System

Abstract

An object of a system according to an embodiment is to reduce the cost of generating a field verification animation using a 3D model.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, and an animation unit. The collection unit collects site data. The generation unit generates a 3D model based on the data collected by the collection unit. The animation unit creates an animation based on the 3D model generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of high costs for generating on-site inspection animations using 3D models.

[0005] The system according to the embodiment aims to reduce the cost of generating scene inspection animations using 3D models. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and an animation unit. The collection unit collects on-site data. The generation unit generates a 3D model based on the data collected by the collection unit. The animation unit creates an animation based on the 3D model generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the cost of generating scene inspection animations using 3D models. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention reduces the cost of generating crime scene investigation animations using 3D models used in major crime scenes. This system collects crime scene data, generates a 3D model based on the collected data, and creates crime scene investigation animations based on the generated 3D model. For example, the system collects photos, videos, and sensor data from the crime scene. For example, a drone can be used to obtain aerial footage of the crime scene. The system then uses generative AI to generate a 3D model based on the collected data. For example, the aerial footage and sensor data can be analyzed to create detailed 3D models of buildings and terrain. The system then uses generative AI to generate animations based on the generated 3D model. For example, an animation that recreates the circumstances of the crime scene and the situation at the crime scene can be created. This significantly reduces the cost of generating crime scene investigation animations. For example, by quickly collecting crime scene data using a drone and automatically generating a 3D model using generative AI, the time required to create crime scene investigation animations can be significantly reduced. Furthermore, because the system generates a 3D model based on detailed crime scene data, the situation at the crime scene can be accurately recreated. This allows for an accurate understanding of the circumstances of the crime scene and the situation at the crime scene, which can be useful in solving the crime scene. For example, by creating a 3D model that includes detailed information about the internal structure of a building and the terrain, the situation at the site can be reproduced more accurately.

[0029] A scene inspection animation generation system according to an embodiment includes a collection unit, a generation unit, and an animation unit. The collection unit collects scene data. The scene data includes, but is not limited to, image data, sensor data, and audio data. The collection unit can collect scene data using, for example, a ground camera or sensor, or a drone. For example, a drone can be used to obtain aerial footage of the scene. The collection unit can also collect environmental data such as temperature and humidity using a sensor. The generation unit generates a 3D model based on the data collected by the collection unit. The generation unit analyzes the collected data using, for example, a generation AI to generate a 3D model of the scene. For example, the generation AI analyzes aerial footage and data from sensors to create detailed 3D models of buildings and terrain. The generation unit can also perform data preprocessing and noise removal. For example, the generation unit normalizes data, interpolates missing values, and uses filtering and smoothing techniques to remove noise. The animation unit creates animations based on the 3D model generated by the generation unit. The animation unit generates animations based on the 3D model using, for example, a generation AI. For example, the generation AI creates animations that recreate the circumstances of an incident or the situation at the scene. The animation unit can also perform physical simulations. For example, the animation unit recreates realistic movements by taking into account the physical characteristics of the 3D model. As a result, the crime scene inspection animation generation system according to the embodiment can efficiently collect crime scene data, generate a 3D model, and create animations, thereby reducing generation costs.

[0030] The collection unit may collect on-site data using a ground camera, a sensor, or a drone. The collection unit may collect on-site data using, for example, a ground camera. Ground cameras include fixed cameras and mobile cameras. For example, fixed cameras are installed at specific locations and continuously collect data. Mobile cameras are mounted on robots or vehicles and collect data while moving. The collection unit may also collect on-site data using sensors. Sensors include temperature sensors, humidity sensors, distance sensors, and the like. For example, a temperature sensor measures the temperature of the site and collects data. A humidity sensor measures the humidity of the site and collects data. A distance sensor measures the distance to an object and collects data. The collection unit may also collect on-site data using a drone. Drones include fixed-wing drones and multi-rotor drones. For example, fixed-wing drones are suitable for collecting data over a wide area. Multi-rotor drones are suitable for collecting detailed data over a narrow area. This allows detailed data to be obtained by collecting on-site data using a variety of devices.

[0031] The generation unit can analyze the collected data and generate a 3D model of the site. For example, the generation unit analyzes the collected data and generates a 3D model of the site. Analysis includes data filtering, feature extraction, pattern recognition, etc. For example, the generation unit filters the data and removes unnecessary data. Feature extraction is the process of extracting important features from the data. Pattern recognition is the process of recognizing specific patterns in the data. The generation unit combines these analysis techniques to generate a 3D model of the site. For example, the generation unit analyzes data from aerial photographs and sensors to create detailed 3D models of buildings and terrain. This allows the site conditions to be accurately reproduced by analyzing the collected data and generating a 3D model.

[0032] The generation unit can perform data preprocessing and noise removal. For example, the generation unit performs data preprocessing and noise removal. Preprocessing includes data normalization and missing value completion. For example, the generation unit normalizes the data to make the data range uniform. Missing value completion is a process of completing missing parts of the data. Noise removal includes filtering technology and smoothing technology. For example, the generation unit uses filtering technology to remove noise from the data. Smoothing technology is a process of smoothing fluctuations in the data. Thus, by performing data preprocessing and noise removal, the accuracy of 3D model generation is improved.

[0033] The animation unit can perform a physical simulation based on the generated 3D model. The animation unit performs a physical simulation based on the generated 3D model, for example. Physical simulations include mechanical simulations and fluid simulations. For example, the animation unit performs a mechanical simulation to simulate the movement of an object. Fluid simulation is a process that simulates the movement of liquids and gases. This allows the physical simulation to realistically reproduce the situation on-site.

[0034] The animation department can analyze the motion capture data based on the generated 3D model. For example, the animation department analyzes the motion capture data based on the generated 3D model. Motion capture data includes optical motion capture, inertial motion capture, and the like. For example, the animation department analyzes the optical motion capture data to recreate realistic movements. Inertial motion capture is a process of capturing movements using sensors. This allows realistic movements to be recreated by analyzing the motion capture data.

[0035] The collection unit can select an appropriate data collection method based on the environmental conditions of the site. The collection unit selects the optimal data collection method, for example, taking into consideration the environmental conditions of the site (weather, time of day, etc.). Environmental conditions include weather, time of day, and illuminance. For example, the collection unit collects data using a waterproof camera or drone when it is raining. The collection unit can also collect data using an infrared camera at night. Furthermore, when it is windy, the collection unit can collect data using a ground sensor. In this way, highly accurate data can be collected by selecting the optimal data collection method according to the environmental conditions.

[0036] The collection unit can collect detailed data by focusing on a specific area of ​​the scene. For example, the collection unit collects detailed data by focusing on a specific area of ​​the scene. Specific areas include the interior of a building, a specific road section, etc. For example, the collection unit can collect detailed photos and videos by focusing on the location of the incident. The collection unit can also place sensors to collect data by focusing on locations where important evidence is located. Furthermore, the collection unit can use a drone to collect detailed footage by focusing on the internal structure of a building. This allows detailed data to be collected by focusing on a specific area.

[0037] The collection unit can improve accuracy by integrating data from multiple sensors. For example, the collection unit can integrate data from multiple sensors to improve accuracy. The multiple sensors may include cameras, temperature sensors, humidity sensors, etc. For example, the collection unit can integrate data from cameras and sensors to generate a more detailed 3D model. The collection unit can also integrate data from drones and ground sensors to grasp an overall picture of the site. Furthermore, the collection unit can integrate footage from multiple cameras to collect data from different perspectives. This improves the accuracy of the data by integrating data from multiple sensors.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the scene. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the geographical location information of the scene. Geographical location information includes GPS data, map data, etc. For example, the collection unit prioritizes collecting data around the location where the incident occurred. The collection unit can also prioritize collecting data from locations where important evidence is located. Furthermore, the collection unit can also collect highly relevant data by taking into account the topographical information of the scene. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information.

[0039] The collection unit can adjust the collection method based on past data from the scene. For example, the collection unit optimizes the collection method by referring to past data from the scene. Past data includes past sensor data, past image data, etc. For example, the collection unit refers to past incident data and selects the optimal collection method. The collection unit can also propose an efficient data collection method based on past scene data. Furthermore, the collection unit can analyze past data and optimize the collection method. In this way, the collection method can be optimized by referring to past data.

[0040] The collection unit can adjust the collection range based on a specific event at the site. The collection unit adjusts the collection range based on, for example, a specific event at the site (e.g., an accident or disaster). Specific events include accidents, disasters, and event hosting. For example, the collection unit prioritizes collecting data from the accident site. In addition, when a disaster occurs, the collection unit can also collect data on the damage range. Furthermore, the collection unit can adjust the collection range based on the specific event. In this way, by adjusting the collection range based on the specific event, necessary data can be collected efficiently.

[0041] The generation unit can select a generation algorithm based on the accuracy of the data. The generation unit selects a generation algorithm based on, for example, the accuracy of the data. The generation algorithm includes a machine learning algorithm, a rule-based algorithm, and the like. For example, the generation unit generates a detailed 3D model using high-accuracy data. The generation unit can also generate a simplified 3D model using low-accuracy data. Furthermore, the generation unit can select an optimal generation algorithm depending on the accuracy of the data. In this way, by selecting a generation algorithm depending on the accuracy of the data, an optimal 3D model can be generated.

[0042] The generation unit can integrate different data sources to improve the accuracy of the model. For example, the generation unit can integrate different data sources (photos, videos, sensors) to improve the accuracy of the model. Different data sources include integrating sensor data and image data. For example, the generation unit can integrate photo and video data to generate a detailed 3D model. The generation unit can also integrate sensor data to generate a detailed 3D model of the site. Furthermore, the generation unit can integrate different data sources to improve the accuracy of the model. As a result, the accuracy of the model is improved by integrating different data sources.

[0043] The generation unit can perform data preprocessing to improve generation accuracy. The generation unit can perform, for example, data preprocessing (noise removal, completion) to improve generation accuracy. Preprocessing includes noise removal, data completion, etc. For example, the generation unit can remove noise from the data to generate a detailed 3D model. The generation unit can also complete missing data to generate an accurate 3D model of the site. Furthermore, the generation unit can perform data preprocessing to improve generation accuracy. As a result, data preprocessing improves generation accuracy.

[0044] The generation unit can generate a detailed model by focusing on a specific area of ​​the crime scene. For example, the generation unit generates a detailed model by focusing on a specific area of ​​the crime scene. Specific areas include the interior of a building, a specific road section, etc. For example, the generation unit generates a detailed 3D model by focusing on the location where an incident occurred. The generation unit can also generate a detailed 3D model by focusing on a location where important evidence is located. Furthermore, the generation unit can generate a detailed 3D model by focusing on the internal structure of a building. This allows a detailed 3D model to be generated by focusing on a specific area.

[0045] The generation unit can optimize the generation algorithm by referring to past 3D model data. The generation unit, for example, optimizes the generation algorithm by referring to past 3D model data. Past 3D model data includes past project data, archive data, etc. For example, the generation unit selects an optimal generation algorithm by referring to past 3D model data. The generation unit can also propose an efficient generation algorithm based on past data. Furthermore, the generation unit can analyze past 3D model data and optimize the generation algorithm. In this way, the generation algorithm can be optimized by referring to past data.

[0046] The generation unit can adjust the generation range of the model based on a specific event at the site. The generation unit adjusts the generation range of the model based on, for example, a specific event at the site (e.g., an accident or disaster). Specific events include accidents, disasters, and event hosting. For example, the generation unit prioritizes generating a 3D model of the accident site. In addition, when a disaster occurs, the generation unit can also generate a 3D model of the damage area. Furthermore, the generation unit can adjust the generation range of the model based on a specific event. In this way, by adjusting the generation range of the model based on a specific event, necessary data can be collected efficiently.

[0047] The animation unit can reproduce realistic movements by taking into account the physical properties of the 3D model. For example, the animation unit reproduces realistic movements by taking into account the physical properties of the 3D model. Physical properties include mass, friction coefficient, elasticity, etc. For example, the animation unit reproduces realistic movements by taking into account the gravity and friction of the 3D model. The animation unit can also reproduce realistic movements by taking into account the material and shape of the 3D model. Furthermore, the animation unit can reproduce realistic movements based on the physical properties of the 3D model. This allows realistic movements to be reproduced by taking into account the physical properties of the 3D model.

[0048] The animation unit can simulate the circumstances under which an incident occurs and perform a detailed reenactment. The animation unit can, for example, simulate the circumstances under which an incident occurs and perform a detailed reenactment. The circumstances under which an incident occurs include a traffic accident, a fire, a crime, and the like. For example, the animation unit can simulate the circumstances at the time of the incident and generate a detailed animation. The animation unit can also generate a detailed animation taking into account the location and time of the incident. Furthermore, the animation unit can perform a detailed reenactment based on the circumstances under which the incident occurred. This makes it possible to perform a detailed reenactment by simulating the circumstances under which the incident occurred.

[0049] The animation unit can analyze motion capture data to reproduce realistic movements. For example, the animation unit analyzes motion capture data to reproduce realistic movements. Motion capture data includes optical motion capture, inertial motion capture, and the like. For example, the animation unit analyzes optical motion capture data to reproduce realistic movements. The animation unit can also generate detailed animations based on inertial motion capture data. Furthermore, the animation unit can also use motion capture data to reproduce realistic movements. In this way, realistic movements can be reproduced by analyzing the motion capture data.

[0050] The animation unit may generate detailed animations by focusing on specific areas of the crime scene. For example, the animation unit may generate detailed animations by focusing on specific areas of the crime scene. Specific areas include the interior of a building, a specific road section, etc. For example, the animation unit may generate detailed animations by focusing on the location where an incident occurred. The animation unit may also generate detailed animations by focusing on locations where important evidence is located. Furthermore, the animation unit may generate detailed animations by focusing on the internal structure of a building. This allows detailed animations to be generated by focusing on specific areas.

[0051] The animation unit can optimize the generation algorithm by referring to past animation data. The animation unit, for example, optimizes the generation algorithm by referring to past animation data. Past animation data includes past project data, archive data, etc. For example, the animation unit selects an optimal generation algorithm by referring to past animation data. The animation unit can also propose an efficient generation algorithm based on past data. Furthermore, the animation unit can analyze past animation data and optimize the generation algorithm. In this way, the generation algorithm can be optimized by referring to past data.

[0052] The animation unit can adjust the range of animation generation based on a specific event at the site. The animation unit adjusts the range of animation generation based on, for example, a specific event at the site (e.g., an accident or disaster). Specific events include accidents, disasters, and event hosting. For example, the animation unit generates animation of the accident site with priority. In addition, when a disaster occurs, the animation unit can also generate animation of the damage range. Furthermore, the animation unit can adjust the range of animation generation based on a specific event. In this way, by adjusting the range of animation generation based on a specific event, necessary data can be collected efficiently.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When collecting data from the scene, the collection unit can analyze surrounding audio data in real time and detect abnormal sounds. For example, the collection unit can detect abnormal sounds such as gunshots or screams and prioritize collecting that data. The collection unit can also automatically collect video data from the surrounding area when an abnormal sound is detected. Furthermore, the collection unit can identify the source of the abnormal sound and provide that location information to other elements. This allows the detection of abnormal sounds and the priority collection of that data to be useful in solving the incident.

[0055] When analyzing collected data, the generation unit can evaluate the reliability of the data and exclude unreliable data. For example, the generation unit can detect sensor failures or missing data and exclude that data. The generation unit can also check the consistency of the data and exclude contradictory data. Furthermore, the generation unit can compare multiple data sources to evaluate the reliability of the data and select highly reliable data. This can improve the accuracy of 3D model generation by eliminating unreliable data.

[0056] When analyzing collected data, the generation unit can generate a 3D model that takes into account changes in the data over time. For example, the generation unit generates a 3D model that reflects changes in buildings and topographical features over time. The generation unit can also compare past data with current data to identify points of change. Furthermore, the generation unit can perform simulations that take into account changes over time and predict future changes. This allows for the generation of a 3D model that takes changes over time into account, making it possible to more accurately reproduce on-site conditions.

[0057] When analyzing collected data, the generation unit can generate a 3D model taking into account the spatial correlation of the data. For example, the generation unit generates a 3D model taking into account the relationship between adjacent buildings and the terrain. The generation unit can also analyze the spatial distribution of data and detect anomalous data. Furthermore, the generation unit can perform simulations that take into account spatial correlation to more accurately reproduce on-site conditions. As a result, by generating a 3D model that takes spatial correlation into account, on-site conditions can be more accurately reproduced.

[0058] When analyzing collected data, the generation unit can evaluate the reliability of the data and exclude unreliable data. For example, the generation unit can detect sensor failures or missing data and exclude that data. The generation unit can also check the consistency of the data and exclude contradictory data. Furthermore, the generation unit can compare multiple data sources to evaluate the reliability of the data and select highly reliable data. This can improve the accuracy of 3D model generation by eliminating unreliable data.

[0059] When analyzing collected data, the generation unit can generate a 3D model that takes into account changes in the data over time. For example, the generation unit generates a 3D model that reflects changes in buildings and topographical features over time. The generation unit can also compare past data with current data to identify points of change. Furthermore, the generation unit can perform simulations that take into account changes over time and predict future changes. This allows for the generation of a 3D model that takes changes over time into account, making it possible to more accurately reproduce on-site conditions.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects on-site data. On-site data includes image data, sensor data, and audio data. The collection unit can collect on-site data using ground cameras and sensors, or drones. For example, a drone can be used to obtain aerial footage of the site. The collection unit can also use sensors to collect environmental data such as temperature and humidity. Step 2: The generation unit generates a 3D model based on the data collected by the collection unit. The generation unit uses generative AI to analyze the collected data and generate a 3D model of the site. For example, the generative AI analyzes aerial footage and data from sensors to create detailed 3D models of buildings and terrain. The generation unit can also perform data preprocessing and noise removal. For example, it normalizes the data, fills in missing values, and uses filtering and smoothing techniques to remove noise. Step 3: The animation department creates animations based on the 3D models generated by the generation department. The animation department uses a generation AI to generate animations based on the 3D models. For example, the generation AI creates animations that recreate the circumstances of an incident or the situation at the scene. The animation department can also perform physical simulations. For example, it takes into account the physical properties of the 3D model to recreate realistic movements.

[0062] (Example 2) A system according to an embodiment of the present invention reduces the cost of generating crime scene investigation animations using 3D models used in major crime scenes. This system collects crime scene data, generates a 3D model based on the collected data, and creates crime scene investigation animations based on the generated 3D model. For example, the system collects photos, videos, and sensor data from the crime scene. For example, a drone can be used to obtain aerial footage of the crime scene. The system then uses generative AI to generate a 3D model based on the collected data. For example, the aerial footage and sensor data can be analyzed to create detailed 3D models of buildings and terrain. The system then uses generative AI to generate animations based on the generated 3D model. For example, an animation that recreates the circumstances of the crime scene and the situation at the crime scene can be created. This significantly reduces the cost of generating crime scene investigation animations. For example, by quickly collecting crime scene data using a drone and automatically generating a 3D model using generative AI, the time required to create crime scene investigation animations can be significantly reduced. Furthermore, because the system generates a 3D model based on detailed crime scene data, the situation at the crime scene can be accurately recreated. This allows for an accurate understanding of the circumstances of the crime scene and the situation at the crime scene, which can be useful in solving the crime scene. For example, by creating a 3D model that includes detailed information about the internal structure of a building and the terrain, the situation at the site can be reproduced more accurately.

[0063] A scene inspection animation generation system according to an embodiment includes a collection unit, a generation unit, and an animation unit. The collection unit collects scene data. The scene data includes, but is not limited to, image data, sensor data, and audio data. The collection unit can collect scene data using, for example, a ground camera or sensor, or a drone. For example, a drone can be used to obtain aerial footage of the scene. The collection unit can also collect environmental data such as temperature and humidity using a sensor. The generation unit generates a 3D model based on the data collected by the collection unit. The generation unit analyzes the collected data using, for example, a generation AI to generate a 3D model of the scene. For example, the generation AI analyzes aerial footage and data from sensors to create detailed 3D models of buildings and terrain. The generation unit can also perform data preprocessing and noise removal. For example, the generation unit normalizes data, interpolates missing values, and uses filtering and smoothing techniques to remove noise. The animation unit creates animations based on the 3D model generated by the generation unit. The animation unit generates animations based on the 3D model using, for example, a generation AI. For example, the generation AI creates animations that recreate the circumstances of an incident or the situation at the scene. The animation unit can also perform physical simulations. For example, the animation unit recreates realistic movements by taking into account the physical characteristics of the 3D model. As a result, the crime scene inspection animation generation system according to the embodiment can efficiently collect crime scene data, generate a 3D model, and create animations, thereby reducing generation costs.

[0064] The collection unit may collect on-site data using a ground camera, a sensor, or a drone. The collection unit may collect on-site data using, for example, a ground camera. Ground cameras include fixed cameras and mobile cameras. For example, fixed cameras are installed at specific locations and continuously collect data. Mobile cameras are mounted on robots or vehicles and collect data while moving. The collection unit may also collect on-site data using sensors. Sensors include temperature sensors, humidity sensors, distance sensors, and the like. For example, a temperature sensor measures the temperature of the site and collects data. A humidity sensor measures the humidity of the site and collects data. A distance sensor measures the distance to an object and collects data. The collection unit may also collect on-site data using a drone. Drones include fixed-wing drones and multi-rotor drones. For example, fixed-wing drones are suitable for collecting data over a wide area. Multi-rotor drones are suitable for collecting detailed data over a narrow area. This allows detailed data to be obtained by collecting on-site data using a variety of devices.

[0065] The generation unit can analyze the collected data and generate a 3D model of the site. For example, the generation unit analyzes the collected data and generates a 3D model of the site. Analysis includes data filtering, feature extraction, pattern recognition, etc. For example, the generation unit filters the data and removes unnecessary data. Feature extraction is the process of extracting important features from the data. Pattern recognition is the process of recognizing specific patterns in the data. The generation unit combines these analysis techniques to generate a 3D model of the site. For example, the generation unit analyzes data from aerial photographs and sensors to create detailed 3D models of buildings and terrain. This allows the site conditions to be accurately reproduced by analyzing the collected data and generating a 3D model.

[0066] The generation unit can perform data preprocessing and noise removal. For example, the generation unit performs data preprocessing and noise removal. Preprocessing includes data normalization and missing value completion. For example, the generation unit normalizes the data to make the data range uniform. Missing value completion is a process of completing missing parts of the data. Noise removal includes filtering technology and smoothing technology. For example, the generation unit uses filtering technology to remove noise from the data. Smoothing technology is a process of smoothing fluctuations in the data. Thus, by performing data preprocessing and noise removal, the accuracy of 3D model generation is improved.

[0067] The animation unit can perform a physical simulation based on the generated 3D model. The animation unit performs a physical simulation based on the generated 3D model, for example. Physical simulations include mechanical simulations and fluid simulations. For example, the animation unit performs a mechanical simulation to simulate the movement of an object. Fluid simulation is a process that simulates the movement of liquids and gases. This allows the physical simulation to realistically reproduce the situation on-site.

[0068] The animation department can analyze the motion capture data based on the generated 3D model. For example, the animation department analyzes the motion capture data based on the generated 3D model. Motion capture data includes optical motion capture, inertial motion capture, and the like. For example, the animation department analyzes the optical motion capture data to recreate realistic movements. Inertial motion capture is a process of capturing movements using sensors. This allows realistic movements to be recreated by analyzing the motion capture data.

[0069] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. Estimation of the user's emotions includes facial expression recognition, voice analysis, biometrics, and the like. For example, the collection unit delays the timing of data collection when the user is feeling stressed and starts collection when the user is relaxed. The collection unit can also advance the timing of data collection when the user is concentrating, thereby efficiently collecting data. Furthermore, the collection unit can adjust the timing of data collection when the user is tired and resume collection after the user has rested. This enables efficient data collection by adjusting the timing of data collection according to the user's emotions.

[0070] The collection unit can select an appropriate data collection method based on the environmental conditions of the site. The collection unit selects the optimal data collection method, for example, taking into consideration the environmental conditions of the site (weather, time of day, etc.). Environmental conditions include weather, time of day, and illuminance. For example, the collection unit collects data using a waterproof camera or drone when it is raining. The collection unit can also collect data using an infrared camera at night. Furthermore, when it is windy, the collection unit can collect data using a ground sensor. In this way, highly accurate data can be collected by selecting the optimal data collection method according to the environmental conditions.

[0071] The collection unit can collect detailed data by focusing on a specific area of ​​the scene. For example, the collection unit collects detailed data by focusing on a specific area of ​​the scene. Specific areas include the interior of a building, a specific road section, etc. For example, the collection unit can collect detailed photos and videos by focusing on the location of the incident. The collection unit can also place sensors to collect data by focusing on locations where important evidence is located. Furthermore, the collection unit can use a drone to collect detailed footage by focusing on the internal structure of a building. This allows detailed data to be collected by focusing on a specific area.

[0072] The collection unit can improve accuracy by integrating data from multiple sensors. For example, the collection unit can integrate data from multiple sensors to improve accuracy. The multiple sensors may include cameras, temperature sensors, humidity sensors, etc. For example, the collection unit can integrate data from cameras and sensors to generate a more detailed 3D model. The collection unit can also integrate data from drones and ground sensors to grasp an overall picture of the site. Furthermore, the collection unit can integrate footage from multiple cameras to collect data from different perspectives. This improves the accuracy of the data by integrating data from multiple sensors.

[0073] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user's emotions. Estimation of the user's emotions includes facial expression recognition, voice analysis, biometrics, and the like. For example, the collection unit prioritizes collection of important data when the user is nervous. The collection unit can also collect detailed data when the user is relaxed. Furthermore, the collection unit can prioritize data that can be collected quickly when the user is in a hurry. In this way, important data can be collected preferentially by determining the priority of data according to the user's emotions.

[0074] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the scene. The collection unit, for example, prioritizes collecting highly relevant data by taking into account the geographical location information of the scene. Geographical location information includes GPS data, map data, etc. For example, the collection unit prioritizes collecting data around the location where the incident occurred. The collection unit can also prioritize collecting data from locations where important evidence is located. Furthermore, the collection unit can also collect highly relevant data by taking into account the topographical information of the scene. In this way, highly relevant data can be collected preferentially by taking into account the geographical location information.

[0075] The collection unit can adjust the collection method based on past data from the scene. For example, the collection unit optimizes the collection method by referring to past data from the scene. Past data includes past sensor data, past image data, etc. For example, the collection unit refers to past incident data and selects the optimal collection method. The collection unit can also propose an efficient data collection method based on past scene data. Furthermore, the collection unit can analyze past data and optimize the collection method. In this way, the collection method can be optimized by referring to past data.

[0076] The collection unit can adjust the collection range based on a specific event at the site. The collection unit adjusts the collection range based on, for example, a specific event at the site (e.g., an accident or disaster). Specific events include accidents, disasters, and event hosting. For example, the collection unit prioritizes collecting data from the accident site. In addition, when a disaster occurs, the collection unit can also collect data on the damage range. Furthermore, the collection unit can adjust the collection range based on the specific event. In this way, by adjusting the collection range based on the specific event, necessary data can be collected efficiently.

[0077] The generation unit can estimate the user's emotion and adjust the 3D model generation method based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the 3D model generation method based on the estimated user's emotion. Estimation of the user's emotion includes facial expression recognition, voice analysis, biometrics, and the like. For example, the generation unit can generate a detailed 3D model when the user is relaxed. The generation unit can also generate a simplified 3D model when the user is in a hurry. Furthermore, the generation unit can generate a visually appealing 3D model when the user is excited. This enables efficient model generation by adjusting the 3D model generation method according to the user's emotion.

[0078] The generation unit can select a generation algorithm based on the accuracy of the data. The generation unit selects a generation algorithm based on, for example, the accuracy of the data. The generation algorithm includes a machine learning algorithm, a rule-based algorithm, and the like. For example, the generation unit generates a detailed 3D model using high-accuracy data. The generation unit can also generate a simplified 3D model using low-accuracy data. Furthermore, the generation unit can select an optimal generation algorithm depending on the accuracy of the data. In this way, by selecting a generation algorithm depending on the accuracy of the data, an optimal 3D model can be generated.

[0079] The generation unit can integrate different data sources to improve the accuracy of the model. For example, the generation unit can integrate different data sources (photos, videos, sensors) to improve the accuracy of the model. Different data sources include integrating sensor data and image data. For example, the generation unit can integrate photo and video data to generate a detailed 3D model. The generation unit can also integrate sensor data to generate a detailed 3D model of the site. Furthermore, the generation unit can integrate different data sources to improve the accuracy of the model. As a result, the accuracy of the model is improved by integrating different data sources.

[0080] The generation unit can perform data preprocessing to improve generation accuracy. The generation unit can perform, for example, data preprocessing (noise removal, completion) to improve generation accuracy. Preprocessing includes noise removal, data completion, etc. For example, the generation unit can remove noise from the data to generate a detailed 3D model. The generation unit can also complete missing data to generate an accurate 3D model of the site. Furthermore, the generation unit can perform data preprocessing to improve generation accuracy. As a result, data preprocessing improves generation accuracy.

[0081] The generation unit can estimate the user's emotion and adjust the level of detail of the 3D model based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the level of detail of the 3D model based on the estimated user's emotion. Estimation of the user's emotion includes facial expression recognition, voice analysis, biometrics, etc. For example, the generation unit can generate a detailed 3D model when the user is relaxed. The generation unit can also generate a simplified 3D model when the user is in a hurry. Furthermore, the generation unit can generate a visually appealing 3D model when the user is excited. This enables efficient model generation by adjusting the level of detail of the 3D model according to the user's emotion.

[0082] The generation unit can generate a detailed model by focusing on a specific area of ​​the crime scene. For example, the generation unit generates a detailed model by focusing on a specific area of ​​the crime scene. Specific areas include the interior of a building, a specific road section, etc. For example, the generation unit generates a detailed 3D model by focusing on the location where an incident occurred. The generation unit can also generate a detailed 3D model by focusing on a location where important evidence is located. Furthermore, the generation unit can generate a detailed 3D model by focusing on the internal structure of a building. This allows a detailed 3D model to be generated by focusing on a specific area.

[0083] The generation unit can optimize the generation algorithm by referring to past 3D model data. The generation unit, for example, optimizes the generation algorithm by referring to past 3D model data. Past 3D model data includes past project data, archive data, etc. For example, the generation unit selects an optimal generation algorithm by referring to past 3D model data. The generation unit can also propose an efficient generation algorithm based on past data. Furthermore, the generation unit can analyze past 3D model data and optimize the generation algorithm. In this way, the generation algorithm can be optimized by referring to past data.

[0084] The generation unit can adjust the generation range of the model based on a specific event at the site. The generation unit adjusts the generation range of the model based on, for example, a specific event at the site (e.g., an accident or disaster). Specific events include accidents, disasters, and event hosting. For example, the generation unit prioritizes generating a 3D model of the accident site. In addition, when a disaster occurs, the generation unit can also generate a 3D model of the damage area. Furthermore, the generation unit can adjust the generation range of the model based on a specific event. In this way, by adjusting the generation range of the model based on a specific event, necessary data can be collected efficiently.

[0085] The animation unit can estimate the user's emotion and adjust the animation expression method based on the estimated user's emotion. For example, the animation unit can estimate the user's emotion and adjust the animation expression method based on the estimated user's emotion. Estimation of the user's emotion includes facial expression recognition, voice analysis, biometrics, and the like. For example, if the user is relaxed, the animation unit can generate an animation that progresses at a leisurely pace. Also, if the user is in a hurry, the animation unit can generate an animation that emphasizes the shortest route. Furthermore, if the user is excited, the animation unit can generate an animation that adds a visually stimulating effect. In this way, by adjusting the animation expression method according to the user's emotion, visually appealing animation can be generated.

[0086] The animation unit can reproduce realistic movements by taking into account the physical properties of the 3D model. For example, the animation unit reproduces realistic movements by taking into account the physical properties of the 3D model. Physical properties include mass, friction coefficient, elasticity, etc. For example, the animation unit reproduces realistic movements by taking into account the gravity and friction of the 3D model. The animation unit can also reproduce realistic movements by taking into account the material and shape of the 3D model. Furthermore, the animation unit can reproduce realistic movements based on the physical properties of the 3D model. This allows realistic movements to be reproduced by taking into account the physical properties of the 3D model.

[0087] The animation unit can simulate the circumstances under which an incident occurs and perform a detailed reenactment. The animation unit can, for example, simulate the circumstances under which an incident occurs and perform a detailed reenactment. The circumstances under which an incident occurs include a traffic accident, a fire, a crime, and the like. For example, the animation unit can simulate the circumstances at the time of the incident and generate a detailed animation. The animation unit can also generate a detailed animation taking into account the location and time of the incident. Furthermore, the animation unit can perform a detailed reenactment based on the circumstances under which the incident occurred. This makes it possible to perform a detailed reenactment by simulating the circumstances under which the incident occurred.

[0088] The animation unit can analyze motion capture data to reproduce realistic movements. For example, the animation unit analyzes motion capture data to reproduce realistic movements. Motion capture data includes optical motion capture, inertial motion capture, and the like. For example, the animation unit analyzes optical motion capture data to reproduce realistic movements. The animation unit can also generate detailed animations based on inertial motion capture data. Furthermore, the animation unit can also use motion capture data to reproduce realistic movements. In this way, realistic movements can be reproduced by analyzing the motion capture data.

[0089] The animation unit can estimate the user's emotion and adjust the length of the animation based on the estimated user's emotion. For example, the animation unit can estimate the user's emotion and adjust the length of the animation based on the estimated user's emotion. Estimation of the user's emotion includes facial expression recognition, voice analysis, biometrics, and the like. For example, if the user is in a hurry, the animation unit can generate a short, to-the-point animation. If the user is relaxed, the animation unit can also generate a longer animation with detailed explanations. Furthermore, if the user is excited, the animation unit can generate an animation with a visually stimulating effect. In this way, by adjusting the length of the animation according to the user's emotion, visually appealing animations can be generated.

[0090] The animation unit may generate detailed animations by focusing on specific areas of the crime scene. For example, the animation unit may generate detailed animations by focusing on specific areas of the crime scene. Specific areas include the interior of a building, a specific road section, etc. For example, the animation unit may generate detailed animations by focusing on the location where an incident occurred. The animation unit may also generate detailed animations by focusing on locations where important evidence is located. Furthermore, the animation unit may generate detailed animations by focusing on the internal structure of a building. This allows detailed animations to be generated by focusing on specific areas.

[0091] The animation unit can optimize the generation algorithm by referring to past animation data. The animation unit, for example, optimizes the generation algorithm by referring to past animation data. Past animation data includes past project data, archive data, etc. For example, the animation unit selects an optimal generation algorithm by referring to past animation data. The animation unit can also propose an efficient generation algorithm based on past data. Furthermore, the animation unit can analyze past animation data and optimize the generation algorithm. In this way, the generation algorithm can be optimized by referring to past data.

[0092] The animation unit can adjust the range of animation generation based on a specific event at the site. The animation unit adjusts the range of animation generation based on, for example, a specific event at the site (e.g., an accident or disaster). Specific events include accidents, disasters, and event hosting. For example, the animation unit generates animation of the accident site with priority. In addition, when a disaster occurs, the animation unit can also generate animation of the damage range. Furthermore, the animation unit can adjust the range of animation generation based on a specific event. In this way, by adjusting the range of animation generation based on a specific event, necessary data can be collected efficiently. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, and animation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect on-site data using the camera 42 or sensors of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a 3D model based on the collected data. The animation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates animation based on the generated 3D model. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, generation unit, and animation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect on-site data using the camera 42 or a sensor of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model based on the collected data. The animation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates animation based on the generated 3D model. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and animation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect on-site data using the camera 42 or sensors of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model based on the collected data. The animation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates animation based on the generated 3D model. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, and animation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect on-site data using the camera 42 or sensors of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D model based on the collected data. The animation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates animation based on the generated 3D model.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When collecting data from the scene, the collection unit can analyze surrounding audio data in real time and detect abnormal sounds. For example, the collection unit can detect abnormal sounds such as gunshots or screams and prioritize collecting that data. The collection unit can also automatically collect video data from the surrounding area when an abnormal sound is detected. Furthermore, the collection unit can identify the source of the abnormal sound and provide that location information to other elements. This allows the detection of abnormal sounds and the priority collection of that data to be useful in solving the incident.

[0095] When analyzing collected data, the generation unit can evaluate the reliability of the data and exclude unreliable data. For example, the generation unit can detect sensor failures or missing data and exclude that data. The generation unit can also check the consistency of the data and exclude contradictory data. Furthermore, the generation unit can compare multiple data sources to evaluate the reliability of the data and select highly reliable data. This can improve the accuracy of 3D model generation by eliminating unreliable data.

[0096] When analyzing collected data, the generation unit can generate a 3D model that takes into account changes in the data over time. For example, the generation unit generates a 3D model that reflects changes in buildings and topographical features over time. The generation unit can also compare past data with current data to identify points of change. Furthermore, the generation unit can perform simulations that take into account changes over time and predict future changes. This allows for the generation of a 3D model that takes changes over time into account, making it possible to more accurately reproduce on-site conditions.

[0097] When analyzing collected data, the generation unit can generate a 3D model taking into account the spatial correlation of the data. For example, the generation unit generates a 3D model taking into account the relationship between adjacent buildings and the terrain. The generation unit can also analyze the spatial distribution of data and detect anomalous data. Furthermore, the generation unit can perform simulations that take into account spatial correlation to more accurately reproduce on-site conditions. As a result, by generating a 3D model that takes spatial correlation into account, on-site conditions can be more accurately reproduced.

[0098] The animation unit can estimate the user's emotion based on the generated 3D model and adjust the visual effects of the animation based on the estimated user's emotion. For example, if the user is surprised, the animation unit can add a visually emphasized effect. If the user is sad, the animation unit can also reflect the emotion by darkening the color tone. Furthermore, if the user is excited, the animation unit can also provide visual stimulation by adjusting the speed of the movement. In this way, by adjusting the visual effects of the animation according to the user's emotion, it is possible to generate animations that appeal to more emotions.

[0099] The animation unit can estimate the user's emotions based on the generated 3D model and adjust the sound effects of the animation based on the estimated user's emotions. For example, if the user is nervous, the animation unit can add tense music. If the user is relaxed, the animation unit can add calm music. If the user is excited, the animation unit can add intense music to enhance the emotion. In this way, by adjusting the sound effects of the animation according to the user's emotions, it is possible to generate animations that appeal to more emotions.

[0100] The collection unit can estimate the user's emotions and adjust the range of data collection based on the estimated user emotions. For example, if the user feels anxious, the collection unit collects a wide range of data. Alternatively, if the user feels relieved, the collection unit can collect data by focusing on a specific area. Furthermore, if the user feels excited, the collection unit can reflect the user's emotions by collecting detailed data. This allows for efficient data collection by adjusting the range of data collection according to the user's emotions.

[0101] When analyzing collected data, the generation unit can evaluate the reliability of the data and exclude unreliable data. For example, the generation unit can detect sensor failures or missing data and exclude that data. The generation unit can also check the consistency of the data and exclude contradictory data. Furthermore, the generation unit can compare multiple data sources to evaluate the reliability of the data and select highly reliable data. This can improve the accuracy of 3D model generation by eliminating unreliable data.

[0102] When analyzing collected data, the generation unit can generate a 3D model that takes into account changes in the data over time. For example, the generation unit generates a 3D model that reflects changes in buildings and topographical features over time. The generation unit can also compare past data with current data to identify points of change. Furthermore, the generation unit can perform simulations that take into account changes over time and predict future changes. This allows for the generation of a 3D model that takes changes over time into account, making it possible to more accurately reproduce on-site conditions.

[0103] The animation unit can estimate the user's emotion based on the generated 3D model and adjust the visual effects of the animation based on the estimated user's emotion. For example, if the user is surprised, the animation unit can add a visually emphasized effect. If the user is sad, the animation unit can also reflect the emotion by darkening the color tone. Furthermore, if the user is excited, the animation unit can also provide visual stimulation by adjusting the speed of the movement. In this way, by adjusting the visual effects of the animation according to the user's emotion, it is possible to generate animations that appeal to more emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects on-site data. On-site data includes image data, sensor data, and audio data. The collection unit can collect on-site data using ground cameras and sensors, or drones. For example, a drone can be used to obtain aerial footage of the site. The collection unit can also use sensors to collect environmental data such as temperature and humidity. Step 2: The generation unit generates a 3D model based on the data collected by the collection unit. The generation unit uses generative AI to analyze the collected data and generate a 3D model of the site. For example, the generative AI analyzes aerial footage and data from sensors to create detailed 3D models of buildings and terrain. The generation unit can also perform data preprocessing and noise removal. For example, it normalizes the data, fills in missing values, and uses filtering and smoothing techniques to remove noise. Step 3: The animation department creates animations based on the 3D models generated by the generation department. The animation department uses a generation AI to generate animations based on the 3D models. For example, the generation AI creates animations that recreate the circumstances of an incident or the situation at the scene. The animation department can also perform physical simulations. For example, it takes into account the physical properties of the 3D model to recreate realistic movements.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data from the field; a generation unit that generates a 3D model based on the data collected by the collection unit; an animation unit that creates animation based on the 3D model generated by the generation unit; A system characterized by:

2. The collecting unit Collect data on the ground using cameras or sensors or drones 2. The system of claim 1.

3. The generation unit Analyze the collected data and generate a 3D model of the site 2. The system of claim 1.

4. The generation unit Preprocessing and denoising the data 2. The system of claim 1.

5. The animation unit Performing physical simulation based on the generated 3D model 2. The system of claim 1.

6. The animation unit Analyzing motion capture data based on the generated 3D model 2. The system of claim 1.

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

8. The collecting unit Select the appropriate data collection method based on the site environmental conditions 2. The system of claim 1.

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