each category. The hypothesis generation unit can also adjust the parameters of the hypothesis generation method based on the category. This improves the accuracy of the hypothesis by applying the optimal hypothesis generation method according to the data category. Some or all of the above processing in the hypothesis generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the hypothesis generation unit can input the gene expression data category into the generation AI and have the generation AI select and apply the hypothesis generation method.

[0108] The hypothesis generation unit can estimate the user's emotions and determine the priority of hypotheses based on the estimated user emotions. For example, if the user is tense, the hypothesis generation unit can prioritize generating important hypotheses. If the user is relaxed, the hypothesis generation unit can prioritize generating detailed hypotheses. Also, if the user is in a hurry, the hypothesis generation unit can prioritize generating hypotheses that can be generated quickly. In this way, by determining the priority of hypotheses according to the user's emotions, important hypotheses can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the hypothesis generation unit may be performed using a generative AI or not. For example, the hypothesis generation unit can input user emotion data into a generative AI and have the generative AI perform the determination of hypothesis priority.

[0109] The hypothesis generation unit can weight hypotheses based on the submission timing of gene expression data during hypothesis generation. For example, the hypothesis generation unit weights hypotheses based on the latest data. The hypothesis generation unit can adjust the hypothesis weighting based on the submission timing. The hypothesis generation unit can also prioritize the generation of hypotheses based on the latest data, prioritizing older data. By weighting hypotheses based on the data submission timing, hypotheses based on the latest data can be generated preferentially. Some or all of the above processing in the hypothesis generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the hypothesis generation unit can input the gene expression data submission timing into the generation AI and have the generation AI perform the hypothesis weighting.

[0110] The hypothesis generation unit can generate hypotheses by referring to relevant market data for gene expression data during hypothesis generation. For example, the hypothesis generation unit can generate hypotheses by referring to relevant market data. The hypothesis generation unit can optimize the hypothesis generation algorithm based on the relevant market data. The hypothesis generation unit can also determine the priority of hypotheses by considering the relevant market data. This allows for the generation of more practical hypotheses by referring to relevant market data. Some or all of the above processing in the hypothesis generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the hypothesis generation unit can input relevant market data for gene expression data into a generation AI and have the generation AI perform hypothesis generation.

[0111] The treatment suggestion unit can estimate the user's emotions and adjust its treatment suggestion method based on the estimated emotions. For example, if the user is anxious, the treatment suggestion unit can suggest a simple and easily understandable treatment. If the user is relaxed, it can suggest a more detailed treatment. Furthermore, if the user is in a hurry, the treatment suggestion unit can prioritize suggesting treatments that can be quickly provided. In this way, by adjusting the treatment suggestion method according to the user's emotions, a more appropriate treatment can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the treatment suggestion unit may be performed using or without generative AI. For example, the treatment suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the treatment suggestion method.

[0112] The treatment proposal unit can analyze the patient's past treatment history to select the optimal treatment method when proposing a treatment method. For example, the treatment proposal unit can analyze the patient's past treatment history and select the optimal treatment method. Based on the past treatment history, the treatment proposal unit can predict the effectiveness of a treatment method and propose the optimal treatment method. The treatment proposal unit can also determine the priority of treatment methods by considering the past treatment history. In this way, by analyzing the past treatment history, the optimal treatment method can be proposed to the patient. Some or all of the above processing in the treatment proposal unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the treatment proposal unit can input the patient's past treatment history into a generative AI and have the generative AI perform the selection of the optimal treatment method.

[0113] The treatment suggestion unit can customize treatment methods based on the patient's current health condition when suggesting a treatment method. For example, the treatment suggestion unit customizes treatment methods based on the patient's current health condition. The treatment suggestion unit can predict the effectiveness of treatment methods, taking into account the current health condition, and propose the optimal treatment method. The treatment suggestion unit can also determine the priority of treatment methods based on the current health condition. This allows for the proposal of more effective treatment methods by customizing treatment methods based on the current health condition. Some or all of the above-described processes in the treatment suggestion unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the treatment suggestion unit can input the patient's current health condition into a generative AI and have the generative AI perform the customization of treatment methods.

[0114] The treatment suggestion unit can estimate the user's emotions and determine the priority of treatments based on the estimated emotions. For example, if the user is anxious, the treatment suggestion unit may prioritize suggesting important treatments. If the user is relaxed, the treatment suggestion unit may prioritize suggesting detailed treatments. Furthermore, if the user is in a hurry, the treatment suggestion unit may prioritize suggesting treatments that can be quickly provided. In this way, by determining the priority of treatments according to the user's emotions, important treatments can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the treatment suggestion unit may be performed using or without generative AI. For example, the treatment suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of treatments.

[0115] The treatment proposal unit can select the optimal treatment method by considering the patient's geographical location information when proposing a treatment method. For example, the treatment proposal unit can select the optimal treatment method based on the patient's geographical location information. The treatment proposal unit can predict the effectiveness of a treatment method by considering geographical location information and propose the optimal treatment method. Furthermore, the treatment proposal unit can also determine the priority of treatment methods based on geographical location information. This allows for the proposal of region-specific treatment methods by considering geographical location information. Some or all of the above-described processes in the treatment proposal unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the treatment proposal unit can input the patient's geographical location information into a generative AI and have the generative AI perform the selection of the optimal treatment method.

[0116] The treatment suggestion unit can analyze a patient's social media activity and propose treatments when suggesting a treatment. For example, the treatment suggestion unit can analyze a patient's social media activity and propose the most suitable treatment. Based on social media activity, the treatment suggestion unit can prioritize suggesting treatments related to specific topics. The treatment suggestion unit can also consider social media activity and propose treatments related to trends. In this way, by analyzing social media activity, it is possible to propose treatments relevant to the patient. Some or all of the above processing in the treatment suggestion unit may be performed using generative AI, or not. For example, the treatment suggestion unit can input data on the patient's social media activity into a generative AI and have the generative AI execute the treatment suggestion.

[0117] The report generation unit can estimate the user's emotions and adjust the report generation method based on the estimated emotions. For example, if the user is stressed, the report generation unit can generate a simple and easy-to-read report. If the user is relaxed, the report generation unit can generate a detailed report. Furthermore, if the user is in a hurry, the report generation unit can prioritize generating a report that can be generated quickly. This allows for the generation of more appropriate reports by adjusting the report generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using or without a generative AI. For example, the report generation unit can input user emotion data into a generative AI and have the generative AI adjust the report generation method.

[0118] The report generation unit can optimize its report generation algorithm by referring to past report data when generating a report. For example, the report generation unit can refer to past report data and select the optimal report generation algorithm. The report generation unit can adjust the parameters of the report generation algorithm based on past report data. The report generation unit can also analyze past report data to find areas for improvement in the report generation algorithm and optimize it. As a result, the accuracy of the report generation algorithm is improved by referring to past report data. Some or all of the above processes in the report generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the report generation unit can input past report data into a generation AI and have the generation AI perform the optimization of the report generation algorithm.

[0119] The report generation unit can apply different report generation methods to each category of gene expression data when generating a report. For example, the report generation unit can select the optimal report generation method according to the category of gene expression data. The report generation unit can improve accuracy by applying different report generation methods to each category. The report generation unit can also adjust the parameters of the report generation method based on the category. This improves the accuracy of the report by applying the optimal report generation method according to the data category. Some or all of the above processing in the report generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the report generation unit can input the gene expression data category into the generation AI and have the generation AI select and apply the report generation method.

[0120] The report generation unit can estimate the user's emotions and determine the priority of reports based on the estimated emotions. For example, if the user is stressed, the report generation unit can prioritize generating important reports. If the user is relaxed, the report generation unit can prioritize generating detailed reports. Also, if the user is in a hurry, the report generation unit can prioritize generating reports that can be generated quickly. In this way, by determining the priority of reports according to the user's emotions, important reports can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the report generation unit may be performed using a generative AI or not. For example, the report generation unit can input user emotion data into a generative AI and have the generative AI perform the determination of report priorities.

[0121] The report generation unit can weight reports based on the submission timing of gene expression data when generating reports. For example, the report generation unit can weight reports based on the latest data. The report generation unit can adjust the weighting of reports based on the submission timing. The report generation unit can also prioritize the generation of reports based on the latest data, prioritizing older data. By weighting reports based on the data submission timing, reports based on the latest data can be generated preferentially. Some or all of the above processing in the report generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the report generation unit can input the submission timing of gene expression data into the generation AI and have the generation AI perform the report weighting.

[0122] The report generation unit can generate reports by referencing relevant market data for gene expression data during report generation. For example, the report generation unit can generate reports by referencing relevant market data. The report generation unit can optimize the report generation algorithm based on the relevant market data. The report generation unit can also determine the priority of reports by considering the relevant market data. This allows for the generation of more practical reports by referencing relevant market data. Some or all of the above processing in the report generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the report generation unit can input relevant market data for gene expression data into a generation AI and have the generation AI perform report generation.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The data acquisition unit can estimate the user's emotions and adjust the timing of gene expression data acquisition based on the estimated emotions. For example, if the user is stressed, the acquisition unit can delay the acquisition timing to acquire data when the user is relaxed. If the user is focused, the acquisition unit can start acquiring data immediately to acquire it at the optimal time. The acquisition unit can also adjust the timing to acquire data after the user has rested if the user is tired. By adjusting the timing of data acquisition according to the user's emotions, more appropriate data acquisition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of data acquisition timing.

[0125] The data acquisition unit can analyze past gene expression data and select the optimal acquisition method. For example, the acquisition unit can identify the most accurate acquisition method from past data and prioritize its use. Based on past data, the acquisition unit can select the optimal acquisition method under specific conditions. Furthermore, the acquisition unit can analyze past data, find areas for improvement in the acquisition method, and optimize it. This improves the accuracy of data acquisition by utilizing past data to select the optimal acquisition method. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input past gene expression data into a generating AI and have the generating AI select the optimal acquisition method.

[0126] The data acquisition unit can filter gene expression data based on the state of the target tissue and environmental conditions. For example, the data acquisition unit can filter the data to be acquired based on the health status of the target tissue. The data acquisition unit can also filter the data to be acquired based on environmental conditions (temperature, humidity, etc.). Furthermore, the data acquisition unit can also filter the data to be acquired based on the growth stage of the target tissue. This allows for more accurate data acquisition by filtering the data based on the state of the target tissue and environmental conditions. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the state of the target tissue and environmental conditions into a generating AI and have the generating AI perform data filtering.

[0127] The data acquisition unit can estimate the user's emotions and determine the priority of gene expression data to acquire based on the estimated user emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring important data. If the user is relaxed, the data acquisition unit can prioritize acquiring detailed data. Also, if the user is in a hurry, the data acquisition unit can prioritize acquiring data that can be acquired quickly. In this way, important data can be prioritized by determining the data priority according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input the user's emotion data into the generative AI and have the generative AI perform the determination of data priority.

[0128] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring gene expression data, taking into account the geographical location information of the target tissue. For example, the acquisition unit can prioritize the acquisition of highly relevant data based on the geographical location information of the target tissue. The acquisition unit can prioritize the acquisition of data from a specific region based on geographical location information. Furthermore, the acquisition unit can also prioritize the acquisition of data suitable for environmental conditions, taking geographical location information into consideration. This allows for the efficient collection of highly relevant data by acquiring data while considering geographical location information. Some or all of the above-described processes in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the geographical location information of the target tissue into a generating AI and cause the generating AI to prioritize the acquisition of highly relevant data.

[0129] The data acquisition unit can analyze the social media activities of a target organization and acquire relevant data when acquiring gene expression data. For example, the data acquisition unit can analyze the social media activities of a target organization and acquire relevant gene expression data. Based on social media activities, the data acquisition unit can prioritize the acquisition of data related to specific topics. The data acquisition unit can also acquire data related to trends, taking social media activities into consideration. This allows for the efficient collection of highly relevant data by analyzing social media activities and acquiring data accordingly. Some or all of the above-described processes in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input data on the social media activities of a target organization into a generating AI and have the generating AI acquire the relevant data.

[0130] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more easily understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0131] The analysis unit can adjust the level of detail of the analysis based on the importance of the gene expression data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for efficient analysis by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the gene expression data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0132] The analysis unit can apply different analysis algorithms depending on the category of the gene expression data during analysis. For example, the analysis unit can select the optimal analysis algorithm according to the category of the gene expression data. The analysis unit can improve accuracy by applying different analysis algorithms for each category. The analysis unit can also adjust the parameters of the analysis algorithm based on the category. This improves the accuracy of the analysis by applying the optimal analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the category of the gene expression data into a generative AI and have the generative AI select and apply the analysis algorithm.

[0133] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The acquisition unit acquires gene expression data. The acquisition unit can acquire comprehensive gene expression information from pathological tissue sections using, for example, spatial transcriptomics technology. It can also acquire gene expression data in various formats, such as RNA-Seq data and microarray data. Step 2: The analysis unit uses a generation AI to analyze the gene expression data acquired by the acquisition unit. For example, the analysis unit analyzes complex gene expression patterns and creates an intuitively understandable 3D visualization model. Various analysis methods can be used, such as data preprocessing and the application of analysis algorithms. Step 3: The prediction unit predicts disease mechanisms based on the data analyzed by the analysis unit. The prediction unit learns from, for example, a large biological database to predict new gene interactions and disease mechanisms. Predictions can be made based on the model and evaluation criteria used. Step 4: The hypothesis generation unit generates new research hypotheses based on the prediction results obtained by the prediction unit. For example, the hypothesis generation unit proposes new research hypotheses based on existing scientific literature and experimental data, and presents the optimal experimental design to verify them. Hypotheses can be generated based on the type of data used and the generation algorithm. Step 5: The treatment proposal unit proposes the optimal treatment based on the hypotheses generated by the hypothesis generation unit. For example, the treatment proposal unit proposes the optimal treatment based on the individual patient's gene expression profile. Treatment can be selected based on evaluation criteria for treatment effectiveness and the patient's condition. Step 6: The report generation unit generates a report of the experimental results based on the treatment proposed by the treatment proposal unit. The report generation unit can, for example, automatically analyze the experimental results and generate a report in the format of a scientific paper. The report can be created based on its structure and the information it contains.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0138] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the acquisition unit, analysis unit, prediction unit, hypothesis generation unit, treatment method proposal unit, and report generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires gene expression data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and analyzes the acquired data to create a 3D visualization model. The prediction unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and predicts new gene interactions and disease mechanisms. The hypothesis generation unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and generates new research hypotheses. The treatment method proposal unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and proposes the optimal treatment method. The report generation unit is implemented in the specific processing unit 290 of the data processing device 12, for example, and generates experimental results as a report. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0143] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the acquisition unit, analysis unit, prediction unit, hypothesis generation unit, treatment method proposal unit, and report generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires gene expression data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the acquired data and creates a 3D visualization model. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which predicts new gene interactions and disease mechanisms. The hypothesis generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which generates new research hypotheses. The treatment method proposal unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which proposes the optimal treatment method. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which generates experimental results as a report. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the acquisition unit, analysis unit, prediction unit, hypothesis generation unit, treatment method proposal unit, and report generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires gene expression data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the acquired data to create a 3D visualization model. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and predicts new gene interactions and disease mechanisms. The hypothesis generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates new research hypotheses. The treatment method proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and proposes the optimal treatment method. The report generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates experimental results as a report. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0175] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0176] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0178] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0179] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0180] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0181] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0182] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0183] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0184] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0185] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0187] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0188] Each of the multiple elements described above, including the acquisition unit, analysis unit, prediction unit, hypothesis generation unit, treatment method proposal unit, and report generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires gene expression data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the acquired data and creates a 3D visualization model. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which predicts new gene interactions and disease mechanisms. The hypothesis generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates new research hypotheses. The treatment method proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal treatment method. The report generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates experimental results as a report. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0191] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0192] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0193] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0197] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0198] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0199] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0200] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0201] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0202] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0204] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0205] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0206] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0207] (Note 1) A unit for acquiring gene expression data, An analysis unit analyzes the gene expression data acquired by the acquisition unit, A prediction unit predicts the disease mechanism based on the data analyzed by the aforementioned analysis unit, A hypothesis generation unit generates a new research hypothesis based on the prediction results obtained by the prediction unit, A treatment method proposal unit that proposes the optimal treatment method based on the hypothesis generated by the hypothesis generation unit, The system includes a report generation unit that generates a report of experimental results based on the treatment proposed by the treatment proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We analyze complex gene expression patterns and create intuitively understandable 3D visualization models. The system described in Appendix 1, characterized by the features described herein. (Note 3) The prediction unit, By learning from large-scale biological databases, we can predict new gene interactions and disease mechanisms. The system described in Appendix 1, characterized by the features described herein. (Note 4) The hypothesis generation unit, Based on existing scientific literature and experimental data, we propose a new research hypothesis and present the optimal experimental design to test it. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned treatment method proposal unit, We propose the optimal treatment plan based on each patient's individual gene expression profile. The system described in Appendix 1, characterized by the features described herein. (Note 6) The report generation unit, Automatically analyzes experimental results and generates reports in the format of scientific papers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of gene expression data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze past gene expression data and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring gene expression data, filtering is performed based on the condition of the target tissue and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of gene expression data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring gene expression data, the geographical location information of the target tissue is taken into consideration to prioritize the acquisition of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring gene expression data, we analyze the social media activity of the target organization and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the gene expression data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of gene expression data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the gene expression data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of gene expression data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, It estimates the user's emotions and adjusts the prediction criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, When making predictions, consider the interrelationships of gene expression data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the attribute information of the person who submitted the gene expression data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the user's sentiment and adjusts the order in which the prediction results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the geographical distribution of gene expression data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, During prediction, we improve prediction accuracy by referring to relevant literature on gene expression data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The hypothesis generation unit, We estimate the user's emotions and adjust the hypothesis generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The hypothesis generation unit, When generating hypotheses, the hypothesis generation algorithm is optimized by referring to past hypothesis data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The hypothesis generation unit, When generating hypotheses, different hypothesis generation methods are applied to each category of gene expression data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The hypothesis generation unit, We estimate the user's emotions and prioritize hypotheses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The hypothesis generation unit, When generating hypotheses, weight them based on the timing of gene expression data submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The hypothesis generation unit, When generating hypotheses, we refer to relevant market data for gene expression data to generate hypotheses. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned treatment method proposal unit, The system estimates the user's emotions and adjusts the treatment suggestion method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned treatment method proposal unit, When proposing a treatment plan, we analyze the patient's past treatment history to select the most suitable treatment method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned treatment method proposal unit, When proposing a treatment plan, customize the treatment based on the patient's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned treatment method proposal unit, It estimates the user's emotions and determines the priority of treatments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned treatment method proposal unit, When proposing treatment options, the optimal treatment method will be selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned treatment method proposal unit, When proposing treatment options, we analyze the patient's social media activity to suggest the appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 37) The report generation unit, We estimate the user's emotions and adjust how reports are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The report generation unit, When generating reports, the report generation algorithm is optimized by referring to past report data. The system described in Appendix 1, characterized by the features described herein. (Note 39) The report generation unit, When generating reports, different report generation methods are applied for each category of gene expression data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The report generation unit, The system estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The report generation unit, When generating reports, the reports are weighted based on when the gene expression data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The report generation unit, When generating the report, the report is generated by referencing relevant market data for gene expression data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A unit for acquiring gene expression data, An analysis unit analyzes the gene expression data acquired by the acquisition unit, A prediction unit predicts the disease mechanism based on the data analyzed by the aforementioned analysis unit, A hypothesis generation unit generates a new research hypothesis based on the prediction results obtained by the prediction unit, A treatment method proposal unit proposes the optimal treatment method based on the hypothesis generated by the hypothesis generation unit, The system includes a report generation unit that generates a report of experimental results based on the treatment proposed by the treatment proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, We analyze complex gene expression patterns and create intuitively understandable 3D visualization models. The system according to feature 1.

3. The prediction unit, By learning from large-scale biological databases, we can predict new gene interactions and disease mechanisms. The system according to feature 1.

4. The hypothesis generation unit, Based on existing scientific literature and experimental data, we propose a new research hypothesis and present the optimal experimental design to test it. The system according to feature 1.

5. The aforementioned treatment method proposal unit, We propose the optimal treatment plan based on each patient's individual gene expression profile. The system according to feature 1.

6. The aforementioned report generation unit, Automatically analyzes experimental results and generates reports in the format of scientific papers. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of gene expression data acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze past gene expression data and select the optimal acquisition method. The system according to feature 1.

9. The acquisition unit is, When acquiring gene expression data, filtering is performed based on the condition of the target tissue and environmental conditions. The system according to feature 1.

10. The acquisition unit is, It estimates the user's emotions and determines the priority of gene expression data to acquire based on the estimated user emotions. The system according to feature 1.

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