system

Abstract

The system according to this embodiment aims to streamline the analysis of brain scan data and electrophysiological data, and to recommend relevant research and optimize experimental designs. [Solution] The system according to the embodiment comprises an analysis unit, a detection unit, a recommendation unit, an optimization unit, and a prediction unit. The analysis unit analyzes brain scan data or electrophysiological data. The detection unit detects anomalies based on the data analyzed by the analysis unit. The recommendation unit recommends relevant research from an existing research database. The optimization unit adjusts the experimental design. The prediction unit predicts the experimental results.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 conventional technology, there is a problem that the analysis of brain scan data and electrophysiological data requires time and effort, and the recommendation of related research and the optimization of experimental design have not been sufficiently carried out.

[0005] The system according to the embodiment aims to streamline the analysis of brain scan data and electrophysiological data and to perform the recommendation of related research and the optimization of experimental design.

Means for Solving the Problems

[0007] The system according to this embodiment can streamline the analysis of brain scan data and electrophysiological data, and can recommend relevant research and optimize experimental designs. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 neuroscience research support system according to an embodiment of the present invention is a system that performs automatic analysis of brain scan and electrophysiological data, recommends relevant research from existing research databases, optimizes experimental designs, and predicts results. The neuroscience research support system automatically analyzes brain scan data and electrophysiological data, enabling researchers to efficiently process large amounts of data and quickly extract important information. Furthermore, the neuroscience research support system recommends relevant research from existing research databases, allowing researchers to grasp the latest research trends and utilize them in their own research. In addition, the neuroscience research support system optimizes experimental designs and predicts results, enabling researchers to improve the efficiency of experiments and obtain more accurate results. For example, the neuroscience research support system receives brain scan data and electrophysiological data as input and analyzes them. For example, it can analyze the activity of specific brain regions and detect abnormal patterns. This allows researchers to quickly grasp brain function and abnormalities. Furthermore, the neuroscience research support system searches for and recommends relevant research from existing research databases based on keywords and themes entered by the researcher. For example, if a researcher is conducting research on a specific brain region, the system can recommend the latest research papers related to that region. This allows researchers to grasp the latest research trends and utilize them in their own research. Furthermore, the neuroscience research support system accepts the experimental design planned by the researcher as input and makes suggestions for optimizing that design. For example, optimizing the sample size and conditions of the experiment can lead to more accurate results. The neuroscience research support system also has a function to predict experimental results. This allows researchers to predict the results of their experiments in advance and adjust their experimental plans accordingly. In this way, the neuroscience research support system has functions such as automatic analysis of brain scans and electrophysiological data, recommendation of relevant research from existing research databases, optimization of experimental designs, and prediction of results, making it a powerful tool to support neuroscience research. As a result, the neuroscience research support system enables researchers to efficiently analyze data, grasp the latest research trends, optimize experimental designs, and predict results.

[0029] The neuroscience research support system according to this embodiment comprises an analysis unit, a detection unit, a recommendation unit, an optimization unit, and a prediction unit. The analysis unit analyzes brain scan data or electrophysiological data. The analysis unit can, for example, analyze the activity of a specific region of the brain using brain scan data. The analysis unit can also analyze brain activity patterns using electrophysiological data. For example, the analysis unit receives brain scan data as input and analyzes the activity of a specific brain region. The analysis unit can, for example, analyze the activity of a specific region of the brain and detect abnormal patterns. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can, for example, detect abnormal activity in a specific region of the brain. The detection unit can, for example, detect activity exceeding a specific threshold or abnormal patterns. The recommendation unit recommends relevant research from existing research databases. The recommendation unit can, for example, recommend relevant research based on keywords or themes entered by the researcher. The recommendation unit can, for example, recommend the latest research papers related to a specific brain region if the researcher is conducting research on that region. The optimization unit adjusts the experimental design. The optimization unit can, for example, optimize the sample size and conditions of an experiment. By optimizing the sample size of an experiment, the optimization unit can obtain more accurate results. The prediction unit predicts experimental results. The prediction unit can, for example, predict experimental results based on past experimental data. The prediction unit can, for example, predict experimental results based on past experimental data and adjust the experimental design. As a result, the neuroscience research support system according to this embodiment enables researchers to efficiently analyze data, grasp the latest research trends, optimize experimental designs, and predict results.

[0030] The analysis unit analyzes brain scan data or electrophysiological data. For example, the analysis unit can analyze the activity of specific brain regions using brain scan data. It can also analyze brain activity patterns using electrophysiological data. Specifically, brain scan data includes advanced image data such as fMRI (functional magnetic resonance imaging) and PET (positron emission tomography). This data provides detailed information on blood flow and metabolic activity in specific brain regions, and the analysis unit uses this data to analyze brain activity with high accuracy. For example, using fMRI data, it is possible to analyze brain activation patterns during specific cognitive tasks and quantitatively evaluate which regions are activated and to what extent. Electrophysiological data includes EEG (electroencephalography) and MEG (magnetoencephalography), which can capture the brain's electrical activity in real time. The analysis unit uses this data to analyze brain activity patterns and detect activity in specific frequency bands or abnormal spike patterns. For example, by analyzing EEG data, abnormal activity in specific frequency bands can be detected, allowing for the detection of precursors to epileptic seizures. The analysis unit can comprehensively analyze this data to gain a detailed understanding of the brain's complex activity patterns and detect abnormal patterns.

[0031] The detection unit detects anomalies based on data analyzed by the analysis unit. For example, the detection unit can detect abnormal activity in specific areas of the brain. Specifically, the detection unit sets a specific threshold based on brain scan data and electrophysiological data provided by the analysis unit, and detects activity or abnormal patterns that exceed that threshold. For example, this could include cases where blood flow in a specific area is abnormally increased in brain scan data, or where activity in a specific frequency band is abnormally high in electrophysiological data. The detection unit can detect these anomalies in real time and notify researchers. Furthermore, the detection unit can use machine learning algorithms to improve the accuracy of anomaly detection. For example, it can learn abnormal patterns using past data and detect anomalies with high accuracy in new data. This allows the detection unit to quickly and accurately detect abnormal brain activity and provide useful information to researchers.

[0032] The recommendation department recommends relevant research from existing research databases. For example, the recommendation department can recommend relevant research based on keywords or themes entered by researchers. Specifically, the recommendation department analyzes the keywords or themes entered by researchers and searches the database for related research papers and datasets. For example, if a researcher is conducting research on "prefrontal cortex activity," the recommendation department can recommend the latest research papers and datasets related to the prefrontal cortex. Furthermore, the recommendation department can recommend more appropriate research by considering the researcher's past research history and interests. For example, it can prioritize recommending highly relevant research based on the researcher's past research themes and cited papers. In this way, the recommendation department can help researchers efficiently find relevant research and grasp the latest research trends.

[0033] The optimization unit adjusts the experimental design. For example, it can optimize the sample size and conditions of an experiment. Specifically, the optimization unit calculates the optimal sample size according to the purpose and conditions of the experiment, improving the accuracy of the experiment. For example, by using statistical power analysis to calculate the required sample size and avoiding excessive sample sizes, it can reduce wasted resources. The optimization unit can also adjust the experimental conditions to improve the reproducibility of the experiment. For example, it can optimize the temperature, humidity, and lighting conditions of the experiment to reduce variability in experimental results. Furthermore, the optimization unit can monitor the progress of the experiment in real time and adjust the conditions as needed. In this way, the optimization unit can efficiently and effectively adjust the experimental design, helping researchers obtain more accurate results.

[0034] The prediction unit predicts experimental results. For example, the prediction unit can predict experimental results based on past experimental data. Specifically, the prediction unit analyzes past experimental data and builds a model to predict experimental results under specific conditions. For example, it can use machine learning algorithms to learn patterns from past data and make predictions for new experimental conditions. This allows the prediction unit to predict results before the experiment progresses and adjust the experimental plan. For example, the prediction unit can predict whether experimental results under specific conditions will fall within the expected range and adjust the experimental conditions as needed. Furthermore, the prediction unit can analyze data in real time during the experiment and update the prediction results. This allows the prediction unit to respond flexibly to the progress of the experiment and support researchers in conducting experiments efficiently.

[0035] The analysis unit can analyze the activity of specific regions of the brain. For example, the analysis unit can analyze the activity of specific regions of the brain using brain scan data. For example, the analysis unit can analyze the activity of specific regions of the brain and detect abnormal patterns. For example, the analysis unit can analyze the activity of specific regions of the brain and enable a detailed understanding of brain function. This makes it possible to understand brain function in detail by analyzing the activity of specific regions of the brain. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input brain scan data into a generating AI and have the generating AI perform the analysis of the activity of specific regions of the brain.

[0036] The detection unit can detect abnormal activity in specific areas of the brain. For example, the detection unit can detect abnormal activity in specific areas of the brain. For example, the detection unit can detect activity exceeding a specific threshold or abnormal patterns. For example, the detection unit can detect abnormal activity in specific areas of the brain, enabling early detection of abnormalities. This makes it possible to detect abnormalities early by detecting abnormal activity in specific areas of the brain. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input brain scan data into a generating AI and have the generating AI perform the detection of abnormal activity in specific areas of the brain.

[0037] The recommendation system can recommend relevant research based on keywords or themes entered by researchers. For example, the recommendation system can recommend relevant research based on keywords or themes entered by researchers. For example, if a researcher is conducting research on a specific brain region, the recommendation system can recommend the latest research papers related to that region. The recommendation system can keep up with the latest research trends by recommending relevant research based on keywords or themes entered by researchers. This allows researchers to keep up with the latest research trends by recommending relevant research based on keywords or themes entered by researchers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input keywords or themes entered by researchers into a generating AI and have the generating AI recommend relevant research.

[0038] The optimization unit can adjust the sample size or conditions of the experiment. For example, the optimization unit optimizes the sample size and conditions of the experiment. For example, by optimizing the sample size of the experiment, the optimization unit can obtain more accurate results. For example, by optimizing the experimental conditions, the optimization unit can improve the efficiency of the experiment. Thus, by optimizing the sample size and conditions of the experiment, the efficiency of the experiment is improved. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the experimental design into a generating AI and have the generating AI perform the optimization of the sample size and conditions.

[0039] The prediction unit can predict experimental results based on past experimental data. For example, the prediction unit can predict experimental results based on past experimental data. For example, the prediction unit can predict experimental results based on past experimental data and adjust the experimental plan. For example, the prediction unit can predict experimental results based on past experimental data and improve the efficiency of the experiment. This makes it possible to adjust the experimental plan by predicting experimental results based on past experimental data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input past experimental data into a generating AI and have the generating AI perform the prediction of experimental results.

[0040] The analysis unit can analyze specific electroencephalogram (EEG) patterns in real time when acquiring brain scan data and provide immediate feedback. For example, the analysis unit can analyze EEG data in real time and immediately detect specific abnormal patterns. For example, the analysis unit can analyze EEG fluctuations in real time and provide feedback to the user. For example, the analysis unit can analyze EEG data in real time and immediately evaluate the activity of specific brain regions. This enables rapid response by analyzing EEG patterns in real time and providing immediate feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input EEG data into a generating AI and have the generating AI perform real-time analysis and feedback.

[0041] The analysis unit can perform detailed analysis for each different frequency band when analyzing electrophysiological data and extract specific activity patterns. For example, the analysis unit can divide the electrophysiological data into frequency bands and analyze the activity patterns of each band. For example, the analysis unit can analyze data from different frequency bands and extract specific brain activity patterns. For example, the analysis unit can analyze the electrophysiological data for each frequency band and detect abnormal activity patterns. This allows for the extraction of specific activity patterns by performing detailed analysis for each different frequency band. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input electrophysiological data into a generating AI and have the generating AI perform analysis for each frequency band.

[0042] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis. For example, the analysis unit can correct the analysis results by referring to the user's past health data. For example, the analysis unit can optimize the analysis algorithm based on the user's past health data. For example, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past health data. As a result, the accuracy of the analysis is improved by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0043] The analysis unit can customize the analysis results by taking into account the user's lifestyle data during the analysis. For example, the analysis unit can refer to the user's lifestyle data and customize the analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's lifestyle data. For example, the analysis unit can personalize the analysis results by taking into account the user's lifestyle data. In this way, the analysis results can be personalized by taking into account the user's lifestyle data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the analysis results.

[0044] The detection unit can detect different abnormal patterns in specific regions of the brain when an anomaly is detected. The detection unit can, for example, analyze the abnormal patterns in specific regions of the brain and detect the anomaly. The detection unit can, for example, analyze data from different brain regions and detect specific abnormal patterns. The detection unit can, for example, detect abnormal patterns in specific regions of the brain and identify the type of anomaly. In this way, the type of anomaly can be identified by detecting different abnormal patterns in specific regions of the brain. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input brain scan data into a generating AI and have the generating AI perform the detection of abnormal patterns.

[0045] The detection unit can optimize its detection algorithm by referring to past anomaly data when an anomaly is detected. For example, the detection unit can optimize its detection algorithm by referring to past anomaly data. For example, the detection unit can improve the accuracy of anomaly detection based on past anomaly data. For example, the detection unit can adjust its anomaly detection algorithm by referring to past anomaly data. This improves the accuracy of the detection algorithm by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past anomaly data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0046] The detection unit can identify the type of anomaly by considering the user's genetic information when an anomaly is detected. The detection unit can, for example, refer to the user's genetic information to identify the type of anomaly. The detection unit can, for example, improve the accuracy of anomaly detection based on the user's genetic information. The detection unit can, for example, consider the user's genetic information to identify the type of anomaly in detail. This allows for detailed identification of the type of anomaly by considering the user's genetic information. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's genetic information into a generating AI and have the generating AI perform the identification of the type of anomaly.

[0047] The detection unit can identify the cause of an anomaly by referring to the user's environmental data when an anomaly is detected. The detection unit can, for example, refer to the user's environmental data to identify the cause of the anomaly. The detection unit can, for example, improve the accuracy of anomaly detection based on the user's environmental data. The detection unit can, for example, take the user's environmental data into consideration to identify the cause of the anomaly in detail. This allows the cause of the anomaly to be identified in detail by referring to the user's environmental data. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's environmental data into a generating AI and have the generating AI perform the identification of the cause of the anomaly.

[0048] The recommendation system can, when making recommendations, refer to the user's past research history to prioritize recommending highly relevant research. For example, the recommendation system can refer to the user's past research history and recommend highly relevant research. For example, the recommendation system can recommend the latest relevant research based on the user's past research history. For example, the recommendation system can refer to the user's past research history and prioritize recommending the most relevant research. This allows the recommendation system to prioritize recommending highly relevant research by referring to the user's past research history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's past research history into a generating AI and have the generating AI recommend highly relevant research.

[0049] The recommendation system can collect information from different databases based on the user's research topic during the recommendation process. For example, the recommendation system can collect relevant information from multiple databases based on the user's research topic. For example, the recommendation system can collect the latest research information from different databases based on the user's research topic. For example, the recommendation system can collect information from the most suitable database depending on the user's research topic. This allows the system to provide more relevant information by collecting information from different databases based on the user's research topic. Some or all of the above-described processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's research topic into a generating AI and have the generating AI collect the information.

[0050] The recommendation system can prioritize referencing the user's institution's research database when making recommendations. For example, the recommendation system can prioritize referencing the user's institution's research database and recommend relevant research. For example, the recommendation system can provide the latest research information based on the user's institution's database. For example, the recommendation system can prioritize referencing the user's institution's database and recommend the most relevant research. This allows for the recommendation of more relevant research by prioritizing the referencing of the user's institution's research database. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's institution's database into a generating AI and have the generating AI perform the recommendation of relevant research.

[0051] The recommendation unit can provide information by referring to patent databases related to the user's research field when making recommendations. For example, the recommendation unit can refer to patent databases related to the user's research field and provide relevant information. For example, the recommendation unit can provide the latest information from patent databases based on the user's research field. For example, the recommendation unit can provide information from the most suitable patent database depending on the user's research field. This allows for the provision of more comprehensive information by also referring to patent databases related to the user's research field. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's research field into a generating AI and have the generating AI perform the task of providing information from patent databases.

[0052] The optimization unit can determine the optimal sample size by referring to past experimental data when optimizing the experimental design. For example, the optimization unit can determine the optimal sample size by referring to past experimental data. For example, the optimization unit can optimize the sample size based on past experimental data. For example, the optimization unit can improve the accuracy of the sample size by referring to past experimental data. This allows the optimal sample size to be determined by referring to past experimental data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input past experimental data into a generating AI and have the generating AI perform the sample size optimization.

[0053] The optimization unit can perform simulations for different condition settings when optimizing the experimental design and select the optimal conditions. For example, the optimization unit can perform simulations for different condition settings and select the optimal conditions. For example, the optimization unit can perform simulations for each condition setting and optimize the experimental design. For example, the optimization unit can simulate different condition settings and select the optimal experimental conditions. In this way, the optimal conditions can be selected by performing simulations for each different condition setting. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input experimental conditions into a generating AI and have the generating AI execute the simulation.

[0054] The optimization unit can apply optimization algorithms specific to the user's research field when optimizing the experimental design. For example, the optimization unit can optimize the experimental design by applying optimization algorithms specific to the user's research field. For example, the optimization unit can improve the accuracy of the experimental design by using algorithms specific to the research field. For example, the optimization unit can adjust the experimental design by applying optimization algorithms appropriate to the user's research field. As a result, the accuracy of the experimental design is improved by applying optimization algorithms specific to the user's research field. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input an optimization algorithm specific to the research field into a generating AI and have the generating AI perform the optimization of the experimental design.

[0055] The optimization unit can propose an optimal design when optimizing experimental designs, taking into account the user's research resources. For example, the optimization unit proposes an optimal experimental design by considering the user's research resources. For example, the optimization unit can optimize experimental designs based on research resources. For example, the optimization unit can provide an optimal experimental design by referring to the user's research resources. In this way, by considering the user's research resources, it can propose an optimal experimental design. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the user's research resources into a generating AI and have the generating AI execute the proposal of an optimal design.

[0056] The prediction unit can optimize its prediction algorithm by referring to past experimental data during prediction. For example, the prediction unit can optimize its prediction algorithm by referring to past experimental data. For example, the prediction unit can improve the accuracy of its prediction algorithm based on past experimental data. For example, the prediction unit can adjust its prediction algorithm by referring to past experimental data. This improves the accuracy of the prediction algorithm by referring to past experimental data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input past experimental data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0057] The prediction unit can compare prediction results for different experimental conditions during prediction and select the optimal conditions. For example, the prediction unit can compare prediction results for different experimental conditions and select the optimal conditions. For example, the prediction unit can compare prediction results for each experimental condition and select the optimal experimental conditions. For example, the prediction unit can compare different experimental conditions and provide the optimal prediction result. In this way, the optimal conditions can be selected by comparing prediction results for different experimental conditions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input experimental conditions into a generating AI and have the generating AI perform the comparison of prediction results.

[0058] The prediction unit can improve prediction accuracy by referring to external data related to the user's research topic during prediction. For example, the prediction unit can improve prediction accuracy by referring to external data related to the user's research topic. For example, the prediction unit can optimize the prediction algorithm based on external data related to the research topic. For example, the prediction unit can improve prediction accuracy by referring to external data corresponding to the user's research topic. As a result, prediction accuracy is improved by referring to external data related to the user's research topic. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input external data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0059] The prediction unit can apply a prediction algorithm specific to the user's research field during prediction. For example, the prediction unit can improve prediction accuracy by applying a prediction algorithm specific to the user's research field. For example, the prediction unit can improve the accuracy of prediction results by using an algorithm specific to the research field. For example, the prediction unit can apply a prediction algorithm appropriate to the user's research field and provide prediction results. This improves prediction accuracy by applying a prediction algorithm specific to the user's research field. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input a prediction algorithm specific to the research field into a generating AI and have the generating AI perform the task of providing prediction results.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The neuroscience research support system can also be equipped with a data visualization unit. This unit visually displays data obtained from the analysis and detection units, allowing researchers to intuitively understand data trends and anomalies. For example, it can generate a 3D model of brain scan data and display the activity of specific brain regions using color coding. It can also graph the temporal fluctuations of electrophysiological data and highlight abnormal patterns. Furthermore, the data visualization unit can display recommendations for related studies in graphs and charts, enabling researchers to easily compare and contrast them. This allows researchers to deepen their understanding of the data and conduct research more efficiently through data visualization.

[0062] The neuroscience research support system can also be equipped with a data filtering unit. This unit filters data obtained from the analysis and detection units based on specific criteria, allowing researchers to extract only the information they need. For example, it can extract activity data from specific brain regions and exclude other data. It can also extract data within a specific time range to detect abnormal patterns. Furthermore, the data filtering unit can filter recommendations for related research, allowing researchers to obtain information focused on topics of interest. This enables researchers to efficiently acquire necessary information through data filtering, thereby improving the accuracy of their research.

[0063] The neuroscience research support system can also include a data integration unit. This unit integrates data obtained from the analysis and detection units to provide comprehensive analysis results. For example, it can integrate brain scan data and electrophysiological data to analyze the relationship between the activity of specific brain regions and their electrical activity. It can also integrate data obtained under different experimental conditions to compare differences between conditions. Furthermore, the data integration unit can integrate recommendations for related studies, enabling researchers to obtain comprehensive information. This allows researchers to gain deeper insights through data integration and improve the quality of their research.

[0064] The neuroscience research support system can also be equipped with a data sharing unit. This unit facilitates collaborative research by sharing data obtained from the analysis and detection units with other researchers. For example, it can share activity data from specific brain regions, allowing other researchers to perform different analyses using the same data. It can also share electrophysiological data, enabling comparison of analysis results from different perspectives. Furthermore, the data sharing unit can share recommendations for related research, promoting information exchange among researchers. This allows researchers to collaborate on research and conduct analyses from a more multifaceted perspective through data sharing.

[0065] The neuroscience research support system can also be equipped with a data backup unit. This unit periodically backs up data obtained from the analysis and detection units, ensuring data security. For example, it can regularly back up brain scan data and electrophysiological data to prevent data loss or corruption. It can also back up recommendations for related studies, allowing users to refer to past recommendations. Furthermore, the data backup unit can back up experimental designs and predictive data, recording the progress of the research. Through data backup, researchers can ensure data security and proceed with their research with confidence.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit analyzes brain scan data or electrophysiological data. For example, the analysis unit can analyze the activity of a specific region of the brain using brain scan data. It can also analyze brain activity patterns using electrophysiological data. Step 2: The detection unit detects anomalies based on the data analyzed by the analysis unit. For example, the detection unit can detect abnormal activity in a specific area of ​​the brain. It can detect activity exceeding a specific threshold or abnormal patterns. Step 3: The recommendation team recommends relevant research from existing research databases. For example, the recommendation team can recommend relevant research based on keywords or themes entered by the researcher. If the researcher is working on a specific brain region, the recommendation team can recommend the latest research papers related to that region. Step 4: The optimization unit adjusts the experimental design. For example, the optimization unit can optimize the sample size and conditions of the experiment. By optimizing the sample size of the experiment, more accurate results can be obtained. Step 5: The prediction unit predicts the experimental results. The prediction unit can predict experimental results based on, for example, past experimental data. It can predict experimental results based on past experimental data and adjust the experimental plan accordingly.

[0068] (Example of form 2) The neuroscience research support system according to an embodiment of the present invention is a system that performs automatic analysis of brain scan and electrophysiological data, recommends relevant research from existing research databases, optimizes experimental designs, and predicts results. The neuroscience research support system automatically analyzes brain scan data and electrophysiological data, enabling researchers to efficiently process large amounts of data and quickly extract important information. Furthermore, the neuroscience research support system recommends relevant research from existing research databases, allowing researchers to grasp the latest research trends and utilize them in their own research. In addition, the neuroscience research support system optimizes experimental designs and predicts results, enabling researchers to improve the efficiency of experiments and obtain more accurate results. For example, the neuroscience research support system receives brain scan data and electrophysiological data as input and analyzes them. For example, it can analyze the activity of specific brain regions and detect abnormal patterns. This allows researchers to quickly grasp brain function and abnormalities. Furthermore, the neuroscience research support system searches for and recommends relevant research from existing research databases based on keywords and themes entered by the researcher. For example, if a researcher is conducting research on a specific brain region, the system can recommend the latest research papers related to that region. This allows researchers to grasp the latest research trends and utilize them in their own research. Furthermore, the neuroscience research support system accepts the experimental design planned by the researcher as input and makes suggestions for optimizing that design. For example, optimizing the sample size and conditions of the experiment can lead to more accurate results. The neuroscience research support system also has a function to predict experimental results. This allows researchers to predict the results of their experiments in advance and adjust their experimental plans accordingly. In this way, the neuroscience research support system has functions such as automatic analysis of brain scans and electrophysiological data, recommendation of relevant research from existing research databases, optimization of experimental designs, and prediction of results, making it a powerful tool to support neuroscience research. As a result, the neuroscience research support system enables researchers to efficiently analyze data, grasp the latest research trends, optimize experimental designs, and predict results.

[0069] The neuroscience research support system according to this embodiment comprises an analysis unit, a detection unit, a recommendation unit, an optimization unit, and a prediction unit. The analysis unit analyzes brain scan data or electrophysiological data. The analysis unit can, for example, analyze the activity of a specific region of the brain using brain scan data. The analysis unit can also analyze brain activity patterns using electrophysiological data. For example, the analysis unit receives brain scan data as input and analyzes the activity of a specific brain region. The analysis unit can, for example, analyze the activity of a specific region of the brain and detect abnormal patterns. The detection unit detects anomalies based on the data analyzed by the analysis unit. The detection unit can, for example, detect abnormal activity in a specific region of the brain. The detection unit can, for example, detect activity exceeding a specific threshold or abnormal patterns. The recommendation unit recommends relevant research from existing research databases. The recommendation unit can, for example, recommend relevant research based on keywords or themes entered by the researcher. The recommendation unit can, for example, recommend the latest research papers related to a specific brain region if the researcher is conducting research on that region. The optimization unit adjusts the experimental design. The optimization unit can, for example, optimize the sample size and conditions of an experiment. By optimizing the sample size of an experiment, the optimization unit can obtain more accurate results. The prediction unit predicts experimental results. The prediction unit can, for example, predict experimental results based on past experimental data. The prediction unit can, for example, predict experimental results based on past experimental data and adjust the experimental design. As a result, the neuroscience research support system according to this embodiment enables researchers to efficiently analyze data, grasp the latest research trends, optimize experimental designs, and predict results.

[0070] The analysis unit analyzes brain scan data or electrophysiological data. For example, the analysis unit can analyze the activity of specific brain regions using brain scan data. It can also analyze brain activity patterns using electrophysiological data. Specifically, brain scan data includes advanced image data such as fMRI (functional magnetic resonance imaging) and PET (positron emission tomography). This data provides detailed information on blood flow and metabolic activity in specific brain regions, and the analysis unit uses this data to analyze brain activity with high accuracy. For example, using fMRI data, it is possible to analyze brain activation patterns during specific cognitive tasks and quantitatively evaluate which regions are activated and to what extent. Electrophysiological data includes EEG (electroencephalography) and MEG (magnetoencephalography), which can capture the brain's electrical activity in real time. The analysis unit uses this data to analyze brain activity patterns and detect activity in specific frequency bands or abnormal spike patterns. For example, by analyzing EEG data, abnormal activity in specific frequency bands can be detected, allowing for the detection of precursors to epileptic seizures. The analysis unit can comprehensively analyze this data to gain a detailed understanding of the brain's complex activity patterns and detect abnormal patterns.

[0071] The detection unit detects anomalies based on data analyzed by the analysis unit. For example, the detection unit can detect abnormal activity in specific areas of the brain. Specifically, the detection unit sets a specific threshold based on brain scan data and electrophysiological data provided by the analysis unit, and detects activity or abnormal patterns that exceed that threshold. For example, this could include cases where blood flow in a specific area is abnormally increased in brain scan data, or where activity in a specific frequency band is abnormally high in electrophysiological data. The detection unit can detect these anomalies in real time and notify researchers. Furthermore, the detection unit can use machine learning algorithms to improve the accuracy of anomaly detection. For example, it can learn abnormal patterns using past data and detect anomalies with high accuracy in new data. This allows the detection unit to quickly and accurately detect abnormal brain activity and provide useful information to researchers.

[0072] The recommendation department recommends relevant research from existing research databases. For example, the recommendation department can recommend relevant research based on keywords or themes entered by researchers. Specifically, the recommendation department analyzes the keywords or themes entered by researchers and searches the database for related research papers and datasets. For example, if a researcher is conducting research on "prefrontal cortex activity," the recommendation department can recommend the latest research papers and datasets related to the prefrontal cortex. Furthermore, the recommendation department can recommend more appropriate research by considering the researcher's past research history and interests. For example, it can prioritize recommending highly relevant research based on the researcher's past research themes and cited papers. In this way, the recommendation department can help researchers efficiently find relevant research and grasp the latest research trends.

[0073] The optimization unit adjusts the experimental design. For example, it can optimize the sample size and conditions of an experiment. Specifically, the optimization unit calculates the optimal sample size according to the purpose and conditions of the experiment, improving the accuracy of the experiment. For example, by using statistical power analysis to calculate the required sample size and avoiding excessive sample sizes, it can reduce wasted resources. The optimization unit can also adjust the experimental conditions to improve the reproducibility of the experiment. For example, it can optimize the temperature, humidity, and lighting conditions of the experiment to reduce variability in experimental results. Furthermore, the optimization unit can monitor the progress of the experiment in real time and adjust the conditions as needed. In this way, the optimization unit can efficiently and effectively adjust the experimental design, helping researchers obtain more accurate results.

[0074] The prediction unit predicts experimental results. For example, the prediction unit can predict experimental results based on past experimental data. Specifically, the prediction unit analyzes past experimental data and builds a model to predict experimental results under specific conditions. For example, it can use machine learning algorithms to learn patterns from past data and make predictions for new experimental conditions. This allows the prediction unit to predict results before the experiment progresses and adjust the experimental plan. For example, the prediction unit can predict whether experimental results under specific conditions will fall within the expected range and adjust the experimental conditions as needed. Furthermore, the prediction unit can analyze data in real time during the experiment and update the prediction results. This allows the prediction unit to respond flexibly to the progress of the experiment and support researchers in conducting experiments efficiently.

[0075] The analysis unit can analyze the activity of specific regions of the brain. For example, the analysis unit can analyze the activity of specific regions of the brain using brain scan data. For example, the analysis unit can analyze the activity of specific regions of the brain and detect abnormal patterns. For example, the analysis unit can analyze the activity of specific regions of the brain and enable a detailed understanding of brain function. This makes it possible to understand brain function in detail by analyzing the activity of specific regions of the brain. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input brain scan data into a generating AI and have the generating AI perform the analysis of the activity of specific regions of the brain.

[0076] The detection unit can detect abnormal activity in specific areas of the brain. For example, the detection unit can detect abnormal activity in specific areas of the brain. For example, the detection unit can detect activity exceeding a specific threshold or abnormal patterns. For example, the detection unit can detect abnormal activity in specific areas of the brain, enabling early detection of abnormalities. This makes it possible to detect abnormalities early by detecting abnormal activity in specific areas of the brain. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input brain scan data into a generating AI and have the generating AI perform the detection of abnormal activity in specific areas of the brain.

[0077] The recommendation system can recommend relevant research based on keywords or themes entered by researchers. For example, the recommendation system can recommend relevant research based on keywords or themes entered by researchers. For example, if a researcher is conducting research on a specific brain region, the recommendation system can recommend the latest research papers related to that region. The recommendation system can keep up with the latest research trends by recommending relevant research based on keywords or themes entered by researchers. This allows researchers to keep up with the latest research trends by recommending relevant research based on keywords or themes entered by researchers. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input keywords or themes entered by researchers into a generating AI and have the generating AI recommend relevant research.

[0078] The optimization unit can adjust the sample size or conditions of the experiment. For example, the optimization unit optimizes the sample size and conditions of the experiment. For example, by optimizing the sample size of the experiment, the optimization unit can obtain more accurate results. For example, by optimizing the experimental conditions, the optimization unit can improve the efficiency of the experiment. Thus, by optimizing the sample size and conditions of the experiment, the efficiency of the experiment is improved. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the experimental design into a generating AI and have the generating AI perform the optimization of the sample size and conditions.

[0079] The prediction unit can predict experimental results based on past experimental data. For example, the prediction unit can predict experimental results based on past experimental data. For example, the prediction unit can predict experimental results based on past experimental data and adjust the experimental plan. For example, the prediction unit can predict experimental results based on past experimental data and improve the efficiency of the experiment. This makes it possible to adjust the experimental plan by predicting experimental results based on past experimental data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input past experimental data into a generating AI and have the generating AI perform the prediction of experimental results.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize the analysis of important data and provide results quickly. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive results. For example, if the user is in a hurry, the analysis unit can perform a simplified analysis and provide a quick overview. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions. 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 AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The analysis unit can analyze specific electroencephalogram (EEG) patterns in real time when acquiring brain scan data and provide immediate feedback. For example, the analysis unit can analyze EEG data in real time and immediately detect specific abnormal patterns. For example, the analysis unit can analyze EEG fluctuations in real time and provide feedback to the user. For example, the analysis unit can analyze EEG data in real time and immediately evaluate the activity of specific brain regions. This enables rapid response by analyzing EEG patterns in real time and providing immediate feedback. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input EEG data into a generating AI and have the generating AI perform real-time analysis and feedback.

[0082] The analysis unit can perform detailed analysis for each different frequency band when analyzing electrophysiological data and extract specific activity patterns. For example, the analysis unit can divide the electrophysiological data into frequency bands and analyze the activity patterns of each band. For example, the analysis unit can analyze data from different frequency bands and extract specific brain activity patterns. For example, the analysis unit can analyze the electrophysiological data for each frequency band and detect abnormal activity patterns. This allows for the extraction of specific activity patterns by performing detailed analysis for each different frequency band. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input electrophysiological data into a generating AI and have the generating AI perform analysis for each frequency band.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis. For example, the analysis unit can correct the analysis results by referring to the user's past health data. For example, the analysis unit can optimize the analysis algorithm based on the user's past health data. For example, the analysis unit can improve the accuracy of anomaly detection by referring to the user's past health data. As a result, the accuracy of the analysis is improved by referring to the user's past health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past health data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0085] The analysis unit can customize the analysis results by taking into account the user's lifestyle data during the analysis. For example, the analysis unit can refer to the user's lifestyle data and customize the analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's lifestyle data. For example, the analysis unit can personalize the analysis results by taking into account the user's lifestyle data. In this way, the analysis results can be personalized by taking into account the user's lifestyle data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the analysis results.

[0086] The detection unit can estimate the user's emotions and adjust the anomaly detection threshold based on the estimated user emotions. For example, if the user is stressed, the detection unit can set the anomaly detection threshold low to detect anomalies early. For example, if the user is relaxed, the detection unit can set the anomaly detection threshold high to perform a more detailed analysis. For example, if the user is in a hurry, the detection unit can adjust the anomaly detection threshold to provide results quickly. By adjusting the anomaly detection threshold according to the user's emotions, more appropriate anomaly detection 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The detection unit can detect different abnormal patterns in specific regions of the brain when an anomaly is detected. The detection unit can, for example, analyze the abnormal patterns in specific regions of the brain and detect the anomaly. The detection unit can, for example, analyze data from different brain regions and detect specific abnormal patterns. The detection unit can, for example, detect abnormal patterns in specific regions of the brain and identify the type of anomaly. In this way, the type of anomaly can be identified by detecting different abnormal patterns in specific regions of the brain. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input brain scan data into a generating AI and have the generating AI perform the detection of abnormal patterns.

[0088] The detection unit can optimize its detection algorithm by referring to past anomaly data when an anomaly is detected. For example, the detection unit can optimize its detection algorithm by referring to past anomaly data. For example, the detection unit can improve the accuracy of anomaly detection based on past anomaly data. For example, the detection unit can adjust its anomaly detection algorithm by referring to past anomaly data. This improves the accuracy of the detection algorithm by referring to past anomaly data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input past anomaly data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0089] The detection unit can estimate the user's emotions and adjust the order in which anomaly detection results are displayed based on the estimated user emotions. For example, if the user is tense, the detection unit can prioritize displaying important anomalies. For example, if the user is relaxed, the detection unit can sequentially display detailed anomaly information. For example, if the user is in a hurry, the detection unit can quickly display concise anomaly information. By adjusting the order in which anomaly detection results are displayed according to the user's emotions, a more appropriate display 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The detection unit can identify the type of anomaly by considering the user's genetic information when an anomaly is detected. The detection unit can, for example, refer to the user's genetic information to identify the type of anomaly. The detection unit can, for example, improve the accuracy of anomaly detection based on the user's genetic information. The detection unit can, for example, consider the user's genetic information to identify the type of anomaly in detail. This allows for detailed identification of the type of anomaly by considering the user's genetic information. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's genetic information into a generating AI and have the generating AI perform the identification of the type of anomaly.

[0091] The detection unit can identify the cause of an anomaly by referring to the user's environmental data when an anomaly is detected. The detection unit can, for example, refer to the user's environmental data to identify the cause of the anomaly. The detection unit can, for example, improve the accuracy of anomaly detection based on the user's environmental data. The detection unit can, for example, take the user's environmental data into consideration to identify the cause of the anomaly in detail. This allows the cause of the anomaly to be identified in detail by referring to the user's environmental data. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the user's environmental data into a generating AI and have the generating AI perform the identification of the cause of the anomaly.

[0092] The recommendation system can estimate the user's emotions and adjust the priority of recommended research based on those emotions. For example, if the user is stressed, the recommendation system may prioritize recommending important research. If the user is relaxed, the recommendation system may prioritize recommending detailed research. If the user is in a hurry, the recommendation system may prioritize recommending concise research. By adjusting the priority of recommended research according to the user's emotions, more appropriate research can be recommended. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The recommendation system can, when making recommendations, refer to the user's past research history to prioritize recommending highly relevant research. For example, the recommendation system can refer to the user's past research history and recommend highly relevant research. For example, the recommendation system can recommend the latest relevant research based on the user's past research history. For example, the recommendation system can refer to the user's past research history and prioritize recommending the most relevant research. This allows the recommendation system to prioritize recommending highly relevant research by referring to the user's past research history. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's past research history into a generating AI and have the generating AI recommend highly relevant research.

[0094] The recommendation system can collect information from different databases based on the user's research topic during the recommendation process. For example, the recommendation system can collect relevant information from multiple databases based on the user's research topic. For example, the recommendation system can collect the latest research information from different databases based on the user's research topic. For example, the recommendation system can collect information from the most suitable database depending on the user's research topic. This allows the system to provide more relevant information by collecting information from different databases based on the user's research topic. Some or all of the above-described processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's research topic into a generating AI and have the generating AI collect the information.

[0095] The recommendation section can estimate the user's emotions and adjust how recommended research is displayed based on the estimated emotions. For example, if the user is stressed, the recommendation section can provide a simple and highly visible display. If the user is relaxed, the recommendation section can provide a display that includes detailed information. If the user is in a hurry, the recommendation section can provide a concise display. By adjusting how recommended research is displayed according to the user's emotions, a more appropriate display 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 recommendation section may be performed using AI or not using AI. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The recommendation system can prioritize referencing the user's institution's research database when making recommendations. For example, the recommendation system can prioritize referencing the user's institution's research database and recommend relevant research. For example, the recommendation system can provide the latest research information based on the user's institution's database. For example, the recommendation system can prioritize referencing the user's institution's database and recommend the most relevant research. This allows for the recommendation of more relevant research by prioritizing the referencing of the user's institution's research database. Some or all of the above processes in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input the user's institution's database into a generating AI and have the generating AI perform the recommendation of relevant research.

[0097] The recommendation unit can provide information by referring to patent databases related to the user's research field when making recommendations. For example, the recommendation unit can refer to patent databases related to the user's research field and provide relevant information. For example, the recommendation unit can provide the latest information from patent databases based on the user's research field. For example, the recommendation unit can provide information from the most suitable patent database depending on the user's research field. This allows for the provision of more comprehensive information by also referring to patent databases related to the user's research field. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the user's research field into a generating AI and have the generating AI perform the task of providing information from patent databases.

[0098] The optimization unit can estimate the user's emotions and adjust the method for optimizing the experimental design based on the estimated user emotions. For example, if the user is stressed, the optimization unit can suggest a simple experimental design. For example, if the user is relaxed, the optimization unit can suggest a detailed experimental design. For example, if the user is in a hurry, the optimization unit can suggest a quickly executable experimental design. This allows for more appropriate experimental designs by adjusting the method for optimizing the experimental design according to the user's emotions. 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 optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The optimization unit can determine the optimal sample size by referring to past experimental data when optimizing the experimental design. For example, the optimization unit can determine the optimal sample size by referring to past experimental data. For example, the optimization unit can optimize the sample size based on past experimental data. For example, the optimization unit can improve the accuracy of the sample size by referring to past experimental data. This allows the optimal sample size to be determined by referring to past experimental data. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input past experimental data into a generating AI and have the generating AI perform the sample size optimization.

[0100] The optimization unit can perform simulations for different condition settings when optimizing the experimental design and select the optimal conditions. For example, the optimization unit can perform simulations for different condition settings and select the optimal conditions. For example, the optimization unit can perform simulations for each condition setting and optimize the experimental design. For example, the optimization unit can simulate different condition settings and select the optimal experimental conditions. In this way, the optimal conditions can be selected by performing simulations for each different condition setting. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input experimental conditions into a generating AI and have the generating AI execute the simulation.

[0101] The optimization unit can estimate the user's emotions and prioritize experimental designs based on the estimated emotions. For example, if the user is stressed, the optimization unit may prioritize important experimental designs. If the user is relaxed, the optimization unit may prioritize detailed experimental designs. If the user is in a hurry, the optimization unit may prioritize experimental designs that can be executed quickly. This allows for more appropriate experimental designs by prioritizing experimental designs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The optimization unit can apply optimization algorithms specific to the user's research field when optimizing the experimental design. For example, the optimization unit can optimize the experimental design by applying optimization algorithms specific to the user's research field. For example, the optimization unit can improve the accuracy of the experimental design by using algorithms specific to the research field. For example, the optimization unit can adjust the experimental design by applying optimization algorithms appropriate to the user's research field. As a result, the accuracy of the experimental design is improved by applying optimization algorithms specific to the user's research field. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input an optimization algorithm specific to the research field into a generating AI and have the generating AI perform the optimization of the experimental design.

[0103] The optimization unit can propose an optimal design when optimizing experimental designs, taking into account the user's research resources. For example, the optimization unit proposes an optimal experimental design by considering the user's research resources. For example, the optimization unit can optimize experimental designs based on research resources. For example, the optimization unit can provide an optimal experimental design by referring to the user's research resources. In this way, by considering the user's research resources, it can propose an optimal experimental design. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the user's research resources into a generating AI and have the generating AI execute the proposal of an optimal design.

[0104] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit can provide a display method that gets straight to the point. By adjusting the display method of the prediction results according to the user's emotions, a more appropriate display 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0105] The prediction unit can optimize its prediction algorithm by referring to past experimental data during prediction. For example, the prediction unit can optimize its prediction algorithm by referring to past experimental data. For example, the prediction unit can improve the accuracy of its prediction algorithm based on past experimental data. For example, the prediction unit can adjust its prediction algorithm by referring to past experimental data. This improves the accuracy of the prediction algorithm by referring to past experimental data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input past experimental data into a generating AI and have the generating AI perform the optimization of the prediction algorithm.

[0106] The prediction unit can compare prediction results for different experimental conditions during prediction and select the optimal conditions. For example, the prediction unit can compare prediction results for different experimental conditions and select the optimal conditions. For example, the prediction unit can compare prediction results for each experimental condition and select the optimal experimental conditions. For example, the prediction unit can compare different experimental conditions and provide the optimal prediction result. In this way, the optimal conditions can be selected by comparing prediction results for different experimental conditions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input experimental conditions into a generating AI and have the generating AI perform the comparison of prediction results.

[0107] The prediction unit can estimate the user's emotions and prioritize prediction results based on the estimated emotions. For example, if the user is stressed, the prediction unit can prioritize displaying important prediction results. For example, if the user is relaxed, the prediction unit can prioritize displaying detailed prediction results. For example, if the user is in a hurry, the prediction unit can prioritize displaying concise prediction results. By prioritizing prediction results according to the user's emotions, more appropriate prediction results can be obtained. Emotion estimation is implemented 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 AI, for example, or without AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The prediction unit can improve prediction accuracy by referring to external data related to the user's research topic during prediction. For example, the prediction unit can improve prediction accuracy by referring to external data related to the user's research topic. For example, the prediction unit can optimize the prediction algorithm based on external data related to the research topic. For example, the prediction unit can improve prediction accuracy by referring to external data corresponding to the user's research topic. As a result, prediction accuracy is improved by referring to external data related to the user's research topic. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input external data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0109] The prediction unit can apply a prediction algorithm specific to the user's research field during prediction. For example, the prediction unit can improve prediction accuracy by applying a prediction algorithm specific to the user's research field. For example, the prediction unit can improve the accuracy of prediction results by using an algorithm specific to the research field. For example, the prediction unit can apply a prediction algorithm appropriate to the user's research field and provide prediction results. This improves prediction accuracy by applying a prediction algorithm specific to the user's research field. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input a prediction algorithm specific to the research field into a generating AI and have the generating AI perform the task of providing prediction results.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The neuroscience research support system can also be equipped with a data visualization unit. This unit visually displays data obtained from the analysis and detection units, allowing researchers to intuitively understand data trends and anomalies. For example, it can generate a 3D model of brain scan data and display the activity of specific brain regions using color coding. It can also graph the temporal fluctuations of electrophysiological data and highlight abnormal patterns. Furthermore, the data visualization unit can display recommendations for related studies in graphs and charts, enabling researchers to easily compare and contrast them. This allows researchers to deepen their understanding of the data and conduct research more efficiently through data visualization.

[0112] The neuroscience research support system can also be equipped with a data filtering unit. This unit filters data obtained from the analysis and detection units based on specific criteria, allowing researchers to extract only the information they need. For example, it can extract activity data from specific brain regions and exclude other data. It can also extract data within a specific time range to detect abnormal patterns. Furthermore, the data filtering unit can filter recommendations for related research, allowing researchers to obtain information focused on topics of interest. This enables researchers to efficiently acquire necessary information through data filtering, thereby improving the accuracy of their research.

[0113] The neuroscience research support system can also include a data integration unit. This unit integrates data obtained from the analysis and detection units to provide comprehensive analysis results. For example, it can integrate brain scan data and electrophysiological data to analyze the relationship between the activity of specific brain regions and their electrical activity. It can also integrate data obtained under different experimental conditions to compare differences between conditions. Furthermore, the data integration unit can integrate recommendations for related studies, enabling researchers to obtain comprehensive information. This allows researchers to gain deeper insights through data integration and improve the quality of their research.

[0114] The neuroscience research support system can also be equipped with a data sharing unit. This unit facilitates collaborative research by sharing data obtained from the analysis and detection units with other researchers. For example, it can share activity data from specific brain regions, allowing other researchers to perform different analyses using the same data. It can also share electrophysiological data, enabling comparison of analysis results from different perspectives. Furthermore, the data sharing unit can share recommendations for related research, promoting information exchange among researchers. This allows researchers to collaborate on research and conduct analyses from a more multifaceted perspective through data sharing.

[0115] The neuroscience research support system can also be equipped with a data backup unit. This unit periodically backs up data obtained from the analysis and detection units, ensuring data security. For example, it can regularly back up brain scan data and electrophysiological data to prevent data loss or corruption. It can also back up recommendations for related studies, allowing users to refer to past recommendations. Furthermore, the data backup unit can back up experimental designs and predictive data, recording the progress of the research. Through data backup, researchers can ensure data security and proceed with their research with confidence.

[0116] The neuroscience research support system allows the analysis unit to estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the system can prioritize the analysis of important data and provide results quickly. If the user is relaxed, it can perform a detailed analysis and provide comprehensive results. If the user is in a hurry, it can perform a simplified analysis and provide a quick overview. This allows for more appropriate analysis by adjusting the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The neuroscience research support system can estimate the user's emotions and adjust the anomaly detection threshold based on the estimated emotions. For example, if the user is stressed, the anomaly detection threshold can be set low to detect anomalies early. If the user is relaxed, the anomaly detection threshold can be set high to allow for more detailed analysis. If the user is in a hurry, the anomaly detection threshold can be adjusted to provide results quickly. By adjusting the anomaly detection threshold according to the user's emotions, more appropriate anomaly detection becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The neuroscience research support system can estimate the user's emotions and adjust the priority of recommended research based on that estimation. For example, if the user is stressed, important research will be prioritized. If the user is relaxed, detailed research can be prioritized. If the user is in a hurry, concise research can be prioritized. By adjusting the priority of recommended research according to the user's emotions, more appropriate research can be recommended. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The neuroscience research support system's optimization unit can estimate the user's emotions and adjust the experimental design optimization method based on the estimated emotions. For example, if the user is stressed, it can suggest a simple experimental design. If the user is relaxed, it can suggest a detailed experimental design. If the user is in a hurry, it can suggest a quickly executable experimental design. This allows for more appropriate experimental designs by adjusting the optimization method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The neuroscience research support system can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the prediction results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The analysis unit analyzes brain scan data or electrophysiological data. For example, the analysis unit can analyze the activity of a specific region of the brain using brain scan data. It can also analyze brain activity patterns using electrophysiological data. Step 2: The detection unit detects anomalies based on the data analyzed by the analysis unit. For example, the detection unit can detect abnormal activity in a specific area of ​​the brain. It can detect activity exceeding a specific threshold or abnormal patterns. Step 3: The recommendation team recommends relevant research from existing research databases. For example, the recommendation team can recommend relevant research based on keywords or themes entered by the researcher. If the researcher is working on a specific brain region, the recommendation team can recommend the latest research papers related to that region. Step 4: The optimization unit adjusts the experimental design. For example, the optimization unit can optimize the sample size and conditions of the experiment. By optimizing the sample size of the experiment, more accurate results can be obtained. Step 5: The prediction unit predicts the experimental results. The prediction unit can predict experimental results based on, for example, past experimental data. It can predict experimental results based on past experimental data and adjust the experimental plan accordingly.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the processing of analyzing brain scan data and electrophysiological data is performed by the processor 28 of the data processing device 12. The detection unit is implemented by the control unit 46A of the smart device 14 and detects anomalies based on the analyzed data. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends relevant research from an existing research database. The optimization unit is implemented by the control unit 46A of the smart device 14 and adjusts the experimental design. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts experimental results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the processing of analyzing brain scan data and electrophysiological data is performed by the processor 28 of the data processing device 12. The detection unit is implemented by the control unit 46A of the smart glasses 214 and detects anomalies based on the analyzed data. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends relevant research from an existing research database. The optimization unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the experimental design. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts experimental results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the processing of analyzing brain scan data and electrophysiological data is performed by the processor 28 of the data processing device 12. The detection unit is implemented by the control unit 46A of the headset terminal 314 and detects anomalies based on the analyzed data. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends relevant research from an existing research database. The optimization unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the experimental design. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts experimental results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, the processing of analyzing brain scan data and electrophysiological data is performed by the processor 28 of the data processing device 12. The detection unit is implemented by the control unit 46A of the robot 414 and detects anomalies based on the analyzed data. The recommendation unit is implemented by the specific processing unit 290 of the data processing device 12 and recommends relevant research from an existing research database. The optimization unit is implemented by the control unit 46A of the robot 414 and adjusts the experimental design. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts experimental results. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) An analysis unit that analyzes brain scan data or electrophysiological data, A detection unit that detects anomalies based on the data analyzed by the analysis unit, A recommendation department that recommends relevant research from existing research databases, An optimization unit that adjusts the experimental design, It comprises a prediction unit that predicts experimental results, A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyzing the activity of specific areas of the brain The system described in Appendix 1, characterized by the features described herein. (Note 3) The detection unit is Detects abnormal activity in specific areas of the brain. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recommendation department, The system recommends relevant research based on keywords or themes entered by researchers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, Adjust the sample size or experimental settings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The prediction unit, Predicting experimental results based on past experimental data The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During brain scan data acquisition, specific electroencephalogram (EEG) patterns are analyzed in real time, providing immediate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing electrophysiological data, detailed analysis is performed for each different frequency band to extract specific activity patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the analysis results are customized by taking into account the user's lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The detection unit is It estimates the user's emotions and adjusts the anomaly detection threshold based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is When an anomaly is detected, different anomaly patterns are detected in specific areas of the brain. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is When an anomaly is detected, the detection algorithm is optimized by referring to past anomaly data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is It estimates the user's emotions and adjusts the order in which anomaly detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is When an anomaly is detected, the type of anomaly is identified by considering the user's genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is When an anomaly is detected, the system refers to the user's environment data to identify the cause of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recommendation department, It estimates user sentiment and adjusts the priority of recommended research based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, When making recommendations, the system prioritizes recommending highly relevant research by referencing the user's past research history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, information is collected from different databases based on the user's research topic. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, We estimate the user's sentiment and adjust how recommended research is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making recommendations, priority will be given to referencing the research database of the user's affiliated institution. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, we also refer to patent databases related to the user's research field to provide information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, We estimate user emotions and adjust the method for optimizing the experimental design based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, When optimizing the experimental design, the optimal sample size is determined by referring to past experimental data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, When optimizing experimental design, simulations are performed for each different condition setting to select the optimal condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The optimization unit, We estimate user emotions and prioritize experimental designs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, When optimizing experimental designs, apply optimization algorithms specific to the user's research field. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, When optimizing experimental designs, we propose the optimal design considering the user's research resources. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, During prediction, the prediction algorithm is optimized by referring to past experimental data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, During prediction, the prediction results are compared for each different experimental condition, and the optimal condition is selected. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, It estimates the user's emotions and prioritizes the prediction results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, When making predictions, external data related to the user's research topic is referenced to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 36) The prediction unit, When making predictions, apply a prediction algorithm that is specific to the user's research field. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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. An analysis unit that analyzes brain scan data or electrophysiological data, A detection unit that detects anomalies based on the data analyzed by the analysis unit, A recommendation department that recommends relevant research from existing research databases, An optimization unit that adjusts the experimental design, It comprises a prediction unit that predicts experimental results, A system characterized by the following features.

2. The aforementioned analysis unit, Analyzing the activity of specific areas of the brain The system according to feature 1.

3. The detection unit is Detects abnormal activity in specific areas of the brain. The system according to feature 1.

4. The aforementioned recommendation department, The system recommends relevant research based on keywords or themes entered by researchers. The system according to feature 1.

5. The optimization unit, Adjust the sample size or experimental settings. The system according to feature 1.

6. The prediction unit, Predicting experimental results based on past experimental data The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During brain scan data acquisition, specific electroencephalogram (EEG) patterns are analyzed in real time, providing immediate feedback. The system according to feature 1.

9. The aforementioned analysis unit, When analyzing electrophysiological data, detailed analysis is performed for each different frequency band to extract specific activity patterns. The system according to feature 1.

10. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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