system

Abstract

The system according to this embodiment aims to support better decision-making by digitizing intuitive information from animals and humans. [Solution] The system according to this embodiment comprises a data collection unit, an analysis unit, and a provision unit. The data collection unit converts intuitive information from animals and humans into data. The analysis unit analyzes the intuitive information collected by the data collection unit. The provision unit provides the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the direct emotional information of animals and humans has not been sufficiently digitized and utilized, and there is room for improvement.

[0005] The system according to the embodiment aims to digitize the direct emotional information of animals and humans and support excellent decision-making.

Means for Solving the Problems

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a provision unit. The data collection unit digitizes the direct emotional information of animals and humans. The analysis unit analyzes the direct emotional information collected by the data collection unit. The provision unit provides the result analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can digitize intuitive information from animals and humans, and support better decision-making. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] <00001The 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 generative AI agent system according to an embodiment of the present invention is a system that utilizes the "instincts and intuition" of animals and humans. This generative AI agent system supports superior decision-making by digitizing intuitive information from animals and humans and having the generative AI analyze that data. For example, by collecting data on abnormal behavior when animals predict earthquakes or changes in internal hormones when expert traders make investment decisions, and having the generative AI analyze this data, it is possible to detect earthquake precursors or predict the probability of investment success. Specifically, the generative AI agent system comprises a data collection unit that digitizes intuitive information from animals and humans, an analysis unit that analyzes the collected intuitive information, and a provision unit that provides the analyzed results. For example, the data collection unit is equipped with cameras and sensors to detect abnormal behavior in animals and sensors to detect changes in internal hormones in expert traders. The analysis unit analyzes the abnormal behavior data of animals based on the collected data and detects earthquake precursors. It also analyzes the data on changes in internal hormones in expert traders and predicts the probability of investment success. The provision unit notifies the user of the analysis results. This allows the generative AI agent system to support superior decision-making that leverages the "instincts and intuition" of animals and humans. For example, by analyzing earthquake prediction data from animals, it can predict the occurrence of earthquakes and mitigate damage. Furthermore, by analyzing the intuition data of expert traders, it can increase the probability of successful investments. Thus, the generative AI agent system can support superior decision-making that leverages the "instincts and intuition" of animals and humans.

[0029] The generation AI agent system according to this embodiment comprises a data collection unit, an analysis unit, and a provision unit. The data collection unit converts intuitive information from animals and humans into data. The data collection unit includes, for example, cameras and sensors for detecting abnormal animal behavior. For example, if an animal predicts an earthquake and exhibits abnormal behavior, the data collection unit records that behavior with cameras and sensors and converts it into data. The data collection unit also includes sensors for detecting changes in the internal hormones of expert traders. For example, the data collection unit detects changes in the internal hormones of expert traders when they make investment decisions and converts them into data. The analysis unit analyzes the intuitive information collected by the data collection unit. For example, the analysis unit analyzes the abnormal animal behavior data to detect earthquake precursors. For example, the analysis unit predicts the occurrence of earthquakes based on the abnormal animal behavior data. The analysis unit also analyzes the internal hormone change data of expert traders and predicts the probability of investment success. For example, the analysis unit predicts the probability of investment success based on the internal hormone change data of expert traders. The provision unit provides the results analyzed by the analysis unit. The providing unit, for example, notifies the user of the analysis results. The providing unit notifies the user of the analysis results and supports appropriate decision-making. As a result, the generating AI agent system according to the embodiment can support superior decision-making by digitizing, analyzing, and providing intuitive information from animals and humans.

[0030] The data collection unit digitizes intuitive information from animals and humans. Specifically, it is equipped with cameras and sensors to detect abnormal animal behavior. For example, if an animal predicts an earthquake and exhibits abnormal behavior, that behavior is recorded by the camera and sensors and digitized. The camera captures the animal's movements in detail with high resolution, and the sensors detect changes in the animal's movements and environment. This allows even subtle changes in the animal's behavior to be collected as data without being missed. The data collection unit is also equipped with sensors to detect changes in the internal hormones of expert traders. For example, the sensors detect and digitize changes in the internal hormones of expert traders when they make investment decisions. The sensors include skin-worn and non-contact types, allowing for real-time monitoring of hormone changes. This allows for accurate capture and collection of physiological changes associated with the trader's intuitive judgment. Furthermore, the data collection unit has a communication function to centrally manage this data and transmit it to the analysis unit. The data is stored on a cloud server and made accessible to the analysis unit. This allows the data collection unit to efficiently and accurately collect intuitive information from animals and humans, improving the overall performance of the system.

[0031] The analysis unit analyzes intuitive information collected by the data collection unit. Specifically, it analyzes abnormal animal behavior data to detect earthquake precursors. The analysis uses AI to process data in real time and analyze animal behavior patterns. For example, based on abnormal animal behavior data, it compares it with past data to identify patterns of abnormal behavior in order to predict earthquake occurrences. The AI ​​uses image recognition technology and machine learning algorithms to analyze animal behavior data and detect earthquake precursors with high accuracy. Furthermore, the analysis unit analyzes hormone change data from expert traders to predict the probability of investment success. Specifically, it evaluates the accuracy of traders' judgments based on hormone change data and predicts the probability of investment success. The AI ​​performs time-series analysis and pattern recognition of hormone data to predict the likelihood of success of traders' intuitive judgments with high accuracy. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past earthquake and investment data, it can predict risk fluctuations under specific conditions and formulate future countermeasures. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The service provider provides the results analyzed by the analysis provider. Specifically, it notifies users of the analysis results to support appropriate decision-making. For example, if analysis of abnormal animal behavior data detects signs of an impending earthquake, the service provider will issue a warning to the user and provide evacuation instructions. Users can check the analysis results in real time via their smartphones or computers. Also, if analysis of a master trader's hormone change data predicts a high probability of investment success, the service provider will notify the trader of the timing for investment. This allows traders to make more reliable investment decisions based on data that supports their intuitive judgments. Furthermore, the service provider is equipped with an interface to display analysis results in a visually easy-to-understand manner. For example, it uses graphs and charts to visually display analysis results so that users can intuitively understand them. In addition, the service provider can collect user feedback and continuously improve the accuracy and method of providing analysis results. This allows the service provider to provide users with quick and accurate information and support better decision-making.

[0033] The data collection unit is equipped with cameras and sensors to detect abnormal animal behavior. For example, the data collection unit can capture images of abnormal animal behavior with a camera and analyze the footage. For example, the data collection unit can detect abnormal animal behavior with sensors and collect the data. For example, the data collection unit can monitor abnormal animal behavior in real time and collect data when abnormal behavior occurs. This makes it possible to understand earthquake precursors by detecting abnormal animal behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data captured by a camera into a generating AI and have the generating AI perform abnormal behavior detection.

[0034] The data collection unit is equipped with sensors to detect changes in the expert trader's internal hormones. For example, the data collection unit detects changes in the expert trader's internal hormones using sensors and collects the data. For example, the data collection unit monitors changes in the expert trader's internal hormones in real time and collects data when changes occur. For example, the data collection unit periodically measures changes in the expert trader's internal hormones and collects the data. This makes it possible to predict the probability of investment success by detecting changes in the expert trader's internal hormones. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the hormone data detected by the sensors into a generating AI and have the generating AI perform an analysis of the hormone changes.

[0035] The analysis unit analyzes abnormal animal behavior data to detect earthquake precursors. The analysis unit predicts the occurrence of earthquakes based on abnormal animal behavior data. The analysis unit identifies patterns of abnormal behavior by analyzing abnormal animal behavior data. The analysis unit identifies the causes of abnormal behavior by analyzing abnormal animal behavior data. In this way, earthquake precursors can be detected by analyzing abnormal animal behavior data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data into a generative AI and have the generative AI perform earthquake precursor detection.

[0036] The analysis unit analyzes data on changes in the internal hormones of expert traders and predicts the probability of investment success. The analysis unit predicts the probability of investment success based on data on changes in the internal hormones of expert traders. The analysis unit analyzes data on changes in the internal hormones of expert traders and identifies patterns of hormone changes. The analysis unit analyzes data on changes in the internal hormones of expert traders and identifies the relationship between hormone changes and the probability of investment success. In this way, the probability of investment success can be predicted by analyzing data on changes in the internal hormones of expert traders. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on changes in the internal hormones of expert traders into a generative AI and have the generative AI perform a prediction of the probability of investment success.

[0037] The service provider notifies the user of the analysis results. The service provider, for example, notifies the user of the analysis results to support appropriate decision-making. The service provider, for example, notifies the user of the analysis results in real time. The service provider, for example, notifies the user of the analysis results periodically. This enables the user to make appropriate decisions by notifying them of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate the notification content.

[0038] The data collection unit learns patterns of abnormal behavior when detecting abnormal behavior in animals, thereby enabling more accurate data collection. For example, the data collection unit learns patterns of abnormal behavior in animals and starts collecting data when a specific behavior occurs. For example, the data collection unit adjusts the timing of data collection considering the frequency and duration of abnormal behavior. For example, the data collection unit uses different sensors to collect data depending on the type of abnormal behavior. This allows for more accurate data collection by learning patterns of abnormal behavior in animals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input abnormal behavior data from animals into a generating AI and have the generating AI learn patterns of abnormal behavior.

[0039] The data collection unit uses different sensors for each type of hormone to detect changes in the expert trader's internal hormones and collects detailed data. For example, the data collection unit uses a sensor to detect changes in cortisol to measure stress levels. For example, the data collection unit uses a sensor to detect changes in adrenaline to measure excitement levels. For example, the data collection unit uses a sensor to detect changes in serotonin to measure relaxation levels. By using different sensors for each type of hormone, detailed data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the change data for each hormone into a generating AI and have the generating AI perform a detailed analysis of the data.

[0040] The data collection unit optimizes sensor placement during data collection, taking into account the animal's habitat and the trader's working environment. For example, the data collection unit adjusts the placement of cameras and sensors according to the animal's habitat. For example, the data collection unit optimizes sensor placement according to the trader's working environment. For example, the data collection unit dynamically changes sensor placement in response to environmental changes. This improves the accuracy of data collection by optimizing sensor placement while considering the animal's habitat and the trader's working environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data into a generating AI and have the generating AI determine the optimal sensor placement.

[0041] The data collection unit enhances data relevance by referencing the behavioral history of animals and the past trading history of traders during data collection. For example, the data collection unit may refer to the past behavioral history of animals to identify patterns of abnormal behavior. For example, the data collection unit may refer to the past trading history of traders to analyze the relationship between hormonal changes and trading results. For example, the data collection unit may improve the accuracy of current data collection based on past data. This enhances data relevance by referencing the behavioral history of animals and the past trading history of traders. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input past behavioral history data into a generating AI and have the generating AI perform the task of improving data relevance.

[0042] The analysis unit improves the accuracy of its analysis by considering the frequency and duration of abnormal behavior when analyzing abnormal behavior data from animals. For example, the analysis unit analyzes earthquake precursors based on the frequency of abnormal behavior. For example, the analysis unit evaluates the importance of abnormal behavior based on its duration. For example, the analysis unit identifies the cause of abnormal behavior based on its type. This allows for improved analysis accuracy when analyzing abnormal behavior data from animals by considering the frequency and duration of occurrence. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal behavior data from animals into a generative AI and have the generative AI perform an analysis of the frequency and duration of occurrence.

[0043] The analysis unit learns patterns of hormone changes when analyzing data on changes in the body's hormones of expert traders, thereby more accurately predicting the probability of investment success. For example, the analysis unit learns patterns of cortisol changes and correlates stress levels with the probability of investment success. For example, the analysis unit learns patterns of adrenaline changes and correlates states of excitement with the probability of investment success. For example, the analysis unit learns patterns of serotonin changes and correlates states of relaxation with the probability of investment success. In this way, by learning patterns of hormone changes, the probability of investment success can be predicted more accurately. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input hormone change data into a generative AI and have the generative AI perform the learning of change patterns.

[0044] The analysis unit improves the accuracy of earthquake predictions by combining abnormal animal behavior data and weather data during analysis. For example, the analysis unit combines abnormal behavior data and weather data to analyze the probability of earthquake occurrence. For example, the analysis unit combines abnormal behavior data and weather data to identify the cause of abnormal behavior. For example, the analysis unit combines abnormal behavior data and weather data to analyze patterns of abnormal behavior. In this way, the accuracy of earthquake predictions can be improved by combining abnormal animal behavior data and weather data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data and weather data into a generative AI and have the generative AI perform the task of improving the accuracy of earthquake predictions.

[0045] The analysis unit combines data on changes in the body hormones of expert traders with market trend data during analysis to increase the probability of investment success. For example, the analysis unit combines hormone change data and market trend data to analyze the probability of investment success. For example, the analysis unit combines hormone change data and market trend data to evaluate investment risk. For example, the analysis unit combines hormone change data and market trend data to analyze investment timing. In this way, the probability of investment success can be increased by combining data on changes in body hormones and market trend data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input hormone change data and market trend data into a generative AI and have the generative AI perform the task of improving the probability of investment success.

[0046] The service provider selects the optimal notification method by referring to the user's past behavior history when providing analysis results. For example, the service provider selects the optimal notification method by referring to the user's past behavior history. For example, the service provider adjusts the timing of notifications based on the user's past behavior history. For example, the service provider analyzes the user's past behavior history and provides the optimal notification content. This makes it possible to select the optimal notification method by referring to the user's past behavior history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input past behavior history data into a generating AI and have the generating AI perform the selection of the optimal notification method.

[0047] The service provider provides customized information according to the user's areas of interest when providing analysis results. For example, the service provider provides customized notification content according to the user's areas of interest. For example, the service provider selects the optimal notification method based on the user's areas of interest. For example, the service provider analyzes the user's areas of interest and provides highly relevant information. In this way, highly relevant information can be provided by providing customized information according to the user's areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the provision of customized information.

[0048] When providing analysis results, the service provider selects the optimal display method by considering the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. In this way, the service provider can select the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information into a generating AI and have the generating AI select the optimal display method.

[0049] The service provider, when providing analysis results, considers the user's geographical location information to provide highly relevant information. For example, the service provider provides highly relevant information based on the user's current location. For example, the service provider refers to the user's past location information to provide the most appropriate notification content. For example, the service provider selects the optimal notification timing based on the user's location information. This allows the service provider to provide highly relevant information by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can learn patterns of abnormal behavior in animals when detecting abnormal behavior, enabling more accurate data collection. For example, it can learn patterns of abnormal behavior in animals and start data collection when a specific behavior occurs. It can also adjust the timing of data collection considering the frequency and duration of the abnormal behavior. Furthermore, it can collect data using different sensors depending on the type of abnormal behavior. This allows for more accurate data collection by learning patterns of abnormal behavior in animals.

[0052] The analysis unit can improve the accuracy of animal abnormal behavior data by considering the frequency and duration of the abnormal behavior. For example, it can analyze earthquake precursors based on the frequency of abnormal behavior. It can also evaluate the importance of abnormal behavior based on its duration. Furthermore, it can identify the cause of abnormal behavior based on its type. In this way, by considering the frequency and duration of abnormal behavior data, the accuracy of the analysis can be improved when analyzing animal abnormal behavior data.

[0053] The analysis unit learns patterns of hormone changes when analyzing data on the internal hormones of expert traders, enabling it to more accurately predict the probability of investment success. For example, it can learn cortisol change patterns and correlate stress levels with the probability of investment success. It can also learn adrenaline change patterns and correlate states of excitement with the probability of investment success. Furthermore, it can learn serotonin change patterns and correlate states of relaxation with the probability of investment success. In this way, by learning hormone change patterns, it can more accurately predict the probability of investment success.

[0054] The service provider can select the optimal notification method by referring to the user's past behavior history when providing analysis results. For example, it can select the optimal notification method by referring to the user's past behavior history. It can also adjust the timing of notifications based on the user's past behavior history. Furthermore, it can analyze the user's past behavior history and provide the optimal notification content. In this way, the optimal notification method can be selected by referring to the user's past behavior history.

[0055] The service provider can provide customized information based on the user's areas of interest when delivering analysis results. For example, it can provide customized notification content based on the user's areas of interest. It can also select the optimal notification method based on the user's areas of interest. Furthermore, it can analyze the user's areas of interest and provide highly relevant information. In this way, by providing customized information based on the user's areas of interest, it can provide highly relevant information.

[0056] The service provider can select the optimal display method when providing analysis results, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be selected by considering the user's device information.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit digitizes intuitive information from animals and humans. For example, it is equipped with cameras and sensors to detect abnormal animal behavior, and if an animal predicts an earthquake and exhibits abnormal behavior, that behavior is recorded and digitized. It is also equipped with sensors to detect changes in the internal hormones of expert traders, and changes in their internal hormones when making investment decisions are detected and digitized. Step 2: The analysis unit analyzes the intuitive information collected by the data collection unit. For example, it analyzes data on abnormal animal behavior to detect earthquake precursors. It also analyzes data on changes in the internal hormones of expert traders to predict the probability of investment success. Step 3: The provisioning unit provides the results analyzed by the analysis unit. For example, it notifies the user of the analysis results to support appropriate decision-making.

[0059] (Example of form 2) The generative AI agent system according to an embodiment of the present invention is a system that utilizes the "instincts and intuition" of animals and humans. This generative AI agent system supports superior decision-making by digitizing intuitive information from animals and humans and having the generative AI analyze that data. For example, by collecting data on abnormal behavior when animals predict earthquakes or changes in internal hormones when expert traders make investment decisions, and having the generative AI analyze this data, it is possible to detect earthquake precursors or predict the probability of investment success. Specifically, the generative AI agent system comprises a data collection unit that digitizes intuitive information from animals and humans, an analysis unit that analyzes the collected intuitive information, and a provision unit that provides the analyzed results. For example, the data collection unit is equipped with cameras and sensors to detect abnormal behavior in animals and sensors to detect changes in internal hormones in expert traders. The analysis unit analyzes the abnormal behavior data of animals based on the collected data and detects earthquake precursors. It also analyzes the data on changes in internal hormones in expert traders and predicts the probability of investment success. The provision unit notifies the user of the analysis results. This allows the generative AI agent system to support superior decision-making that leverages the "instincts and intuition" of animals and humans. For example, by analyzing earthquake prediction data from animals, it can predict the occurrence of earthquakes and mitigate damage. Furthermore, by analyzing the intuition data of expert traders, it can increase the probability of successful investments. Thus, the generative AI agent system can support superior decision-making that leverages the "instincts and intuition" of animals and humans.

[0060] The generation AI agent system according to this embodiment comprises a data collection unit, an analysis unit, and a provision unit. The data collection unit converts intuitive information from animals and humans into data. The data collection unit includes, for example, cameras and sensors for detecting abnormal animal behavior. For example, if an animal predicts an earthquake and exhibits abnormal behavior, the data collection unit records that behavior with cameras and sensors and converts it into data. The data collection unit also includes sensors for detecting changes in the internal hormones of expert traders. For example, the data collection unit detects changes in the internal hormones of expert traders when they make investment decisions and converts them into data. The analysis unit analyzes the intuitive information collected by the data collection unit. For example, the analysis unit analyzes the abnormal animal behavior data to detect earthquake precursors. For example, the analysis unit predicts the occurrence of earthquakes based on the abnormal animal behavior data. The analysis unit also analyzes the internal hormone change data of expert traders and predicts the probability of investment success. For example, the analysis unit predicts the probability of investment success based on the internal hormone change data of expert traders. The provision unit provides the results analyzed by the analysis unit. The providing unit, for example, notifies the user of the analysis results. The providing unit notifies the user of the analysis results and supports appropriate decision-making. As a result, the generating AI agent system according to the embodiment can support superior decision-making by digitizing, analyzing, and providing intuitive information from animals and humans.

[0061] The data collection unit digitizes intuitive information from animals and humans. Specifically, it is equipped with cameras and sensors to detect abnormal animal behavior. For example, if an animal predicts an earthquake and exhibits abnormal behavior, that behavior is recorded by the camera and sensors and digitized. The camera captures the animal's movements in detail with high resolution, and the sensors detect changes in the animal's movements and environment. This allows even subtle changes in the animal's behavior to be collected as data without being missed. The data collection unit is also equipped with sensors to detect changes in the internal hormones of expert traders. For example, the sensors detect and digitize changes in the internal hormones of expert traders when they make investment decisions. The sensors include skin-worn and non-contact types, allowing for real-time monitoring of hormone changes. This allows for accurate capture and collection of physiological changes associated with the trader's intuitive judgment. Furthermore, the data collection unit has a communication function to centrally manage this data and transmit it to the analysis unit. The data is stored on a cloud server and made accessible to the analysis unit. This allows the data collection unit to efficiently and accurately collect intuitive information from animals and humans, improving the overall performance of the system.

[0062] The analysis unit analyzes intuitive information collected by the data collection unit. Specifically, it analyzes abnormal animal behavior data to detect earthquake precursors. The analysis uses AI to process data in real time and analyze animal behavior patterns. For example, based on abnormal animal behavior data, it compares it with past data to identify patterns of abnormal behavior in order to predict earthquake occurrences. The AI ​​uses image recognition technology and machine learning algorithms to analyze animal behavior data and detect earthquake precursors with high accuracy. Furthermore, the analysis unit analyzes hormone change data from expert traders to predict the probability of investment success. Specifically, it evaluates the accuracy of traders' judgments based on hormone change data and predicts the probability of investment success. The AI ​​performs time-series analysis and pattern recognition of hormone data to predict the likelihood of success of traders' intuitive judgments with high accuracy. In addition, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analysis. For example, based on past earthquake and investment data, it can predict risk fluctuations under specific conditions and formulate future countermeasures. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0063] The service provider provides the results analyzed by the analysis provider. Specifically, it notifies users of the analysis results to support appropriate decision-making. For example, if analysis of abnormal animal behavior data detects signs of an impending earthquake, the service provider will issue a warning to the user and provide evacuation instructions. Users can check the analysis results in real time via their smartphones or computers. Also, if analysis of a master trader's hormone change data predicts a high probability of investment success, the service provider will notify the trader of the timing for investment. This allows traders to make more reliable investment decisions based on data that supports their intuitive judgments. Furthermore, the service provider is equipped with an interface to display analysis results in a visually easy-to-understand manner. For example, it uses graphs and charts to visually display analysis results so that users can intuitively understand them. In addition, the service provider can collect user feedback and continuously improve the accuracy and method of providing analysis results. This allows the service provider to provide users with quick and accurate information and support better decision-making.

[0064] The data collection unit is equipped with cameras and sensors to detect abnormal animal behavior. For example, the data collection unit can capture images of abnormal animal behavior with a camera and analyze the footage. For example, the data collection unit can detect abnormal animal behavior with sensors and collect the data. For example, the data collection unit can monitor abnormal animal behavior in real time and collect data when abnormal behavior occurs. This makes it possible to understand earthquake precursors by detecting abnormal animal behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data captured by a camera into a generating AI and have the generating AI perform abnormal behavior detection.

[0065] The data collection unit is equipped with sensors to detect changes in the expert trader's internal hormones. For example, the data collection unit detects changes in the expert trader's internal hormones using sensors and collects the data. For example, the data collection unit monitors changes in the expert trader's internal hormones in real time and collects data when changes occur. For example, the data collection unit periodically measures changes in the expert trader's internal hormones and collects the data. This makes it possible to predict the probability of investment success by detecting changes in the expert trader's internal hormones. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the hormone data detected by the sensors into a generating AI and have the generating AI perform an analysis of the hormone changes.

[0066] The analysis unit analyzes abnormal animal behavior data to detect earthquake precursors. The analysis unit predicts the occurrence of earthquakes based on abnormal animal behavior data. The analysis unit identifies patterns of abnormal behavior by analyzing abnormal animal behavior data. The analysis unit identifies the causes of abnormal behavior by analyzing abnormal animal behavior data. In this way, earthquake precursors can be detected by analyzing abnormal animal behavior data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data into a generative AI and have the generative AI perform earthquake precursor detection.

[0067] The analysis unit analyzes data on changes in the internal hormones of expert traders and predicts the probability of investment success. The analysis unit predicts the probability of investment success based on data on changes in the internal hormones of expert traders. The analysis unit analyzes data on changes in the internal hormones of expert traders and identifies patterns of hormone changes. The analysis unit analyzes data on changes in the internal hormones of expert traders and identifies the relationship between hormone changes and the probability of investment success. In this way, the probability of investment success can be predicted by analyzing data on changes in the internal hormones of expert traders. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on changes in the internal hormones of expert traders into a generative AI and have the generative AI perform a prediction of the probability of investment success.

[0068] The service provider notifies the user of the analysis results. The service provider, for example, notifies the user of the analysis results to support appropriate decision-making. The service provider, for example, notifies the user of the analysis results in real time. The service provider, for example, notifies the user of the analysis results periodically. This enables the user to make appropriate decisions by notifying them of the analysis results. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate the notification content.

[0069] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit reduces the frequency of data collection to reduce stress. For example, if the user is tense, the data collection unit increases the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit shortens the timing of data collection to quickly acquire data. In this way, by adjusting the timing of data collection based on the user's emotions, stress can be reduced and detailed data can be collected. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit learns patterns of abnormal behavior when detecting abnormal behavior in animals, thereby enabling more accurate data collection. For example, the data collection unit learns patterns of abnormal behavior in animals and starts collecting data when a specific behavior occurs. For example, the data collection unit adjusts the timing of data collection considering the frequency and duration of abnormal behavior. For example, the data collection unit uses different sensors to collect data depending on the type of abnormal behavior. This allows for more accurate data collection by learning patterns of abnormal behavior in animals. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input abnormal behavior data from animals into a generating AI and have the generating AI learn patterns of abnormal behavior.

[0071] The data collection unit uses different sensors for each type of hormone to detect changes in the expert trader's internal hormones and collects detailed data. For example, the data collection unit uses a sensor to detect changes in cortisol to measure stress levels. For example, the data collection unit uses a sensor to detect changes in adrenaline to measure excitement levels. For example, the data collection unit uses a sensor to detect changes in serotonin to measure relaxation levels. By using different sensors for each type of hormone, detailed data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the change data for each hormone into a generating AI and have the generating AI perform a detailed analysis of the data.

[0072] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit prioritizes collecting less important data. For example, if the user is stressed, the data collection unit prioritizes collecting more important data. For example, if the user is in a hurry, the data collection unit prioritizes collecting data that can be collected quickly. In this way, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit optimizes sensor placement during data collection, taking into account the animal's habitat and the trader's working environment. For example, the data collection unit adjusts the placement of cameras and sensors according to the animal's habitat. For example, the data collection unit optimizes sensor placement according to the trader's working environment. For example, the data collection unit dynamically changes sensor placement in response to environmental changes. This improves the accuracy of data collection by optimizing sensor placement while considering the animal's habitat and the trader's working environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data into a generating AI and have the generating AI determine the optimal sensor placement.

[0074] The data collection unit enhances data relevance by referencing the behavioral history of animals and the past trading history of traders during data collection. For example, the data collection unit may refer to the past behavioral history of animals to identify patterns of abnormal behavior. For example, the data collection unit may refer to the past trading history of traders to analyze the relationship between hormonal changes and trading results. For example, the data collection unit may improve the accuracy of current data collection based on past data. This enhances data relevance by referencing the behavioral history of animals and the past trading history of traders. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input past behavioral history data into a generating AI and have the generating AI perform the task of improving data relevance.

[0075] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit displays detailed analysis results. If the user is tense, for example, the analysis unit displays concise analysis results. If the user is in a hurry, for example, the analysis unit displays concise analysis results. In this way, by adjusting the display method of the analysis results based on the user's emotions, the system can provide the user with the most appropriate information. 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.

[0076] The analysis unit improves the accuracy of its analysis by considering the frequency and duration of abnormal behavior when analyzing abnormal behavior data from animals. For example, the analysis unit analyzes earthquake precursors based on the frequency of abnormal behavior. For example, the analysis unit evaluates the importance of abnormal behavior based on its duration. For example, the analysis unit identifies the cause of abnormal behavior based on its type. This allows for improved analysis accuracy when analyzing abnormal behavior data from animals by considering the frequency and duration of occurrence. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal behavior data from animals into a generative AI and have the generative AI perform an analysis of the frequency and duration of occurrence.

[0077] The analysis unit learns patterns of hormone changes when analyzing data on changes in the body's hormones of expert traders, thereby more accurately predicting the probability of investment success. For example, the analysis unit learns patterns of cortisol changes and correlates stress levels with the probability of investment success. For example, the analysis unit learns patterns of adrenaline changes and correlates states of excitement with the probability of investment success. For example, the analysis unit learns patterns of serotonin changes and correlates states of relaxation with the probability of investment success. In this way, by learning patterns of hormone changes, the probability of investment success can be predicted more accurately. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input hormone change data into a generative AI and have the generative AI perform the learning of change patterns.

[0078] The analysis unit estimates the user's emotions and determines the priority of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit prioritizes displaying detailed analysis results. For example, if the user is tense, the analysis unit prioritizes displaying concise analysis results. For example, if the user is in a hurry, the analysis unit prioritizes displaying concise analysis results. In this way, by prioritizing the analysis results based on the user's emotions, important information can be provided preferentially. 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 without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The analysis unit improves the accuracy of earthquake predictions by combining abnormal animal behavior data and weather data during analysis. For example, the analysis unit combines abnormal behavior data and weather data to analyze the probability of earthquake occurrence. For example, the analysis unit combines abnormal behavior data and weather data to identify the cause of abnormal behavior. For example, the analysis unit combines abnormal behavior data and weather data to analyze patterns of abnormal behavior. In this way, the accuracy of earthquake predictions can be improved by combining abnormal animal behavior data and weather data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input abnormal animal behavior data and weather data into a generative AI and have the generative AI perform the task of improving the accuracy of earthquake predictions.

[0080] The analysis unit combines data on changes in the body hormones of expert traders with market trend data during analysis to increase the probability of investment success. For example, the analysis unit combines hormone change data and market trend data to analyze the probability of investment success. For example, the analysis unit combines hormone change data and market trend data to evaluate investment risk. For example, the analysis unit combines hormone change data and market trend data to analyze investment timing. In this way, the probability of investment success can be increased by combining data on changes in body hormones and market trend data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input hormone change data and market trend data into a generative AI and have the generative AI perform the task of improving the probability of investment success.

[0081] The service provider estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, if the user is relaxed, the service provider reduces the frequency of notifications to alleviate stress. For example, if the user is stressed, the service provider increases the frequency of notifications and provides more detailed information. For example, if the user is in a hurry, the service provider shortens the notification interval to provide information quickly. This allows for stress reduction and quick information delivery by adjusting the timing of notifications based on 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The service provider selects the optimal notification method by referring to the user's past behavior history when providing analysis results. For example, the service provider selects the optimal notification method by referring to the user's past behavior history. For example, the service provider adjusts the timing of notifications based on the user's past behavior history. For example, the service provider analyzes the user's past behavior history and provides the optimal notification content. This makes it possible to select the optimal notification method by referring to the user's past behavior history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input past behavior history data into a generating AI and have the generating AI perform the selection of the optimal notification method.

[0083] The service provider provides customized information according to the user's areas of interest when providing analysis results. For example, the service provider provides customized notification content according to the user's areas of interest. For example, the service provider selects the optimal notification method based on the user's areas of interest. For example, the service provider analyzes the user's areas of interest and provides highly relevant information. In this way, highly relevant information can be provided by providing customized information according to the user's areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user area of ​​interest data into a generating AI and have the generating AI perform the provision of customized information.

[0084] The service provider estimates the user's emotions and adjusts the notification content based on the estimated emotions. For example, if the user is relaxed, the service provider provides detailed notification content. For example, if the user is stressed, the service provider provides concise notification content. For example, if the user is in a hurry, the service provider provides a to-the-point notification content. In this way, by adjusting the notification content based on the user's emotions, the service provider can provide the user with the most appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] When providing analysis results, the service provider selects the optimal display method by considering the user's device information. For example, if the user is using a smartphone, the service provider provides a display method that matches the screen size. For example, if the user is using a tablet, the service provider provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the service provider provides a concise and highly visible display method. In this way, the service provider can select the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input device information into a generating AI and have the generating AI select the optimal display method.

[0086] The service provider, when providing analysis results, considers the user's geographical location information to provide highly relevant information. For example, the service provider provides highly relevant information based on the user's current location. For example, the service provider refers to the user's past location information to provide the most appropriate notification content. For example, the service provider selects the optimal notification timing based on the user's location information. This allows the service provider to provide highly relevant information by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant information.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is relaxed, the frequency of data collection can be reduced to alleviate stress. Conversely, if the user is stressed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, the timing of data collection can be shortened to obtain data quickly. In this way, by adjusting the timing of data collection based on the user's emotions, stress can be reduced and more detailed data can be collected.

[0089] The data collection unit can learn patterns of abnormal behavior in animals when detecting abnormal behavior, enabling more accurate data collection. For example, it can learn patterns of abnormal behavior in animals and start data collection when a specific behavior occurs. It can also adjust the timing of data collection considering the frequency and duration of the abnormal behavior. Furthermore, it can collect data using different sensors depending on the type of abnormal behavior. This allows for more accurate data collection by learning patterns of abnormal behavior in animals.

[0090] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is relaxed, detailed analysis results can be displayed. If the user is stressed, concise analysis results can be displayed. Furthermore, if the user is in a hurry, results that get straight to the point can be displayed. In this way, by adjusting how the analysis results are displayed based on the user's emotions, the system can provide the user with the most relevant information.

[0091] The analysis unit can improve the accuracy of animal abnormal behavior data by considering the frequency and duration of the abnormal behavior. For example, it can analyze earthquake precursors based on the frequency of abnormal behavior. It can also evaluate the importance of abnormal behavior based on its duration. Furthermore, it can identify the cause of abnormal behavior based on its type. In this way, by considering the frequency and duration of abnormal behavior data, the accuracy of the analysis can be improved when analyzing animal abnormal behavior data.

[0092] The analysis unit learns patterns of hormone changes when analyzing data on the internal hormones of expert traders, enabling it to more accurately predict the probability of investment success. For example, it can learn cortisol change patterns and correlate stress levels with the probability of investment success. It can also learn adrenaline change patterns and correlate states of excitement with the probability of investment success. Furthermore, it can learn serotonin change patterns and correlate states of relaxation with the probability of investment success. In this way, by learning hormone change patterns, it can more accurately predict the probability of investment success.

[0093] The system can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is relaxed, the frequency of notifications can be reduced to alleviate stress. Conversely, if the user is stressed, the frequency of notifications can be increased to provide more detailed information. Furthermore, if the user is in a hurry, the notification timing can be shortened to provide information quickly. In this way, by adjusting the timing of notifications based on the user's emotions, stress can be reduced and information can be provided quickly.

[0094] The service provider can select the optimal notification method by referring to the user's past behavior history when providing analysis results. For example, it can select the optimal notification method by referring to the user's past behavior history. It can also adjust the timing of notifications based on the user's past behavior history. Furthermore, it can analyze the user's past behavior history and provide the optimal notification content. In this way, the optimal notification method can be selected by referring to the user's past behavior history.

[0095] The service provider can provide customized information based on the user's areas of interest when delivering analysis results. For example, it can provide customized notification content based on the user's areas of interest. It can also select the optimal notification method based on the user's areas of interest. Furthermore, it can analyze the user's areas of interest and provide highly relevant information. In this way, by providing customized information based on the user's areas of interest, it can provide highly relevant information.

[0096] The notification system can estimate the user's emotions and adjust the notification content based on those emotions. For example, if the user is relaxed, it can provide detailed notifications. If the user is stressed, it can provide concise notifications. Furthermore, if the user is in a hurry, it can provide notifications that get straight to the point. By adjusting the notification content based on the user's emotions, the system can provide the user with the most relevant information.

[0097] The service provider can select the optimal display method when providing analysis results, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the optimal display method can be selected by considering the user's device information.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit digitizes intuitive information from animals and humans. For example, it is equipped with cameras and sensors to detect abnormal animal behavior, and if an animal predicts an earthquake and exhibits abnormal behavior, that behavior is recorded and digitized. It is also equipped with sensors to detect changes in the internal hormones of expert traders, and changes in their internal hormones when making investment decisions are detected and digitized. Step 2: The analysis unit analyzes the intuitive information collected by the data collection unit. For example, it analyzes data on abnormal animal behavior to detect earthquake precursors. It also analyzes data on changes in the internal hormones of expert traders to predict the probability of investment success. Step 3: The provisioning unit provides the results analyzed by the analysis unit. For example, it notifies the user of the analysis results to support appropriate decision-making.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart device 14 to detect abnormal animal behavior or changes in the internal hormones of expert traders, and this data is converted into data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to predict earthquake precursors or the probability of investment success. The provision unit is implemented in the control unit 46A of the smart device 14, and notifies the user of the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the data acquisition unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data acquisition unit uses the camera 42 and sensors of the smart glasses 214 to detect abnormal animal behavior or changes in the internal hormones of expert traders, and this data is converted into data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to predict earthquake precursors or the probability of investment success. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and notifies the user of the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the data acquisition unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data acquisition unit uses the camera 42 and sensors of the headset terminal 314 to detect abnormal animal behavior or changes in the internal hormones of expert traders, and this data is converted into data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to predict earthquake precursors or the probability of investment success. The provision unit is implemented in the control unit 46A of the headset terminal 314, for example, and notifies the user of the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the data acquisition unit, analysis unit, and data provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data acquisition unit uses the camera 42 and sensors of the robot 414 to detect abnormal animal behavior or changes in the internal hormones of a master trader, and this data is converted into data by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to predict earthquake precursors or the probability of investment success. The data provision unit is implemented in, for example, the control unit 46A of the robot 414, and notifies the user of the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A data collection unit that digitizes intuitive information from animals and humans, An analysis unit analyzes intuitive information collected by the data collection unit, The system comprises a providing unit that provides the results analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is Equipped with cameras and sensors to detect abnormal animal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data acquisition unit is Equipped with sensors to detect changes in the body hormones of expert traders. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyzing abnormal animal behavior data to detect earthquake precursors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyzing the hormonal changes data of expert traders to predict their investment success rate. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Notify the user of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data acquisition unit is When detecting abnormal behavior in animals, the system learns patterns of abnormal behavior to collect more accurate data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is When detecting changes in the hormones within expert traders, different sensors are used for each type of hormone to collect detailed data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is When collecting data, the optimal sensor placement is determined by considering the animal's habitat and the trader's working environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data acquisition unit is When collecting data, we refer to the behavioral history of animals and the past trading history of traders to improve data relevance. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The aforementioned analysis unit, When analyzing abnormal animal behavior data, we improve the accuracy of the analysis by considering the frequency and duration of the abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, By analyzing the hormonal change data of expert traders, we learn the patterns of hormonal changes and more accurately predict the probability of investment success. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, combining data on abnormal animal behavior with weather data improves the accuracy of earthquake prediction. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, data on changes in the body hormones of expert traders is combined with market trend data to increase the probability of investment success. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing analysis results, the system will refer to the user's past behavior history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing analysis results, we will provide customized information according to the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the notification content based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing analysis results, the optimal display method is selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing analysis results, we take the user's geographical location into consideration to provide highly relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that digitizes intuitive information from animals and humans, An analysis unit analyzes intuitive information collected by the data collection unit, The system comprises a providing unit that provides the results analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned data acquisition unit is Equipped with cameras and sensors to detect abnormal animal behavior. The system according to feature 1.

3. The aforementioned data acquisition unit is Equipped with sensors to detect changes in the body hormones of expert traders. The system according to feature 1.

4. The aforementioned analysis unit, Analyzing abnormal animal behavior data to detect earthquake precursors. The system according to feature 1.

5. The aforementioned analysis unit, Analyzing the hormonal changes data of expert traders to predict their investment success rate. The system according to feature 1.

6. The aforementioned supply unit is, Notify the user of the analysis results. The system according to feature 1.

7. The aforementioned data acquisition unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned data acquisition unit is When detecting abnormal behavior in animals, the system learns patterns of abnormal behavior to collect more accurate data. The system according to feature 1.

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