system

Abstract

The system according to the embodiment aims to monitor the movement of objects in real time and analyze their behavioral patterns to provide specific insights. [Solution] According to an embodiment, the system includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the movement of an object in real time. The analysis unit analyzes the data collected by the monitoring unit to identify the spatiotemporal behavioral patterns of the object. The providing unit provides specific insights based on the analysis results obtained by the analysis unit.

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 do not adequately monitor object movements in real time and analyze their behavioral patterns to gain specific insights, leaving room for improvement.

[0005] The system according to the embodiment aims to monitor the movement of objects in real time and analyze their behavioral patterns to provide specific insights. [Means for solving the problem]

[0006] A system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the movement of an object in real time. The analysis unit analyzes the data collected by the monitoring unit to identify the spatiotemporal behavioral patterns of the object. The providing unit provides specific insights based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] A system according to an embodiment can monitor the movement of objects in real time and analyze their behavioral patterns to provide specific insights. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 flow tracking system according to an embodiment of the present invention monitors object movements in real time, analyzes them using a generative AI, and provides valuable insights. The flow tracking system uses IP cameras to monitor object movements in real time, and the generative AI analyzes the collected data to identify the spatiotemporal behavioral patterns of the objects. For example, the flow tracking system monitors customer movements in a retail store, vehicle movements in traffic management, and passenger movements in an airport in real time. The flow tracking system then uses a generative AI to analyze video data obtained from the IP cameras to identify the spatiotemporal behavioral patterns of the objects. For example, the generative AI analyzes customer routes, vehicle paths, and passenger movement patterns. Based on the analysis results, the flow tracking system then provides valuable insights to improve user experiences across multiple domains, such as retail, traffic management, and airports. For example, the flow tracking system optimizes product placement based on customer movements, adjusts traffic signal timing based on vehicle movements, and optimizes guide displays based on passenger movements. This improves user experience and enables efficient operations in each domain. This allows the flow tracking system to monitor and analyze object movements in real time and provide valuable insights. For example, flow tracking systems can optimize product placement in a retail store based on customer movement, adjust traffic light timing based on vehicle movement in traffic management, and optimize signage in an airport based on passenger movement, improving user experience and enabling efficient operations in each domain.

[0029] A flow tracking system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the movement of objects in real time. For example, the monitoring unit monitors the movement of objects using an IP camera. The monitoring unit can also collect monitoring data using AI. For example, the monitoring unit monitors the movement of customers in a retail store using an IP camera. The monitoring unit can also monitor the movement of vehicles in traffic management. The monitoring unit can also monitor the movement of passengers in an airport. The analysis unit analyzes the data collected by the monitoring unit using a generative AI. For example, the analysis unit analyzes video data obtained from an IP camera to identify spatiotemporal behavior patterns of objects. For example, the analysis unit analyzes customer routes using the generative AI. The analysis unit can also analyze vehicle routes using the generative AI. The analysis unit can also analyze passenger movement patterns using the generative AI. The providing unit provides valuable insights based on the analysis results obtained by the analysis unit. For example, the providing unit optimizes the placement of products in a retail store based on the analysis results. The providing unit can also adjust traffic signal timing in traffic management based on the analysis results. The providing unit can also optimize guidance displays within an airport based on the analysis results. This allows the flow tracking system according to the embodiment to monitor and analyze object movements in real time and provide valuable insights. For example, the flow tracking system may optimize product placement based on customer movements within a retail store, adjust traffic signal timing based on vehicle movements in traffic management, and optimize guidance displays based on passenger movements within an airport. This improves the user experience in each domain and enables efficient operations.

[0030] The monitoring unit can monitor the movement of objects in real time using an IP camera. The monitoring unit, for example, monitors the movement of objects in real time using an IP camera. For example, the monitoring unit monitors the movement of customers in a retail store. The monitoring unit can also monitor the movement of vehicles in traffic management. The monitoring unit can also monitor the movement of passengers in an airport. In this way, by using an IP camera, the movement of objects can be monitored in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by an IP camera into a generation AI and cause the generation AI to perform real-time monitoring.

[0031] The analysis unit can analyze video data obtained from an IP camera and clarify the spatiotemporal behavioral patterns of the object. The analysis unit can, for example, analyze video data obtained from an IP camera and clarify the spatiotemporal behavioral patterns of the object. For example, the analysis unit can analyze customer routes using a generation AI. The analysis unit can also analyze vehicle routes using a generation AI. The analysis unit can also analyze passenger movement patterns using a generation AI. In this way, the behavioral patterns of the object can be revealed by analyzing the video data obtained from the IP camera. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input video data obtained from an IP camera to a generation AI and cause the generation AI to analyze the spatiotemporal behavioral patterns of the object.

[0032] The providing unit can provide valuable insights to improve user experience across multiple domains, such as retail, traffic management, and airports, based on the analysis results. For example, the providing unit can optimize the placement of products in a retail store based on the analysis results. The providing unit can also adjust traffic signal timing in traffic management based on the analysis results. The providing unit can also optimize guide displays in airports based on the analysis results. This improves the user experience by providing valuable insights based on the analysis results. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to provide valuable insights.

[0033] The provision unit can adjust the product placement based on customer movement. The provision unit, for example, adjusts the product placement based on customer movement. For example, the provision unit optimizes the product placement based on customer movement. The provision unit can also change the product placement based on customer movement. The provision unit can also rearrange the product placement based on customer movement. This improves the efficiency of the retail store by optimizing the product placement based on customer movement. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input customer movement data into a generation AI and cause the generation AI to optimize the product placement.

[0034] The providing unit can adjust the timing of signals based on vehicle movement. The providing unit, for example, adjusts the timing of signals based on vehicle movement. For example, the providing unit optimizes the timing of signals based on vehicle movement. The providing unit can also change the timing of signals based on vehicle movement. The providing unit can also reset the timing of signals based on vehicle movement. This improves the efficiency of traffic management by adjusting the timing of signals based on vehicle movement. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input vehicle movement data to the generating AI and cause the generating AI to adjust the timing of signals.

[0035] The providing unit can adjust the guidance display based on the passenger's movement. The providing unit, for example, adjusts the guidance display based on the passenger's movement. For example, the providing unit optimizes the guidance display based on the passenger's movement. The providing unit can also change the guidance display based on the passenger's movement. The providing unit can also reset the guidance display based on the passenger's movement. This improves airport efficiency by optimizing the guidance display based on the passenger's movement. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the passenger's movement to the generating AI and cause the generating AI to optimize the guidance display.

[0036] The monitoring unit can apply different monitoring algorithms depending on the type of object. For example, when monitoring human movement, the monitoring unit can apply a face recognition algorithm to identify individuals. When monitoring vehicle movement, the monitoring unit can also apply a license plate recognition algorithm to identify vehicles. When monitoring animal movement, the monitoring unit can also apply an animal species recognition algorithm to identify species. This improves monitoring accuracy by applying an appropriate monitoring algorithm depending on the type of object. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the type of object to the generation AI and cause the generation AI to apply an appropriate monitoring algorithm.

[0037] The monitoring unit can adjust the monitoring method in response to changes in the environment. For example, when the lighting becomes dim, the monitoring unit continues monitoring using an infrared camera. Furthermore, when it rains, the monitoring unit can also continue monitoring using a waterproof camera. Furthermore, when the lighting becomes brighter, the monitoring unit can switch to a normal camera to continue monitoring. This improves the continuity of monitoring by adjusting the monitoring method in response to changes in the environment. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input environmental change data to the generation AI and have the generation AI adjust the monitoring method.

[0038] The monitoring unit can improve the accuracy of monitoring by referring to past monitoring data. For example, the monitoring unit can predict the movement of an object during a specific time period based on the past monitoring data. The monitoring unit can also apply an algorithm to detect abnormal movement based on the past monitoring data. The monitoring unit can also optimize the placement of surveillance cameras based on the past monitoring data. This improves the accuracy of monitoring by referring to the past monitoring data. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and have the generation AI improve the accuracy of monitoring.

[0039] The monitoring unit can adjust the monitoring frequency based on the speed of the object's movement. For example, if the object is moving at high speed, the monitoring unit increases the monitoring frequency to collect detailed data. Also, if the object is moving at low speed, the monitoring unit can reduce the monitoring frequency to efficiently collect data. Also, if the object is stationary, the monitoring unit can minimize the monitoring frequency to save resources. Thus, by adjusting the monitoring frequency based on the speed of the object's movement, efficient data collection is possible. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object movement speed data to the generation AI and cause the generation AI to adjust the monitoring frequency.

[0040] The monitoring unit can adjust the monitoring resolution according to the size of the object. For example, when monitoring a large object (e.g., a vehicle), the monitoring unit monitors a wide area at low resolution. Furthermore, when monitoring a small object (e.g., a small animal), the monitoring unit can perform detailed monitoring at high resolution. Furthermore, when monitoring a medium-sized object (e.g., a human), the monitoring unit can perform balanced monitoring at medium resolution. This enables appropriate monitoring by adjusting the monitoring resolution according to the size of the object. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object size data to the generation AI and cause the generation AI to adjust the monitoring resolution.

[0041] The monitoring unit can perform monitoring filtering based on the color and shape of the object. For example, the monitoring unit prioritizes monitoring of objects of a specific color (e.g., red). The monitoring unit can also prioritize monitoring of objects of a specific shape (e.g., rectangle). The monitoring unit can also prioritize monitoring of objects of a combination of color and shape (e.g., red rectangle). In this way, by performing monitoring filtering based on the color and shape of the object, it is possible to prioritize monitoring of specific objects. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object color and shape data to the generation AI and have the generation AI perform monitoring filtering.

[0042] The analysis unit can adjust the level of detail of the analysis based on the movement pattern of the object. For example, if the object is moving in a complex manner, the analysis unit can perform a detailed analysis. Furthermore, if the object is moving in a simple manner, the analysis unit can perform a simplified analysis. Furthermore, if the object's movement is predictable, the analysis unit can perform an analysis based on the prediction. This allows for appropriate analysis by adjusting the level of detail of the analysis based on the movement pattern of the object. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input object movement pattern data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the object category. For example, when analyzing human movement, the analysis unit can apply a behavior recognition algorithm. When analyzing vehicle movement, the analysis unit can also apply a path prediction algorithm. When analyzing animal movement, the analysis unit can also apply a species recognition algorithm. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the object category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input object category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit predicts the movement of an object during a specific time period based on past analysis results. The analysis unit can also apply an algorithm that detects abnormal movement based on past analysis results. The analysis unit can also optimize parameters of the analysis algorithm based on past analysis results. This improves the accuracy of the analysis by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] The analysis unit can determine the analysis priority based on the frequency of object movement. For example, the analysis unit prioritizes analysis of objects that move frequently. The analysis unit can also postpone analysis of objects that move less frequently. The analysis unit can also optimally allocate analysis resources according to the frequency of movement. This enables efficient analysis by determining the analysis priority based on the frequency of object movement. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input object movement frequency data into the generation AI and have the generation AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of objects. For example, the analysis unit prioritizes analysis of highly relevant objects. The analysis unit can also postpone analysis of less relevant objects. The analysis unit can also optimally allocate analysis resources according to the relevance of objects. In this way, by adjusting the order of analysis based on the relevance of objects, highly relevant objects can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input object relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0047] The analysis unit can adjust the analysis algorithm according to the speed of the object's movement. For example, the analysis unit applies a quick analysis algorithm to an object moving at high speed. The analysis unit can also apply a detailed analysis algorithm to an object moving at low speed. The analysis unit can also optimize the parameters of the analysis algorithm according to the speed of movement. This enables appropriate analysis by adjusting the analysis algorithm according to the speed of the object's movement. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input object movement speed data to the generation AI and have the generation AI adjust the analysis algorithm.

[0048] The providing unit can adjust the level of detail to be provided based on the importance of the analysis result. For example, the providing unit provides detailed insight for analysis results with high importance. The providing unit can also provide simplified insight for analysis results with low importance. The providing unit can also optimally allocate resources for providing insight according to the importance of the analysis result. This allows important insights to be provided preferentially by adjusting the level of detail to be provided based on the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis result to the generating AI and cause the generating AI to adjust the level of detail to be provided.

[0049] The providing unit can apply different providing algorithms depending on the domain. For example, the providing unit can apply a product placement optimization algorithm based on customer movement to a retail domain. The providing unit can also apply a signal timing adjustment algorithm based on vehicle movement to a traffic management domain. The providing unit can also apply a guide display optimization algorithm based on passenger movement to an airport domain. In this way, optimal insights can be provided for each domain by applying an appropriate providing algorithm depending on the domain. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input domain data to a generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0050] The providing unit can improve the accuracy of the provision by referring to past provision results. For example, the providing unit can optimize insights for a specific time period based on past provision results. The providing unit can also apply an algorithm that detects abnormal behavior based on past provision results. The providing unit can also optimize parameters of the provision algorithm based on past provision results. This improves the accuracy of the provision by referring to past provision results. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0051] The providing unit can determine the priority of provision based on the submission time of the analysis results. For example, the providing unit can provide the most recent analysis results preferentially. The providing unit can also provide analysis results that were submitted earlier later. The providing unit can also optimally allocate resources for providing insights according to the submission time. This allows the latest insights to be provided preferentially by determining the priority of provision based on the submission time of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the submission time of the analysis results to the generation AI and have the generation AI determine the priority of provision.

[0052] The providing unit can adjust the order of provision based on the relevance of the analysis results. For example, the providing unit can provide highly relevant analysis results preferentially. The providing unit can also provide less relevant analysis results later. The providing unit can also optimally allocate resources for providing insights according to the relevance of the analysis results. In this way, by adjusting the order of provision based on the relevance of the analysis results, highly relevant insights can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0053] The providing unit can adjust the use of technical terminology provided according to the user's level of expertise. For example, the providing unit can provide insights that use a lot of technical terminology to a user with high level of expertise. The providing unit can also provide concise and easy-to-understand insights to a user with low level of expertise. The providing unit can also optimize the expression method of the insights to be provided according to the user's level of expertise. This allows for providing insights that are easy for the user to understand by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology provided.

[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 monitoring unit can adjust the monitoring resolution based on the speed of the object's movement. For example, if the object is moving at high speed, the monitoring unit monitors a wide area at low resolution to grasp the overall movement of the object. If the object is moving at low speed, the monitoring unit can perform detailed monitoring at high resolution to capture the details of the object. Furthermore, if the object is stationary, the monitoring unit can perform balanced monitoring at medium resolution. This enables efficient monitoring by adjusting the monitoring resolution according to the speed of the object's movement. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object movement speed data to the generation AI and have the generation AI adjust the monitoring resolution.

[0056] The analysis unit can determine the analysis priority based on the direction of the object's movement. For example, if the object is moving in a specific direction, analysis related to that direction can be prioritized. Also, if the object is moving in the opposite direction, analysis of that movement can be postponed. Furthermore, analysis resources can be optimally allocated according to the direction of the object's movement. This enables efficient analysis by determining the analysis priority based on the direction of the object's movement. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input object movement direction data to the generation AI and have the generation AI determine the analysis priority.

[0057] The monitoring unit can adjust the timing of monitoring based on the object's movement pattern. For example, if the object is moving regularly, the monitoring unit can monitor at regular intervals. Alternatively, if the object is moving irregularly, the monitoring unit can monitor more frequently to collect detailed data. Furthermore, if the object's movement is predictable, the monitoring unit can adjust the timing of monitoring based on the prediction. This enables efficient monitoring by adjusting the timing of monitoring based on the object's movement pattern. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object movement pattern data to a generation AI and have the generation AI adjust the timing of monitoring.

[0058] The providing unit can adjust the timing of provision based on the importance of the analysis results. For example, analysis results with high importance can be provided immediately. Analysis results with low importance can also be provided later. Furthermore, the timing of provision can be optimally allocated depending on the importance of the analysis results. As a result, important insights can be provided quickly by adjusting the timing of provision based on the importance of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the timing of provision.

[0059] The analysis unit can adjust the level of detail of the analysis based on the frequency of object movement. For example, a detailed analysis can be performed on an object that moves frequently. A simplified analysis can also be performed on an object that moves infrequently. Furthermore, analysis resources can be optimally allocated according to the frequency of object movement. This enables efficient analysis by adjusting the level of detail of the analysis based on the frequency of object movement. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input frequency data of object movement into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0060] The monitoring unit can optimally allocate monitoring resources based on the predicted object movement. For example, if it is predicted that an object will move to a specific area, the monitoring unit concentrates resources in that area. The monitoring unit can also adjust the placement of monitoring cameras based on the predicted object movement. Furthermore, the monitoring frequency can be adjusted according to the predicted object movement. This enables efficient monitoring by optimally allocating monitoring resources based on the predicted object movement. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input predicted object movement data into a generation AI and have the generation AI allocate monitoring resources.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The monitoring unit monitors the movement of objects in real time. For example, IP cameras can be used to monitor customer movements in a retail store, vehicle movements in traffic management, or passenger movements in an airport. The monitoring unit can also use AI to collect surveillance data. Step 2: The analysis unit uses generative AI to analyze the data collected by the monitoring unit and identify the spatiotemporal behavioral patterns of objects. For example, it analyzes video data obtained from IP cameras to analyze customer routes, vehicle paths, and passenger movement patterns. Step 3: The provision unit provides specific insights based on the analysis results obtained by the analysis unit, for example, optimizing product placement in a retail store, adjusting traffic signal timing in traffic management, and optimizing guide displays in an airport.

[0063] (Example 2) A flow tracking system according to an embodiment of the present invention monitors object movements in real time, analyzes them using a generative AI, and provides valuable insights. The flow tracking system uses IP cameras to monitor object movements in real time, and the generative AI analyzes the collected data to identify the spatiotemporal behavioral patterns of the objects. For example, the flow tracking system monitors customer movements in a retail store, vehicle movements in traffic management, and passenger movements in an airport in real time. The flow tracking system then uses a generative AI to analyze video data obtained from the IP cameras to identify the spatiotemporal behavioral patterns of the objects. For example, the generative AI analyzes customer routes, vehicle paths, and passenger movement patterns. Based on the analysis results, the flow tracking system then provides valuable insights to improve user experiences across multiple domains, such as retail, traffic management, and airports. For example, the flow tracking system optimizes product placement based on customer movements, adjusts traffic signal timing based on vehicle movements, and optimizes guide displays based on passenger movements. This improves user experience and enables efficient operations in each domain. This allows the flow tracking system to monitor and analyze object movements in real time and provide valuable insights. For example, flow tracking systems can optimize product placement in a retail store based on customer movement, adjust traffic light timing based on vehicle movement in traffic management, and optimize signage in an airport based on passenger movement, improving user experience and enabling efficient operations in each domain.

[0064] A flow tracking system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the movement of objects in real time. For example, the monitoring unit monitors the movement of objects using an IP camera. The monitoring unit can also collect monitoring data using AI. For example, the monitoring unit monitors the movement of customers in a retail store using an IP camera. The monitoring unit can also monitor the movement of vehicles in traffic management. The monitoring unit can also monitor the movement of passengers in an airport. The analysis unit analyzes the data collected by the monitoring unit using a generative AI. For example, the analysis unit analyzes video data obtained from an IP camera to identify spatiotemporal behavior patterns of objects. For example, the analysis unit analyzes customer routes using the generative AI. The analysis unit can also analyze vehicle routes using the generative AI. The analysis unit can also analyze passenger movement patterns using the generative AI. The providing unit provides valuable insights based on the analysis results obtained by the analysis unit. For example, the providing unit optimizes the placement of products in a retail store based on the analysis results. The providing unit can also adjust traffic signal timing in traffic management based on the analysis results. The providing unit can also optimize guidance displays within an airport based on the analysis results. This allows the flow tracking system according to the embodiment to monitor and analyze object movements in real time and provide valuable insights. For example, the flow tracking system may optimize product placement based on customer movements within a retail store, adjust traffic signal timing based on vehicle movements in traffic management, and optimize guidance displays based on passenger movements within an airport. This improves the user experience in each domain and enables efficient operations.

[0065] The monitoring unit can monitor the movement of objects in real time using an IP camera. The monitoring unit, for example, monitors the movement of objects in real time using an IP camera. For example, the monitoring unit monitors the movement of customers in a retail store. The monitoring unit can also monitor the movement of vehicles in traffic management. The monitoring unit can also monitor the movement of passengers in an airport. In this way, by using an IP camera, the movement of objects can be monitored in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input video data acquired by an IP camera into a generation AI and cause the generation AI to perform real-time monitoring.

[0066] The analysis unit can analyze video data obtained from an IP camera and clarify the spatiotemporal behavioral patterns of the object. The analysis unit can, for example, analyze video data obtained from an IP camera and clarify the spatiotemporal behavioral patterns of the object. For example, the analysis unit can analyze customer routes using a generation AI. The analysis unit can also analyze vehicle routes using a generation AI. The analysis unit can also analyze passenger movement patterns using a generation AI. In this way, the behavioral patterns of the object can be revealed by analyzing the video data obtained from the IP camera. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input video data obtained from an IP camera to a generation AI and cause the generation AI to analyze the spatiotemporal behavioral patterns of the object.

[0067] The providing unit can provide valuable insights to improve user experience across multiple domains, such as retail, traffic management, and airports, based on the analysis results. For example, the providing unit can optimize the placement of products in a retail store based on the analysis results. The providing unit can also adjust traffic signal timing in traffic management based on the analysis results. The providing unit can also optimize guide displays in airports based on the analysis results. This improves the user experience by providing valuable insights based on the analysis results. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to provide valuable insights.

[0068] The provision unit can adjust the product placement based on customer movement. The provision unit, for example, adjusts the product placement based on customer movement. For example, the provision unit optimizes the product placement based on customer movement. The provision unit can also change the product placement based on customer movement. The provision unit can also rearrange the product placement based on customer movement. This improves the efficiency of the retail store by optimizing the product placement based on customer movement. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input customer movement data into a generation AI and cause the generation AI to optimize the product placement.

[0069] The providing unit can adjust the timing of signals based on vehicle movement. The providing unit, for example, adjusts the timing of signals based on vehicle movement. For example, the providing unit optimizes the timing of signals based on vehicle movement. The providing unit can also change the timing of signals based on vehicle movement. The providing unit can also reset the timing of signals based on vehicle movement. This improves the efficiency of traffic management by adjusting the timing of signals based on vehicle movement. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input vehicle movement data to the generating AI and cause the generating AI to adjust the timing of signals.

[0070] The providing unit can adjust the guidance display based on the passenger's movement. The providing unit, for example, adjusts the guidance display based on the passenger's movement. For example, the providing unit optimizes the guidance display based on the passenger's movement. The providing unit can also change the guidance display based on the passenger's movement. The providing unit can also reset the guidance display based on the passenger's movement. This improves airport efficiency by optimizing the guidance display based on the passenger's movement. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the passenger's movement to the generating AI and cause the generating AI to optimize the guidance display.

[0071] The monitoring unit can analyze the user's emotions and adjust the timing of monitoring based on the analyzed user emotions. For example, if the user is feeling stressed, the monitoring unit can reduce the frequency of monitoring to reduce the user's burden. Furthermore, if the user is relaxed, the monitoring unit can increase the frequency of monitoring and collect detailed data. Furthermore, if the user is in a hurry, the monitoring unit can speed up the timing of monitoring and collect data in real time. This reduces the user's burden by adjusting the timing of monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI, or without an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of monitoring.

[0072] The monitoring unit can apply different monitoring algorithms depending on the type of object. For example, when monitoring human movement, the monitoring unit can apply a face recognition algorithm to identify individuals. When monitoring vehicle movement, the monitoring unit can also apply a license plate recognition algorithm to identify vehicles. When monitoring animal movement, the monitoring unit can also apply an animal species recognition algorithm to identify species. This improves monitoring accuracy by applying an appropriate monitoring algorithm depending on the type of object. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the type of object to the generation AI and cause the generation AI to apply an appropriate monitoring algorithm.

[0073] The monitoring unit can adjust the monitoring method in response to changes in the environment. For example, when the lighting becomes dim, the monitoring unit continues monitoring using an infrared camera. Furthermore, when it rains, the monitoring unit can also continue monitoring using a waterproof camera. Furthermore, when the lighting becomes brighter, the monitoring unit can switch to a normal camera to continue monitoring. This improves the continuity of monitoring by adjusting the monitoring method in response to changes in the environment. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input environmental change data to the generation AI and have the generation AI adjust the monitoring method.

[0074] The monitoring unit can improve the accuracy of monitoring by referring to past monitoring data. For example, the monitoring unit can predict the movement of an object during a specific time period based on the past monitoring data. The monitoring unit can also apply an algorithm to detect abnormal movement based on the past monitoring data. The monitoring unit can also optimize the placement of surveillance cameras based on the past monitoring data. This improves the accuracy of monitoring by referring to the past monitoring data. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and have the generation AI improve the accuracy of monitoring.

[0075] The monitoring unit can analyze the user's emotions and determine the priority of objects to monitor based on the analyzed user's emotions. For example, if the user feels anxious, the monitoring unit can prioritize monitoring important objects (e.g., children or expensive items). Furthermore, if the user is relaxed, the monitoring unit can perform overall monitoring and collect detailed data. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring specific objects (e.g., vehicles or people). Thus, by determining the priority of objects to monitor based on the user's emotions, important objects can be prioritized for monitoring. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the monitoring unit can be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of objects to monitor.

[0076] The monitoring unit can adjust the monitoring frequency based on the speed of the object's movement. For example, if the object is moving at high speed, the monitoring unit increases the monitoring frequency to collect detailed data. Also, if the object is moving at low speed, the monitoring unit can reduce the monitoring frequency to efficiently collect data. Also, if the object is stationary, the monitoring unit can minimize the monitoring frequency to save resources. Thus, by adjusting the monitoring frequency based on the speed of the object's movement, efficient data collection is possible. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object movement speed data to the generation AI and cause the generation AI to adjust the monitoring frequency.

[0077] The monitoring unit can adjust the monitoring resolution according to the size of the object. For example, when monitoring a large object (e.g., a vehicle), the monitoring unit monitors a wide area at low resolution. Furthermore, when monitoring a small object (e.g., a small animal), the monitoring unit can perform detailed monitoring at high resolution. Furthermore, when monitoring a medium-sized object (e.g., a human), the monitoring unit can perform balanced monitoring at medium resolution. This enables appropriate monitoring by adjusting the monitoring resolution according to the size of the object. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object size data to the generation AI and cause the generation AI to adjust the monitoring resolution.

[0078] The monitoring unit can perform monitoring filtering based on the color and shape of the object. For example, the monitoring unit prioritizes monitoring of objects of a specific color (e.g., red). The monitoring unit can also prioritize monitoring of objects of a specific shape (e.g., rectangle). The monitoring unit can also prioritize monitoring of objects of a combination of color and shape (e.g., red rectangle). In this way, by performing monitoring filtering based on the color and shape of the object, it is possible to prioritize monitoring of specific objects. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object color and shape data to the generation AI and have the generation AI perform monitoring filtering.

[0079] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary of the analysis result. By adjusting the way the analysis is presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0080] The analysis unit can adjust the level of detail of the analysis based on the movement pattern of the object. For example, if the object is moving in a complex manner, the analysis unit can perform a detailed analysis. Furthermore, if the object is moving in a simple manner, the analysis unit can perform a simplified analysis. Furthermore, if the object's movement is predictable, the analysis unit can perform an analysis based on the prediction. This allows for appropriate analysis by adjusting the level of detail of the analysis based on the movement pattern of the object. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input object movement pattern data into the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the object category. For example, when analyzing human movement, the analysis unit can apply a behavior recognition algorithm. When analyzing vehicle movement, the analysis unit can also apply a path prediction algorithm. When analyzing animal movement, the analysis unit can also apply a species recognition algorithm. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the object category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input object category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0082] The analysis unit can improve the accuracy of the analysis by referring to past analysis results. For example, the analysis unit predicts the movement of an object during a specific time period based on past analysis results. The analysis unit can also apply an algorithm that detects abnormal movement based on past analysis results. The analysis unit can also optimize parameters of the analysis algorithm based on past analysis results. This improves the accuracy of the analysis by referring to past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0083] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. By adjusting the length of the analysis based on the user's emotions, an analysis result of an appropriate length for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0084] The analysis unit can determine the analysis priority based on the frequency of object movement. For example, the analysis unit prioritizes analysis of objects that move frequently. The analysis unit can also postpone analysis of objects that move less frequently. The analysis unit can also optimally allocate analysis resources according to the frequency of movement. This enables efficient analysis by determining the analysis priority based on the frequency of object movement. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input object movement frequency data into the generation AI and have the generation AI determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of objects. For example, the analysis unit prioritizes analysis of highly relevant objects. The analysis unit can also postpone analysis of less relevant objects. The analysis unit can also optimally allocate analysis resources according to the relevance of objects. In this way, by adjusting the order of analysis based on the relevance of objects, highly relevant objects can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input object relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0086] The analysis unit can adjust the analysis algorithm according to the speed of the object's movement. For example, the analysis unit applies a quick analysis algorithm to an object moving at high speed. The analysis unit can also apply a detailed analysis algorithm to an object moving at low speed. The analysis unit can also optimize the parameters of the analysis algorithm according to the speed of movement. This enables appropriate analysis by adjusting the analysis algorithm according to the speed of the object's movement. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input object movement speed data to the generation AI and have the generation AI adjust the analysis algorithm.

[0087] The providing unit can analyze the user's emotions and adjust the way in which the insight is presented based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible insight. Furthermore, if the user is relaxed, the providing unit can also provide a detailed insight. Furthermore, if the user is in a hurry, the providing unit can also provide a key point insight. By adjusting the way in which the insight is presented based on the user's emotions, it is possible to provide insight that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way in which the insight is presented.

[0088] The providing unit can adjust the level of detail to be provided based on the importance of the analysis result. For example, the providing unit provides detailed insight for analysis results with high importance. The providing unit can also provide simplified insight for analysis results with low importance. The providing unit can also optimally allocate resources for providing insight according to the importance of the analysis result. This allows important insights to be provided preferentially by adjusting the level of detail to be provided based on the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis result to the generating AI and cause the generating AI to adjust the level of detail to be provided.

[0089] The providing unit can apply different providing algorithms depending on the domain. For example, the providing unit can apply a product placement optimization algorithm based on customer movement to a retail domain. The providing unit can also apply a signal timing adjustment algorithm based on vehicle movement to a traffic management domain. The providing unit can also apply a guide display optimization algorithm based on passenger movement to an airport domain. In this way, optimal insights can be provided for each domain by applying an appropriate providing algorithm depending on the domain. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input domain data to a generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0090] The providing unit can improve the accuracy of the provision by referring to past provision results. For example, the providing unit can optimize insights for a specific time period based on past provision results. The providing unit can also apply an algorithm that detects abnormal behavior based on past provision results. The providing unit can also optimize parameters of the provision algorithm based on past provision results. This improves the accuracy of the provision by referring to past provision results. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0091] The providing unit can analyze the user's emotions and determine the priority of insights to be provided based on the analyzed user's emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing important insights. Furthermore, if the user is relaxed, the providing unit can also provide general insights. Furthermore, if the user is in a hurry, the providing unit can prioritize providing insights that focus on the main points. In this way, by determining the priority of insights to be provided based on the user's emotions, important insights can be provided preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of insights to be provided.

[0092] The providing unit can determine the priority of provision based on the submission time of the analysis results. For example, the providing unit can provide the most recent analysis results preferentially. The providing unit can also provide analysis results that were submitted earlier later. The providing unit can also optimally allocate resources for providing insights according to the submission time. This allows the latest insights to be provided preferentially by determining the priority of provision based on the submission time of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the submission time of the analysis results to the generation AI and have the generation AI determine the priority of provision.

[0093] The providing unit can adjust the order of provision based on the relevance of the analysis results. For example, the providing unit can provide highly relevant analysis results preferentially. The providing unit can also provide less relevant analysis results later. The providing unit can also optimally allocate resources for providing insights according to the relevance of the analysis results. In this way, by adjusting the order of provision based on the relevance of the analysis results, highly relevant insights can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0094] The providing unit can adjust the use of technical terminology provided according to the user's level of expertise. For example, the providing unit can provide insights that use a lot of technical terminology to a user with high level of expertise. The providing unit can also provide concise and easy-to-understand insights to a user with low level of expertise. The providing unit can also optimize the expression method of the insights to be provided according to the user's level of expertise. This allows for providing insights that are easy for the user to understand by adjusting the use of technical terminology provided according to the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology provided. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, analysis unit, and provision unit, described above, may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit monitors the movement of an object in real time using the camera 42 of the smart device 14. The analysis unit may be realized by the specific processing unit 290 of the data processing device 12 and analyzes data collected from the monitoring unit using generative AI. The provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and provides valuable insights based on the analysis results. Some or all of the monitoring unit, analysis unit, and provision unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, the analysis unit, and the providing unit, described above, may be implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit monitors the movement of an object in real time using the camera 42 of the smart glasses 214. The analysis unit may be implemented by the specific processing unit 290 of the data processing device 12 and analyzes data collected from the monitoring unit using generative AI. The providing unit may be implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides valuable insights based on the analysis results. Some or all of the monitoring unit, the analysis unit, and the providing unit may be implemented, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, analysis unit, and provision 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 monitoring unit monitors the movement of an object in real time using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes data collected from the monitoring unit using generative AI. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides valuable insights based on the analysis results. Some or all of the monitoring unit, analysis unit, and provision unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, the analysis unit, and the providing unit described above may be realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit monitors the movement of an object in real time using the camera 42 of the robot 414. The analysis unit may be realized by the specific processing unit 290 of the data processing device 12 and analyzes data collected from the monitoring unit using generative AI. The providing unit may be realized, for example, by the specific processing unit 290 of the data processing device 12 and provides valuable insights based on the analysis results. Some or all of the monitoring unit, the analysis unit, and the providing unit may be realized, for example, by the control unit 46A of the robot 414.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The monitoring unit can adjust the monitoring resolution based on the speed of the object's movement. For example, if the object is moving at high speed, the monitoring unit monitors a wide area at low resolution to grasp the overall movement of the object. If the object is moving at low speed, the monitoring unit can perform detailed monitoring at high resolution to capture the details of the object. Furthermore, if the object is stationary, the monitoring unit can perform balanced monitoring at medium resolution. This enables efficient monitoring by adjusting the monitoring resolution according to the speed of the object's movement. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object movement speed data to the generation AI and have the generation AI adjust the monitoring resolution.

[0097] The analysis unit can determine the analysis priority based on the direction of the object's movement. For example, if the object is moving in a specific direction, analysis related to that direction can be prioritized. Also, if the object is moving in the opposite direction, analysis of that movement can be postponed. Furthermore, analysis resources can be optimally allocated according to the direction of the object's movement. This enables efficient analysis by determining the analysis priority based on the direction of the object's movement. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input object movement direction data to the generation AI and have the generation AI determine the analysis priority.

[0098] The providing unit can analyze the user's emotions and adjust the amount of information to be provided based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can reduce the amount of information to provide in a form that is less burdensome to the user. Also, if the user is relaxed, the providing unit can provide detailed information to allow the user to understand more deeply. Furthermore, if the user is in a hurry, the providing unit can provide information that focuses on the main points to allow the user to understand quickly. By adjusting the amount of information to be provided based on the user's emotions, optimal information can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the amount of information to be provided.

[0099] The monitoring unit can adjust the timing of monitoring based on the object's movement pattern. For example, if the object is moving regularly, the monitoring unit can monitor at regular intervals. Alternatively, if the object is moving irregularly, the monitoring unit can monitor more frequently to collect detailed data. Furthermore, if the object's movement is predictable, the monitoring unit can adjust the timing of monitoring based on the prediction. This enables efficient monitoring by adjusting the timing of monitoring based on the object's movement pattern. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input object movement pattern data to a generation AI and have the generation AI adjust the timing of monitoring.

[0100] The analysis unit can analyze the user's emotions and adjust the visual presentation of the analysis based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible graph or chart. Alternatively, if the user is relaxed, the analysis unit can provide a complex graph or chart containing detailed data. Furthermore, if the user is in a hurry, the analysis unit can provide a visual presentation that focuses on the key points, allowing for quick understanding. By adjusting the visual presentation of the analysis based on the user's emotions, analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the visual presentation of the analysis.

[0101] The providing unit can adjust the timing of provision based on the importance of the analysis results. For example, analysis results with high importance can be provided immediately. Analysis results with low importance can also be provided later. Furthermore, the timing of provision can be optimally allocated depending on the importance of the analysis results. As a result, important insights can be provided quickly by adjusting the timing of provision based on the importance of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis results to the generation AI and cause the generation AI to adjust the timing of provision.

[0102] The monitoring unit can analyze the user's emotions and adjust the monitoring range based on the analyzed user emotions. For example, if the user feels anxious, the monitoring unit can monitor a wide area, providing the user with a sense of security. Alternatively, if the user is relaxed, the monitoring unit can focus monitoring on a specific area and collect detailed data. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring important areas and quickly collect data. This allows optimal monitoring for the user by adjusting the monitoring range based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above-described processing in the monitoring unit can be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring range.

[0103] The analysis unit can adjust the level of detail of the analysis based on the frequency of object movement. For example, a detailed analysis can be performed on an object that moves frequently. A simplified analysis can also be performed on an object that moves infrequently. Furthermore, analysis resources can be optimally allocated according to the frequency of object movement. This enables efficient analysis by adjusting the level of detail of the analysis based on the frequency of object movement. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input frequency data of object movement into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0104] The providing unit can analyze the user's emotions and adjust the format of the information to be provided based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide information in a simple, highly visible text format. Alternatively, if the user is relaxed, the providing unit can provide information in a graphical format containing detailed data. Furthermore, if the user is in a hurry, the providing unit can provide information in a bulleted list format that focuses on the main points, allowing for quick understanding. By adjusting the format of the information to be provided based on the user's emotions, it is possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the format of the information to be provided.

[0105] The monitoring unit can optimally allocate monitoring resources based on the predicted object movement. For example, if it is predicted that an object will move to a specific area, the monitoring unit concentrates resources in that area. The monitoring unit can also adjust the placement of monitoring cameras based on the predicted object movement. Furthermore, the monitoring frequency can be adjusted according to the predicted object movement. This enables efficient monitoring by optimally allocating monitoring resources based on the predicted object movement. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input predicted object movement data into a generation AI and have the generation AI allocate monitoring resources.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The monitoring unit monitors the movement of objects in real time. For example, IP cameras can be used to monitor customer movements in a retail store, vehicle movements in traffic management, or passenger movements in an airport. The monitoring unit can also use AI to collect surveillance data. Step 2: The analysis unit uses generative AI to analyze the data collected by the monitoring unit and identify the spatiotemporal behavioral patterns of objects. For example, it analyzes video data obtained from IP cameras to analyze customer routes, vehicle paths, and passenger movement patterns. Step 3: The provision unit provides specific insights based on the analysis results obtained by the analysis unit, for example, optimizing product placement in a retail store, adjusting traffic signal timing in traffic management, and optimizing guide displays in an airport.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 monitoring unit that monitors the movement of the object in real time; an analysis unit that analyzes the data collected by the monitoring unit and identifies spatiotemporal behavior patterns of objects; a providing unit that provides specific insight based on the analysis results obtained by the analysis unit. A system characterized by:

2. The monitoring unit Monitor object movements in real time using IP cameras 2. The system of claim 1.

3. The analysis unit Analyzing video data obtained from IP cameras to reveal spatiotemporal behavioral patterns of objects 2. The system of claim 1.

4. The providing unit Based on the analysis results, provide valuable insights to improve user experience across multiple domains: retail, traffic management, and airports 2. The system of claim 1.

5. The providing unit Adjust product placement based on customer movement 2. The system of claim 1.

6. The providing unit Adjusting traffic light timing based on vehicle movement 2. The system of claim 1.

7. The providing unit Adapting directions based on passenger movements 2. The system of claim 1.

8. The monitoring unit Analyze user emotions and adjust monitoring timing based on the analyzed user emotions.

2. The system of claim 1.

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