System

Abstract

An object of a system according to an embodiment is to analyze a simulation result and present obtained knowledge.SOLUTION: A system includes a collection unit, a simulation unit, an analysis unit, and a presentation unit. The collection unit collects data from reality. The simulation unit performs a simulation based on the data collected by the collection unit. The analysis unit analyzes the simulation result obtained by the simulation unit. The presentation unit presents knowledge based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies require advanced expertise to analyze simulation results and present the findings, making them difficult for general users to use.

[0005] The system according to the embodiment aims to analyze the simulation results and present the findings obtained. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a simulation unit, an analysis unit, and a presentation unit. The collection unit collects data from reality. The simulation unit performs a simulation based on the data collected by the collection unit. The analysis unit analyzes the simulation results obtained by the simulation unit. The presentation unit presents findings based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the simulation results and present the findings obtained. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses digital twin technology to conduct simulations based on information and data acquired from the real world and then feeds the results back into the real world. This system provides an AI service that analyzes and considers phenomena arising from the visualization results of the simulation and presents the findings. This allows users without specialized knowledge to understand the simulation results and use them to improve the real world. For example, a factory manager can improve the efficiency of a production line based on the simulation results. In urban planning, the system can also analyze the causes of traffic congestion and propose optimal traffic management measures. In this way, by combining digital twin technology and AI, simulation results can be effectively utilized to solve real-world problems.

[0029] A system according to an embodiment includes a collection unit, a simulation unit, an analysis unit, and a presentation unit. The collection unit collects data from reality. For example, the collection unit can collect sensor data, user data, environmental data, etc. The collection unit can also optimize the data collection method using AI. The simulation unit performs a simulation based on the data collected by the collection unit. For example, the simulation unit can perform a simulation depending on the algorithm used and the purpose of the simulation. The simulation unit can also improve the accuracy of the simulation using AI. The analysis unit analyzes the simulation results obtained by the simulation unit. For example, the analysis unit can analyze the simulation results using a data analysis method and consider phenomena. The analysis unit can also improve the accuracy of the analysis using AI. The presentation unit presents knowledge based on the analysis results obtained by the analysis unit. For example, the presentation unit can present the knowledge in a report format, a graph display, a dashboard, or other methods. The presentation unit can also optimize the method of presenting the knowledge using AI. As a result, the system according to an embodiment performs a simulation based on data obtained from reality, analyzes the results, and presents the knowledge, allowing users without specialized knowledge to understand the simulation results and use them to improve reality.

[0030] The collection unit can analyze past data collection history and select the optimal collection method. For example, the collection unit selects the most efficient collection method from the past data collection history. The collection unit can also analyze the past data collection history and identify areas for improvement in the collection method. The collection unit can also customize the collection method based on the past data collection history. In this way, by analyzing the past data collection history, the optimal collection method can be selected and the efficiency of data collection can be improved.

[0031] The collection unit can perform filtering based on actual environmental conditions when collecting data. For example, when it is raining, the collection unit avoids collecting outdoor data and prioritizes collecting indoor data. The collection unit can also collect data at night taking lighting conditions into consideration. The collection unit can also prioritize collecting data from a temperature sensor when the temperature is high. In this way, by filtering data collection based on actual environmental conditions, the accuracy of collected data can be improved.

[0032] When collecting data, the collection unit can select the optimum collection means depending on the type of data to be collected. For example, the collection unit uses a temperature sensor when collecting temperature data. The collection unit can also use an acceleration sensor when collecting vibration data. The collection unit can also use a humidity sensor when collecting humidity data. This allows the accuracy of data collection to be improved by selecting the optimum collection means depending on the type of data to be collected.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data from a specific region. The collection unit can also select highly relevant data based on the geographical location information. The collection unit can also set the range of data collection by taking geographical location information into consideration. In this way, by preferentially collecting highly relevant data by taking geographical location information into consideration, the relevance of the data can be increased.

[0034] The collection unit can analyze social media activities and collect related data when collecting data. For example, the collection unit analyzes the content posted by the user on social media and collects related data. The collection unit can also collect related data based on the user's check-in information on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activities.

[0035] The collection unit can customize the collection method by reflecting past feedback when collecting data. For example, the collection unit improves the data collection method based on past feedback. The collection unit can also optimize the data collection procedure by reflecting past feedback. The collection unit can also improve the accuracy of data collection by referring to past feedback. In this way, by reflecting past feedback, the data collection method can be optimized and the collection accuracy can be improved.

[0036] The simulation unit can adjust the level of detail of the simulation based on the importance of the data during the simulation. For example, the simulation unit performs a detailed simulation based on data with high importance. The simulation unit can also perform a simplified simulation based on data with low importance. The simulation unit can also dynamically adjust the level of detail of the simulation according to the importance of the data. This allows for an efficient simulation by adjusting the level of detail of the simulation based on the importance of the data.

[0037] During simulation, the simulation unit can apply different simulation algorithms depending on the data category. For example, the simulation unit applies a heat conduction simulation algorithm to temperature data. The simulation unit can also apply a vibration analysis simulation algorithm to vibration data. The simulation unit can also apply a humidity fluctuation simulation algorithm to humidity data. In this way, by applying different simulation algorithms depending on the data category, the accuracy of the simulation can be improved.

[0038] The simulation unit can improve the accuracy of the simulation by referring to past simulation results during the simulation. For example, the simulation unit improves the simulation algorithm based on the past simulation results. The simulation unit can also adjust the simulation parameters by referring to the past simulation results. The simulation unit can also analyze the past simulation results and improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to the past simulation results.

[0039] During a simulation, the simulation unit can determine the priority of the simulation based on the time when data was collected. For example, the simulation unit preferentially uses the most recent data for the simulation. The simulation unit can also determine the priority of the simulation by referring to past data. The simulation unit can also adjust the order of the simulation based on the time when data was collected. In this way, by determining the priority of the simulation based on the time when data was collected, the most recent data can be used preferentially.

[0040] The simulation unit can adjust the order of simulations based on the relevance of data during simulation. For example, the simulation unit uses highly relevant data preferentially in the simulation. The simulation unit can also adjust the order of simulations based on the relevance of data. The simulation unit can also perform simulations by putting less relevant data on hold. In this way, adjusting the order of simulations based on the relevance of data enables efficient simulations.

[0041] During the simulation, the simulation unit can adjust the use of technical terms in the simulation according to the user's level of expertise. For example, the simulation unit can display simulation results using simple terms for a user with little expertise. The simulation unit can also display simulation results using detailed technical terms for a user with a wealth of expertise. The simulation unit can also adjust the display method of the simulation results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the simulation according to the user's level of expertise, it is possible to provide simulation results that are suitable for the user.

[0042] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the simulation results during the analysis. For example, the analysis unit analyzes the interrelationships between the simulation results and improves the accuracy of the analysis. The analysis unit can also adjust the analysis method by taking into account the interrelationships between the simulation results. The analysis unit can also set analysis parameters based on the interrelationships between the simulation results. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between the simulation results.

[0043] The analysis unit can perform the analysis while taking into consideration the attribute information of the data submitter. For example, the analysis unit can adjust the analysis method by taking into consideration the level of expertise of the data submitter. The analysis unit can also set the analytical perspective by taking into consideration the industry background of the data submitter. The analysis unit can also improve the accuracy of the analysis by referring to the data submitter's past submission history. In this way, by taking into consideration the attribute information of the data submitter, the analytical perspective can be adjusted and accuracy can be improved.

[0044] During analysis, the analysis unit can weight the analysis based on the frequency of data submission. For example, the analysis unit prioritizes analysis of data that is submitted more frequently. The analysis unit can also adjust the weighting of the analysis based on the frequency of submission. The analysis unit can also postpone analysis of data that is submitted less frequently. In this way, by weighting the analysis based on the frequency of data submission, important data can be prioritized in analysis.

[0045] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. For example, the analysis unit displays the analysis results for each region based on the geographical distribution of the data. The analysis unit can also adjust the analysis method by taking into account the geographical distribution. The analysis unit can also set analysis parameters based on the geographical distribution. This makes it possible to provide analysis results for each region by taking into account the geographical distribution of the data.

[0046] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit improves the analysis method based on the related literature. The analysis unit can also adjust the analysis parameters by referring to the related literature. The analysis unit can also set the viewpoint of the analysis based on the related literature. In this way, the accuracy of the analysis can be improved by referring to the related literature.

[0047] The analysis unit can perform the analysis while taking into consideration the market value of the data. For example, the analysis unit prioritizes the analysis of data with high market value. The analysis unit can also adjust the weighting of the analysis based on the market value. The analysis unit can also postpone the analysis of data with low market value. In this way, by taking into consideration the market value of the data, important data can be analyzed with priority.

[0048] When presenting knowledge, the presentation unit can optimize the current presentation method by referring to past presentation results. For example, the presentation unit improves the presentation method based on past presentation results. The presentation unit can also adjust presentation parameters by referring to past presentation results. The presentation unit can also set a presentation perspective based on past presentation results. In this way, by referring to past presentation results, the presentation method can be optimized and the accuracy of knowledge can be improved.

[0049] When presenting findings, the presentation unit can apply different presentation methods to each data category. For example, the presentation unit presents temperature data in a graph format. The presentation unit can also present vibration data in a waveform display. The presentation unit can also present humidity data in a heat map format. By applying different presentation methods to each data category, it is possible to provide findings that are visually easy to understand.

[0050] When presenting knowledge, the presentation unit can present the knowledge taking into consideration the attribute information of the data submitter. The presentation unit can adjust the way the knowledge is displayed, for example, taking into consideration the level of expertise of the data submitter. The presentation unit can also set the perspective of the knowledge taking into consideration the industry background of the data submitter. The presentation unit can also improve the accuracy of the knowledge by referring to the data submitter's past submission history. In this way, by taking into consideration the attribute information of the data submitter, knowledge that is appropriate for the submitter can be provided.

[0051] When presenting knowledge, the presentation unit can analyze changes in knowledge based on the time of data submission. The presentation unit displays changes in knowledge in chronological order, for example, based on the time of data submission. The presentation unit can also analyze changes in knowledge based on the time of submission. The presentation unit can also predict changes in knowledge based on the time of submission. This makes it possible to grasp changes in knowledge over time by analyzing changes in knowledge based on the time of data submission.

[0052] When presenting knowledge, the presentation unit can present the knowledge by referring to related market data. The presentation unit presents the knowledge, for example, based on related market data. The presentation unit can also improve the accuracy of the knowledge by referring to market data. The presentation unit can also set the viewpoint of the knowledge based on market data. In this way, the accuracy of the knowledge can be improved by referring to related market data.

[0053] The presentation unit can present knowledge taking into consideration the technical maturity when presenting the knowledge. For example, the presentation unit preferentially presents knowledge with a high technical maturity. The presentation unit can also adjust the weighting of the knowledge based on the technical maturity. The presentation unit can also present knowledge with a low technical maturity at a later date. In this way, important knowledge can be presented preferentially by taking the technical maturity into consideration.

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

[0055] The system may further include a prediction unit. The prediction unit can predict future phenomena based on the data collected by the collection unit and the simulation results obtained by the simulation unit. For example, the prediction unit can predict future fluctuations in production efficiency based on data from a factory's production line. The prediction unit can also predict the occurrence of traffic congestion in urban planning and propose countermeasures in advance. Furthermore, the prediction unit can predict the impact of climate change based on environmental data and propose appropriate countermeasures. In this way, the system can predict future phenomena and take countermeasures in advance, thereby helping to solve real-world problems.

[0056] The collection unit can analyze past data collection history and select the optimal collection method. For example, the most efficient collection method is selected from the past data collection history. The collection unit can also analyze past data collection history and identify areas for improvement in the collection method. The collection unit can also customize the collection method based on the past data collection history. In this way, by analyzing past data collection history, the optimal collection method can be selected and the efficiency of data collection can be improved.

[0057] The collection unit can filter data based on actual environmental conditions when collecting data. For example, when it is raining, outdoor data collection is avoided and indoor data collection is prioritized. The collection unit can also collect data at night taking lighting conditions into consideration. The collection unit can also prioritize collecting data from a temperature sensor when the temperature is high. In this way, by filtering data collection based on actual environmental conditions, the accuracy of collected data can be improved.

[0058] When collecting data, the collection unit can select the optimal collection means depending on the type of data to be collected. For example, a temperature sensor is used when collecting temperature data. The collection unit can also use an acceleration sensor when collecting vibration data. The collection unit can also use a humidity sensor when collecting humidity data. This allows the accuracy of data collection to be improved by selecting the optimal collection means depending on the type of data to be collected.

[0059] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, data from a specific region can be collected preferentially. The collection unit can also select highly relevant data based on the geographical location information. The collection unit can also set the range of data collection by taking geographical location information into consideration. In this way, by preferentially collecting highly relevant data by taking geographical location information into consideration, the relevance of the data can be increased.

[0060] The collection unit can analyze social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of a user's posts on social media and collect related data. The collection unit can also collect related data based on the user's check-in information on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activities.

[0061] The collection unit can customize the collection method by reflecting past feedback when collecting data. For example, the data collection method can be improved based on past feedback. The collection unit can also optimize the data collection procedure by reflecting past feedback. The collection unit can also improve the accuracy of data collection by referring to past feedback. In this way, by reflecting past feedback, the data collection method can be optimized and the collection accuracy can be improved.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection unit collects data from the real world. For example, the collection unit can collect sensor data, user data, environmental data, etc. The collection unit can also use AI to optimize the data collection method. Step 2: The simulation unit performs a simulation based on the data collected by the collection unit. For example, the simulation unit can perform a simulation depending on the algorithm used and the purpose of the simulation. The simulation unit can also use AI to improve the accuracy of the simulation. Step 3: The analysis unit analyzes the simulation results obtained by the simulation unit. For example, the analysis unit can analyze the simulation results using data analysis techniques and consider phenomena. The analysis unit can also use AI to improve the accuracy of the analysis. Step 4: The presentation unit presents findings based on the analysis results obtained by the analysis unit. For example, the presentation unit can present the findings in a report format, graph display, dashboard, or other manner. The presentation unit can also optimize the presentation method of findings using AI.

[0064] (Example 2) A system according to an embodiment of the present invention uses digital twin technology to conduct simulations based on information and data acquired from the real world and then feeds the results back into the real world. This system provides an AI service that analyzes and considers phenomena arising from the visualization results of the simulation and presents the findings. This allows users without specialized knowledge to understand the simulation results and use them to improve the real world. For example, a factory manager can improve the efficiency of a production line based on the simulation results. In urban planning, the system can also analyze the causes of traffic congestion and propose optimal traffic management measures. In this way, by combining digital twin technology and AI, simulation results can be effectively utilized to solve real-world problems.

[0065] A system according to an embodiment includes a collection unit, a simulation unit, an analysis unit, and a presentation unit. The collection unit collects data from reality. For example, the collection unit can collect sensor data, user data, environmental data, etc. The collection unit can also optimize the data collection method using AI. The simulation unit performs a simulation based on the data collected by the collection unit. For example, the simulation unit can perform a simulation depending on the algorithm used and the purpose of the simulation. The simulation unit can also improve the accuracy of the simulation using AI. The analysis unit analyzes the simulation results obtained by the simulation unit. For example, the analysis unit can analyze the simulation results using a data analysis method and consider phenomena. The analysis unit can also improve the accuracy of the analysis using AI. The presentation unit presents knowledge based on the analysis results obtained by the analysis unit. For example, the presentation unit can present the knowledge in a report format, a graph display, a dashboard, or other methods. The presentation unit can also optimize the method of presenting the knowledge using AI. As a result, the system according to an embodiment performs a simulation based on data obtained from reality, analyzes the results, and presents the knowledge, allowing users without specialized knowledge to understand the simulation results and use them to improve reality.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of data collection to collect data quickly. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The collection unit can analyze past data collection history and select the optimal collection method. For example, the collection unit selects the most efficient collection method from the past data collection history. The collection unit can also analyze the past data collection history and identify areas for improvement in the collection method. The collection unit can also customize the collection method based on the past data collection history. In this way, by analyzing the past data collection history, the optimal collection method can be selected and the efficiency of data collection can be improved.

[0068] The collection unit can perform filtering based on actual environmental conditions when collecting data. For example, when it is raining, the collection unit avoids collecting outdoor data and prioritizes collecting indoor data. The collection unit can also collect data at night taking lighting conditions into consideration. The collection unit can also prioritize collecting data from a temperature sensor when the temperature is high. In this way, by filtering data collection based on actual environmental conditions, the accuracy of collected data can be improved.

[0069] When collecting data, the collection unit can select the optimum collection means depending on the type of data to be collected. For example, the collection unit uses a temperature sensor when collecting temperature data. The collection unit can also use an acceleration sensor when collecting vibration data. The collection unit can also use a humidity sensor when collecting humidity data. This allows the accuracy of data collection to be improved by selecting the optimum collection means depending on the type of data to be collected.

[0070] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data of high importance. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed data. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting data that can be collected quickly. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected efficiently. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data from a specific region. The collection unit can also select highly relevant data based on the geographical location information. The collection unit can also set the range of data collection by taking geographical location information into consideration. In this way, by preferentially collecting highly relevant data by taking geographical location information into consideration, the relevance of the data can be increased.

[0072] The collection unit can analyze social media activities and collect related data when collecting data. For example, the collection unit analyzes the content posted by the user on social media and collects related data. The collection unit can also collect related data based on the user's check-in information on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activities.

[0073] The collection unit can customize the collection method by reflecting past feedback when collecting data. For example, the collection unit improves the data collection method based on past feedback. The collection unit can also optimize the data collection procedure by reflecting past feedback. The collection unit can also improve the accuracy of data collection by referring to past feedback. In this way, by reflecting past feedback, the data collection method can be optimized and the collection accuracy can be improved.

[0074] The simulation unit can estimate the user's emotions and adjust the parameters of the simulation based on the estimated user's emotions. For example, the simulation unit can perform a detailed simulation when the user is relaxed. The simulation unit can also perform a simplified simulation when the user is in a hurry. The simulation unit can also perform a visually stimulating simulation when the user is excited. In this way, by adjusting the simulation parameters according to the user's emotions, a simulation suitable for the user can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0075] The simulation unit can adjust the level of detail of the simulation based on the importance of the data during the simulation. For example, the simulation unit performs a detailed simulation based on data with high importance. The simulation unit can also perform a simplified simulation based on data with low importance. The simulation unit can also dynamically adjust the level of detail of the simulation according to the importance of the data. This allows for an efficient simulation by adjusting the level of detail of the simulation based on the importance of the data.

[0076] During simulation, the simulation unit can apply different simulation algorithms depending on the data category. For example, the simulation unit applies a heat conduction simulation algorithm to temperature data. The simulation unit can also apply a vibration analysis simulation algorithm to vibration data. The simulation unit can also apply a humidity fluctuation simulation algorithm to humidity data. In this way, by applying different simulation algorithms depending on the data category, the accuracy of the simulation can be improved.

[0077] The simulation unit can improve the accuracy of the simulation by referring to past simulation results during the simulation. For example, the simulation unit improves the simulation algorithm based on the past simulation results. The simulation unit can also adjust the simulation parameters by referring to the past simulation results. The simulation unit can also analyze the past simulation results and improve the accuracy of the simulation. In this way, the accuracy of the simulation can be improved by referring to the past simulation results.

[0078] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user emotions. For example, if the user is in a hurry, the simulation unit can perform a simulation that is completed in a short time. Furthermore, if the user is relaxed, the simulation unit can perform a detailed simulation. Furthermore, if the user is excited, the simulation unit can perform a visually stimulating simulation. In this way, by adjusting the length of the simulation according to the user's emotions, a simulation suitable for the user can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] During a simulation, the simulation unit can determine the priority of the simulation based on the time when data was collected. For example, the simulation unit preferentially uses the most recent data for the simulation. The simulation unit can also determine the priority of the simulation by referring to past data. The simulation unit can also adjust the order of the simulation based on the time when data was collected. In this way, by determining the priority of the simulation based on the time when data was collected, the most recent data can be used preferentially.

[0080] The simulation unit can adjust the order of simulations based on the relevance of data during simulation. For example, the simulation unit uses highly relevant data preferentially in the simulation. The simulation unit can also adjust the order of simulations based on the relevance of data. The simulation unit can also perform simulations by putting less relevant data on hold. In this way, adjusting the order of simulations based on the relevance of data enables efficient simulations.

[0081] During the simulation, the simulation unit can adjust the use of technical terms in the simulation according to the user's level of expertise. For example, the simulation unit can display simulation results using simple terms for a user with little expertise. The simulation unit can also display simulation results using detailed technical terms for a user with a wealth of expertise. The simulation unit can also adjust the display method of the simulation results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the simulation according to the user's level of expertise, it is possible to provide simulation results that are suitable for the user.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. The analysis unit can also perform a visually stimulating analysis when the user is excited. This allows the analysis criteria to be adjusted according to the user's emotions, thereby providing analysis results that are suitable for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the simulation results during the analysis. For example, the analysis unit analyzes the interrelationships between the simulation results and improves the accuracy of the analysis. The analysis unit can also adjust the analysis method by taking into account the interrelationships between the simulation results. The analysis unit can also set analysis parameters based on the interrelationships between the simulation results. In this way, the accuracy of the analysis can be improved by taking into account the interrelationships between the simulation results.

[0084] The analysis unit can perform the analysis while taking into consideration the attribute information of the data submitter. For example, the analysis unit can adjust the analysis method by taking into consideration the level of expertise of the data submitter. The analysis unit can also set the analytical perspective by taking into consideration the industry background of the data submitter. The analysis unit can also improve the accuracy of the analysis by referring to the data submitter's past submission history. In this way, by taking into consideration the attribute information of the data submitter, the analytical perspective can be adjusted and accuracy can be improved.

[0085] During analysis, the analysis unit can weight the analysis based on the frequency of data submission. For example, the analysis unit prioritizes analysis of data that is submitted more frequently. The analysis unit can also adjust the weighting of the analysis based on the frequency of submission. The analysis unit can also postpone analysis of data that is submitted less frequently. In this way, by weighting the analysis based on the frequency of data submission, important data can be prioritized in analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can display important analysis results first. Furthermore, if the user is relaxed, the analysis unit can sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that highlight the main points. In this way, by adjusting the display order of the analysis results according to the user's emotions, it is possible to provide analysis results that are suitable for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The analysis unit can perform the analysis while taking into account the geographical distribution of the data. For example, the analysis unit displays the analysis results for each region based on the geographical distribution of the data. The analysis unit can also adjust the analysis method by taking into account the geographical distribution. The analysis unit can also set analysis parameters based on the geographical distribution. This makes it possible to provide analysis results for each region by taking into account the geographical distribution of the data.

[0088] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit improves the analysis method based on the related literature. The analysis unit can also adjust the analysis parameters by referring to the related literature. The analysis unit can also set the viewpoint of the analysis based on the related literature. In this way, the accuracy of the analysis can be improved by referring to the related literature.

[0089] The analysis unit can perform the analysis while taking into consideration the market value of the data. For example, the analysis unit prioritizes the analysis of data with high market value. The analysis unit can also adjust the weighting of the analysis based on the market value. The analysis unit can also postpone the analysis of data with low market value. In this way, by taking into consideration the market value of the data, important data can be analyzed with priority.

[0090] The presentation unit can estimate the user's emotions and adjust the way in which knowledge is displayed based on the estimated user emotions. For example, when the user is nervous, the presentation unit provides a simple, highly visible display method. When the user is relaxed, the presentation unit can also provide a display method that includes detailed information. When the user is in a hurry, the presentation unit can also provide a display method that focuses on the main points. This makes it possible to provide knowledge that is appropriate for the user by adjusting the way knowledge is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When presenting knowledge, the presentation unit can optimize the current presentation method by referring to past presentation results. For example, the presentation unit improves the presentation method based on past presentation results. The presentation unit can also adjust presentation parameters by referring to past presentation results. The presentation unit can also set a presentation perspective based on past presentation results. In this way, by referring to past presentation results, the presentation method can be optimized and the accuracy of knowledge can be improved.

[0092] When presenting findings, the presentation unit can apply different presentation methods to each data category. For example, the presentation unit presents temperature data in a graph format. The presentation unit can also present vibration data in a waveform display. The presentation unit can also present humidity data in a heat map format. By applying different presentation methods to each data category, it is possible to provide findings that are visually easy to understand.

[0093] When presenting knowledge, the presentation unit can present the knowledge taking into consideration the attribute information of the data submitter. The presentation unit can adjust the way the knowledge is displayed, for example, taking into consideration the level of expertise of the data submitter. The presentation unit can also set the perspective of the knowledge taking into consideration the industry background of the data submitter. The presentation unit can also improve the accuracy of the knowledge by referring to the data submitter's past submission history. In this way, by taking into consideration the attribute information of the data submitter, knowledge that is appropriate for the submitter can be provided.

[0094] The presentation unit can estimate the user's emotions and adjust the importance of knowledge based on the estimated user emotions. For example, if the user is nervous, the presentation unit can first present important knowledge. Furthermore, if the user is relaxed, the presentation unit can sequentially present detailed knowledge. Furthermore, if the user is in a hurry, the presentation unit can prioritize presenting knowledge that covers the main points. In this way, by adjusting the importance of knowledge according to the user's emotions, it is possible to provide knowledge that is appropriate for the user. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] When presenting knowledge, the presentation unit can analyze changes in knowledge based on the time of data submission. The presentation unit displays changes in knowledge in chronological order, for example, based on the time of data submission. The presentation unit can also analyze changes in knowledge based on the time of submission. The presentation unit can also predict changes in knowledge based on the time of submission. This makes it possible to grasp changes in knowledge over time by analyzing changes in knowledge based on the time of data submission.

[0096] When presenting knowledge, the presentation unit can present the knowledge by referring to related market data. The presentation unit presents the knowledge, for example, based on related market data. The presentation unit can also improve the accuracy of the knowledge by referring to market data. The presentation unit can also set the viewpoint of the knowledge based on market data. In this way, the accuracy of the knowledge can be improved by referring to related market data.

[0097] The presentation unit can present knowledge taking into consideration the technical maturity when presenting the knowledge. For example, the presentation unit preferentially presents knowledge with a high technical maturity. The presentation unit can also adjust the weighting of the knowledge based on the technical maturity. The presentation unit can also present knowledge with a low technical maturity at a later date. In this way, important knowledge can be presented preferentially by taking the technical maturity into consideration. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, simulation unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data from reality using the camera 42 or microphone 38B of the smart device 14. For example, the simulation unit can be realized by the specific processing unit 290 of the data processing device 12 and performs a simulation based on the collected data. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyzes the simulation results. For example, the presentation unit can present the analysis results using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, simulation unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data from reality using the camera 42 and microphone 238 of the smart glasses 214. For example, the simulation unit is realized by the specific processing unit 290 of the data processing device 12 and performs a simulation based on the collected data. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the simulation results. For example, the presentation unit can present the analysis results using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === and data processing unit 12. Each of the multiple elements including the collection unit, simulation unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect data from reality using the camera 42 or microphone 238 of the headset type terminal 314. For example, the simulation unit is realized by the specific processing unit 290 of the data processing device 12 and performs a simulation based on the collected data. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the simulation results. For example, the presentation unit can present the analysis results using the display 343 or speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, simulation unit, analysis unit, and presentation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data from reality using the camera 42 or microphone 238 of the robot 414. For example, the simulation unit is realized by the specific processing unit 290 of the data processing device 12 and performs a simulation based on the collected data. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the simulation results. For example, the presentation unit can present the analysis results using the speaker 240 or display device of the robot 414.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The system may further include a prediction unit. The prediction unit can predict future phenomena based on the data collected by the collection unit and the simulation results obtained by the simulation unit. For example, the prediction unit can predict future fluctuations in production efficiency based on data from a factory's production line. The prediction unit can also predict the occurrence of traffic congestion in urban planning and propose countermeasures in advance. Furthermore, the prediction unit can predict the impact of climate change based on environmental data and propose appropriate countermeasures. In this way, the system can predict future phenomena and take countermeasures in advance, thereby helping to solve real-world problems.

[0100] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can shorten the timing of data collection to collect data quickly. This reduces the user's burden and enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The collection unit can analyze past data collection history and select the optimal collection method. For example, the most efficient collection method is selected from the past data collection history. The collection unit can also analyze past data collection history and identify areas for improvement in the collection method. The collection unit can also customize the collection method based on the past data collection history. In this way, by analyzing past data collection history, the optimal collection method can be selected and the efficiency of data collection can be improved.

[0102] The collection unit can filter data based on actual environmental conditions when collecting data. For example, when it is raining, outdoor data collection is avoided and indoor data collection is prioritized. The collection unit can also collect data at night taking lighting conditions into consideration. The collection unit can also prioritize collecting data from a temperature sensor when the temperature is high. In this way, by filtering data collection based on actual environmental conditions, the accuracy of collected data can be improved.

[0103] When collecting data, the collection unit can select the optimal collection means depending on the type of data to be collected. For example, a temperature sensor is used when collecting temperature data. The collection unit can also use an acceleration sensor when collecting vibration data. The collection unit can also use a humidity sensor when collecting humidity data. This allows the accuracy of data collection to be improved by selecting the optimal collection means depending on the type of data to be collected.

[0104] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, it prioritizes collecting data of high importance. The collection unit can also prioritize collecting detailed data if the user is relaxed. The collection unit can also prioritize collecting data that can be collected quickly if the user is in a hurry. This allows important data to be collected efficiently by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. For example, data from a specific region can be collected preferentially. The collection unit can also select highly relevant data based on the geographical location information. The collection unit can also set the range of data collection by taking geographical location information into consideration. In this way, by preferentially collecting highly relevant data by taking geographical location information into consideration, the relevance of the data can be increased.

[0106] The collection unit can analyze social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of a user's posts on social media and collect related data. The collection unit can also collect related data based on the user's check-in information on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing social media activities.

[0107] The collection unit can customize the collection method by reflecting past feedback when collecting data. For example, the data collection method can be improved based on past feedback. The collection unit can also optimize the data collection procedure by reflecting past feedback. The collection unit can also improve the accuracy of data collection by referring to past feedback. In this way, by reflecting past feedback, the data collection method can be optimized and the collection accuracy can be improved.

[0108] The simulation unit can estimate the user's emotions and adjust the parameters of the simulation based on the estimated user's emotions. For example, if the user is relaxed, a detailed simulation is performed. If the user is in a hurry, the simulation unit can also perform a simplified simulation. If the user is excited, the simulation unit can also perform a visually stimulating simulation. In this way, by adjusting the simulation parameters according to the user's emotions, a simulation suitable for the user can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The collection unit collects data from the real world. For example, the collection unit can collect sensor data, user data, environmental data, etc. The collection unit can also use AI to optimize the data collection method. Step 2: The simulation unit performs a simulation based on the data collected by the collection unit. For example, the simulation unit can perform a simulation depending on the algorithm used and the purpose of the simulation. The simulation unit can also use AI to improve the accuracy of the simulation. Step 3: The analysis unit analyzes the simulation results obtained by the simulation unit. For example, the analysis unit can analyze the simulation results using data analysis techniques and consider phenomena. The analysis unit can also use AI to improve the accuracy of the analysis. Step 4: The presentation unit presents findings based on the analysis results obtained by the analysis unit. For example, the presentation unit can present the findings in a report format, graph display, dashboard, or other manner. The presentation unit can also optimize the presentation method of findings using AI.

[0111] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] 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.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0176] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0182] [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data from reality; a simulation unit that performs a simulation based on the data collected by the collection unit; an analysis unit that analyzes the simulation results obtained by the simulation unit; a presentation unit that presents findings based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

3. The collecting unit Analyze past data collection history and select the optimal collection method 2. The system of claim 1.

4. The collecting unit Filtering data based on real-world environmental conditions as it is collected 2. The system of claim 1.

5. The collecting unit When collecting data, choose the most appropriate collection method depending on the type of data to be collected.

2. The system of claim 1.

6. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit When collecting data, prioritize the collection of relevant data by taking geographic location into account.

2. The system of claim 1.

8. The collecting unit During data collection, analyze social media activity and collect relevant data 2. The system of claim 1.

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