System

Abstract

An object of the system according to the embodiment is to allow the owner to accurately grasp the progress of the training of the dog and obtain appropriate feedback.SOLUTION: A system according to an embodiment includes a recording and video sharing unit, a behavior analysis unit, and a feedback providing unit. The sound recording and moving image sharing part receives a sound recording and a moving image from the owner. The behavior analysis unit analyzes the audio recording and the video received by the audio recording and video sharing unit to analyze the behavior of the dog. The feedback providing unit provides feedback based on the result analyzed by the behavior analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for owners to accurately grasp their dog's training progress and receive appropriate feedback.

[0005] The system according to the embodiment aims to enable owners to accurately grasp the progress of their dog's training and receive appropriate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording and video sharing unit, a behavior analysis unit, and a feedback providing unit. The recording and video sharing unit receives recordings and videos from the owner. The behavior analysis unit analyzes the recordings and videos received by the recording and video sharing unit to analyze the dog's behavior. The feedback providing unit provides feedback based on the results of the analysis by the behavior analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows owners to accurately grasp the progress of their dog's training and receive appropriate feedback. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) In a training support system according to an embodiment of the present invention, owners share their dog's training progress via audio and video recordings, and a generative AI analyzes the dog's behavior and provides feedback and suggests appropriate training methods and schedules. This enables the training support system to strengthen communication between owners and dogs and support effective training.

[0029] A training support system according to an embodiment includes a recording and video sharing unit, a behavior analysis unit, and a feedback providing unit. The recording and video sharing unit receives recordings and videos from the owner. For example, the owner may record their dog's training session using a smartphone and upload the video. The recording and video sharing unit can receive recordings and videos in various formats, including audio and video file formats, resolutions, and frame rates. The behavior analysis unit analyzes the recordings and videos received by the recording and video sharing unit to analyze the dog's behavior. For example, a generation AI analyzes the video to evaluate how the dog responds to specific commands. The generation AI can analyze behavior based on the type of behavior, an analysis algorithm, and evaluation criteria. The feedback providing unit provides feedback based on the results of the analysis by the behavior analysis unit. For example, the feedback providing unit may provide specific feedback such as, "The dog is responding correctly to the sit command, but is not yet responding sufficiently to the wait command." The feedback providing unit can provide feedback in the form of text, audio, video, real-time feedback, or the like. This allows the training support system to support effective training by allowing owners to share recordings and videos, and the generative AI to analyze behavior and provide feedback.

[0030] The recording and video sharing unit may add a filtering function that removes noise and improves image quality in real time when recording and uploading videos. For example, the recording and video sharing unit may add a filtering function that removes noise in real time when recording and uploading videos. For example, background noise and wind noise may be automatically removed to make dog training audio clearer. The recording and video sharing unit may also improve image quality using techniques such as resolution enhancement, color correction, and frame interpolation. Noise removal is performed using, for example, a filtering algorithm or noise reduction technology. Image quality improvement is performed using, for example, resolution enhancement, color correction, and frame interpolation. The filtering function is provided based on specific content and implementation methods, such as real-time processing or batch processing, and the type of filter to be applied. This improves the quality of recordings and videos, enabling more accurate behavioral analysis.

[0031] The recording and video sharing unit can simultaneously collect not only dog ​​behavior but also environmental sound and background information when recording and sharing videos, enabling more detailed analysis. For example, the recording and video sharing unit adds a function to simultaneously collect not only dog ​​behavior but also environmental sound and background information when recording and sharing videos. For example, it analyzes surrounding sounds and background movement to identify factors affecting a dog's behavior. Environmental sound is collected based on specific types and collection methods, such as background sound, noise, and specific sound sources. Background information is collected based on specific content and collection methods, such as location, time, and weather. This allows for the collection of environmental sound and background information to identify factors affecting a dog's behavior and enable more detailed analysis.

[0032] The audio recording and video sharing unit can add a community function with other pet owners to the audio recording and video sharing platform, enabling them to exchange information and receive advice. The audio recording and video sharing unit, for example, adds a community function with other pet owners to the audio recording and video sharing platform, enabling them to exchange information and receive advice. For example, a forum can be provided where pet owners can share their training progress and concerns and receive advice. The community function can be provided based on specific content and implementation methods, such as a forum, chat, or review system. This allows pet owners to improve the effectiveness of training by exchanging information and receiving advice from other pet owners.

[0033] The recording and video sharing unit can integrate data from different devices when recording and sharing videos, allowing for multifaceted analysis. For example, when recording and sharing videos, the recording and video sharing unit can integrate data from different devices (such as a smartwatch or drone) to perform multifaceted analysis. For example, heart rate data from a smartwatch and aerial footage from a drone can be combined to perform a more detailed analysis of a dog's behavior. Different devices are provided based on the specific type (e.g., smartwatch, drone, sensor) and the data integration method. This allows for more detailed analysis by integrating data from different devices.

[0034] The behavior analysis unit can compare the behavior with past training data when analyzing the behavior and quantitatively evaluate the progress. For example, the behavior analysis unit adds a function to compare the behavior with past training data when analyzing the behavior and quantitatively evaluate the progress. For example, past video data is compared with current video data to evaluate the dog's reaction speed and accuracy. The progress is evaluated based on specific evaluation criteria and methods, such as quantitative indicators, evaluation scores, and comparison methods. This allows the progress to be quantitatively evaluated by comparing with past data.

[0035] The behavior analysis unit can perform a comprehensive evaluation by taking into account the dog's physical condition and health status when analyzing the behavior. For example, the behavior analysis unit adds a function for performing a comprehensive evaluation by taking into account the dog's physical condition and health status when analyzing the behavior. For example, the behavior analysis unit analyzes data on the dog's diet and amount of exercise to evaluate the health status behind the behavior. The physical condition is evaluated based on specific evaluation criteria and methods, such as health check results, behavioral patterns, and vital signs. The health status is evaluated based on specific evaluation criteria and methods, such as medical history, current health status, and medical records. This enables more accurate behavior evaluation by taking into account the dog's physical condition and health status.

[0036] The behavior analysis unit can compare the behavior of different species of animals by referring to the training data of other animals when analyzing behavior. For example, the behavior analysis unit adds a function to compare the behavior of different species of animals by referring to the training data of other animals (such as cats and birds) when analyzing behavior. For example, the behavior of a dog is evaluated based on the training data of cats and birds. The comparison of the behavior of different species of animals is performed based on specific methods and criteria, such as the similarity of behavior patterns and the characteristics of different animals. This makes it possible to compare the behavior of different species of animals by referring to the data of other animals.

[0037] The feedback providing unit can emphasize not only specific areas for improvement but also success stories and positive aspects when providing feedback. For example, the feedback providing unit adds a function to emphasize not only specific areas for improvement but also success stories and positive aspects when providing feedback. For example, examples of successful dog training are emphasized to give the owner confidence. Success stories are provided based on specific content and evaluation criteria, such as past successful experiences and success stories of other owners. Positive aspects are provided based on specific content and evaluation criteria, such as areas for improvement, successful experiences, and positive feedback. In this way, the owner's motivation can be improved by emphasizing success stories and positive aspects.

[0038] The feedback providing unit can provide consistent advice by referring to past feedback history when providing feedback. The feedback providing unit adds a function for providing consistent advice by referring to past feedback history when providing feedback. For example, the feedback providing unit adjusts current feedback based on the content of past feedback. The feedback history is provided based on specific content and management methods, such as the content of past feedback, a method for saving the history, and a search function. This makes it possible to provide consistent advice by referring to the past feedback history.

[0039] The feedback providing unit can provide community-based feedback that takes into account success stories and advice from other pet owners when providing feedback. The feedback providing unit, for example, adds a function to provide community-based feedback that takes into account success stories and advice from other pet owners when providing feedback. For example, the feedback providing unit may share success stories from other pet owners to provide reference for the pet owner. The community-based feedback is provided based on specific content and implementation methods, such as opinions, advice, and sharing of success stories from other pet owners. This allows community-based feedback to be provided by taking into account success stories and advice from other pet owners.

[0040] The feedback providing unit can suggest different training methods and approaches when providing feedback, allowing the owner to select one. For example, the feedback providing unit adds a function to suggest different training methods and approaches when providing feedback, allowing the owner to select one. For example, the feedback providing unit presents multiple training methods, allowing the owner to select the most suitable method. The different training methods are suggested based on specific types and evaluation criteria, such as positive reinforcement, negative reinforcement, and behavior modification techniques. The approaches are suggested based on specific types and evaluation criteria, such as training approach, teaching method, and communication style. In this way, by suggesting different training methods and approaches, the owner can select the most suitable method.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The recording and video sharing unit can automatically evaluate the owner's training environment when recording or sharing videos and suggest the optimal training environment. For example, it can analyze the background sound and lighting conditions of the recording or video and suggest a quiet environment and appropriate lighting conditions. The recording and video sharing unit can also evaluate the size and safety of the training location and suggest the optimal training location. This allows the owner to train in the optimal environment, improving the effectiveness of training.

[0043] The recording and video sharing module can generate graphs and charts that visualize training progress in real time when recording or uploading videos. For example, it can graph a dog's reaction time and success rate, allowing owners to check training progress at a glance. The recording and video sharing module can also compare progress with past training data and indicate areas for improvement. This allows owners to visually grasp training progress and conduct effective training.

[0044] When recording or sharing videos, the recording and video sharing unit can simultaneously analyze not only the dog's behavior, but also the owner's movements and instructions, and suggest areas for improvement. For example, it can analyze the timing and tone of the owner's instructions to suggest more effective ways of giving instructions. It can also analyze the owner's movements and posture to suggest appropriate ways to approach the dog. This can improve the owner's training skills and support more effective training.

[0045] The recording and video sharing section can integrate data from different devices when recording and sharing videos, allowing for a multifaceted evaluation of training progress. For example, it can combine heart rate data from a smartwatch with aerial footage from a drone to analyze a dog's behavior and the owner's stress level. Integrating data from different devices also allows for a comprehensive evaluation of the effectiveness of training and suggestions for improvement. This allows for more detailed training evaluations by utilizing data from different devices.

[0046] When analyzing behavior, the behavior analysis unit can compare it with past training data and quantitatively evaluate progress. For example, it can compare past video data with current video data to evaluate the dog's reaction speed and accuracy. It can also visualize progress in graphs and charts, allowing owners to see the effects of training at a glance. This allows progress to be quantitatively evaluated by comparing it with past data, supporting effective training.

[0047] When analyzing behavior, the behavior analysis unit can also take into account the dog's physical condition and health status to make a comprehensive evaluation. For example, it can analyze data on the dog's diet and exercise volume to evaluate the health status behind the behavior. It can also evaluate the dog's physical condition based on health check results and vital signs, and suggest appropriate timing and methods for training. This enables a comprehensive training evaluation that takes into account the dog's physical condition and health status, supporting more effective training.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The recording and video sharing unit receives recordings and videos from the owner. For example, the owner may record and upload videos of their dog training using a smartphone. The recording and video sharing unit can also receive recordings and videos in various formats, including audio and video file formats, resolutions, and frame rates. Step 2: The behavior analysis unit analyzes the recordings and videos received by the recording and video sharing unit to analyze the dog's behavior. For example, the generation AI can analyze the video to evaluate how the dog responds to a specific command. The generation AI can also analyze the behavior based on the type of behavior, analysis algorithm, and evaluation criteria. Step 3: The feedback providing unit provides feedback based on the results of the analysis by the behavior analysis unit. For example, specific feedback such as "The dog is responding correctly to the sit command, but is not responding sufficiently to the wait command" can be provided. The feedback providing unit can provide feedback in the form of text, audio, video, real-time feedback, etc.

[0050] (Example 2) In a training support system according to an embodiment of the present invention, owners share their dog's training progress via audio and video recordings, and a generative AI analyzes the dog's behavior and provides feedback and suggests appropriate training methods and schedules. This enables the training support system to strengthen communication between owners and dogs and support effective training.

[0051] A training support system according to an embodiment includes a recording and video sharing unit, a behavior analysis unit, and a feedback providing unit. The recording and video sharing unit receives recordings and videos from the owner. For example, the owner may record their dog's training session using a smartphone and upload the video. The recording and video sharing unit can receive recordings and videos in various formats, including audio and video file formats, resolutions, and frame rates. The behavior analysis unit analyzes the recordings and videos received by the recording and video sharing unit to analyze the dog's behavior. For example, a generation AI analyzes the video to evaluate how the dog responds to specific commands. The generation AI can analyze behavior based on the type of behavior, an analysis algorithm, and evaluation criteria. The feedback providing unit provides feedback based on the results of the analysis by the behavior analysis unit. For example, the feedback providing unit may provide specific feedback such as, "The dog is responding correctly to the sit command, but is not yet responding sufficiently to the wait command." The feedback providing unit can provide feedback in the form of text, audio, video, real-time feedback, or the like. This allows the training support system to support effective training by allowing owners to share recordings and videos, and the generative AI to analyze behavior and provide feedback.

[0052] The recording and video sharing unit can provide an interface for estimating the owner's emotions and eliciting positive emotions. For example, the recording and video sharing unit adds a function for estimating emotions by analyzing the owner's facial expressions and tone of voice when recording or sharing videos. For example, when an owner uploads a dog training video, the unit can analyze emotions in real time using a camera or microphone and display a message to elicit positive emotions. The recording and video sharing unit can also estimate emotions using technologies such as voice analysis, facial expression analysis, and text analysis. Positive emotions are evaluated based on specific definitions and criteria, such as joy, satisfaction, and excitement. The interface can be provided in the form of, for example, a user interface, a voice interface, or a graphical interface. This allows the unit to estimate the owner's emotions and elicit positive emotions, thereby improving training motivation.

[0053] The recording and video sharing unit may add a filtering function that removes noise and improves image quality in real time when recording and uploading videos. For example, the recording and video sharing unit may add a filtering function that removes noise in real time when recording and uploading videos. For example, background noise and wind noise may be automatically removed to make dog training audio clearer. The recording and video sharing unit may also improve image quality using techniques such as resolution enhancement, color correction, and frame interpolation. Noise removal is performed using, for example, a filtering algorithm or noise reduction technology. Image quality improvement is performed using, for example, resolution enhancement, color correction, and frame interpolation. The filtering function is provided based on specific content and implementation methods, such as real-time processing or batch processing, and the type of filter to be applied. This improves the quality of recordings and videos, enabling more accurate behavioral analysis.

[0054] The recording and video sharing unit can simultaneously collect not only dog ​​behavior but also environmental sound and background information when recording and sharing videos, enabling more detailed analysis. For example, the recording and video sharing unit adds a function to simultaneously collect not only dog ​​behavior but also environmental sound and background information when recording and sharing videos. For example, it analyzes surrounding sounds and background movement to identify factors affecting a dog's behavior. Environmental sound is collected based on specific types and collection methods, such as background sound, noise, and specific sound sources. Background information is collected based on specific content and collection methods, such as location, time, and weather. This allows for the collection of environmental sound and background information to identify factors affecting a dog's behavior and enable more detailed analysis.

[0055] The audio recording and video sharing unit can add a community function with other pet owners to the audio recording and video sharing platform, enabling them to exchange information and receive advice. The audio recording and video sharing unit, for example, adds a community function with other pet owners to the audio recording and video sharing platform, enabling them to exchange information and receive advice. For example, a forum can be provided where pet owners can share their training progress and concerns and receive advice. The community function can be provided based on specific content and implementation methods, such as a forum, chat, or review system. This allows pet owners to improve the effectiveness of training by exchanging information and receiving advice from other pet owners.

[0056] The recording and video sharing unit can integrate data from different devices when recording and sharing videos, allowing for multifaceted analysis. For example, when recording and sharing videos, the recording and video sharing unit can integrate data from different devices (such as a smartwatch or drone) to perform multifaceted analysis. For example, heart rate data from a smartwatch and aerial footage from a drone can be combined to perform a more detailed analysis of a dog's behavior. Different devices are provided based on the specific type (e.g., smartwatch, drone, sensor) and the data integration method. This allows for more detailed analysis by integrating data from different devices.

[0057] The recording and video sharing unit can use the emotion estimation function when recording and sharing videos to suggest relaxation music to reduce the stress the owner feels during training. For example, when recording and sharing videos, the recording and video sharing unit can use the emotion estimation function to suggest relaxation music to reduce the stress the owner feels during training. For example, the unit can analyze the owner's facial expression and tone of voice and play relaxation music if the owner's stress level is high. The relaxation music is provided based on specific types and selection criteria, such as music genre, tempo, and volume. This can improve the effectiveness of training by reducing the owner's stress.

[0058] When analyzing a dog's behavior, the behavior analysis unit can estimate the dog's emotions and evaluate the behavior based on the emotions. For example, when analyzing a dog's behavior, the behavior analysis unit adds a function to analyze the dog's facial expressions and body movements to estimate emotions. For example, the position of the dog's ears and the movement of its tail can be analyzed to estimate emotions such as joy or anxiety. The dog's emotions are estimated based on specific methods and techniques such as behavior analysis, facial expression analysis, and voice analysis. This allows for more accurate behavior evaluation by taking the dog's emotions into consideration.

[0059] The behavior analysis unit can compare the behavior with past training data when analyzing the behavior and quantitatively evaluate the progress. For example, the behavior analysis unit adds a function to compare the behavior with past training data when analyzing the behavior and quantitatively evaluate the progress. For example, past video data is compared with current video data to evaluate the dog's reaction speed and accuracy. The progress is evaluated based on specific evaluation criteria and methods, such as quantitative indicators, evaluation scores, and comparison methods. This allows the progress to be quantitatively evaluated by comparing with past data.

[0060] The behavior analysis unit can perform a comprehensive evaluation by taking into account the dog's physical condition and health status when analyzing the behavior. For example, the behavior analysis unit adds a function for performing a comprehensive evaluation by taking into account the dog's physical condition and health status when analyzing the behavior. For example, the behavior analysis unit analyzes data on the dog's diet and amount of exercise to evaluate the health status behind the behavior. The physical condition is evaluated based on specific evaluation criteria and methods, such as health check results, behavioral patterns, and vital signs. The health status is evaluated based on specific evaluation criteria and methods, such as medical history, current health status, and medical records. This enables more accurate behavior evaluation by taking into account the dog's physical condition and health status.

[0061] The behavior analysis unit can compare the behavior of different species of animals by referring to the training data of other animals when analyzing behavior. For example, the behavior analysis unit adds a function to compare the behavior of different species of animals by referring to the training data of other animals (such as cats and birds) when analyzing behavior. For example, the behavior of a dog is evaluated based on the training data of cats and birds. The comparison of the behavior of different species of animals is performed based on specific methods and criteria, such as the similarity of behavior patterns and the characteristics of different animals. This makes it possible to compare the behavior of different species of animals by referring to the data of other animals.

[0062] When analyzing the dog's behavior, the behavior analysis unit uses an emotion estimation function to analyze the owner's emotional response to the dog's behavior, thereby improving motivation for training. For example, when analyzing the dog's behavior, the behavior analysis unit adds a function to analyze the owner's emotional response to the dog's behavior using the emotion estimation function. For example, the behavior analysis unit analyzes the owner's facial expressions and tone of voice and provides feedback to improve motivation for training. The emotional response is analyzed based on specific evaluation criteria and methods, such as facial expression analysis, voice analysis, and behavior analysis. Motivation is evaluated based on specific evaluation criteria and methods, such as training continuity, sense of accomplishment, and feedback acceptance. In this way, the owner's emotional response can be analyzed to improve motivation for training.

[0063] The feedback providing unit can estimate the owner's emotions when providing feedback and emphasize positive feedback. For example, the feedback providing unit adds a function of estimating the owner's emotions when providing feedback and emphasizing positive feedback. For example, the feedback providing unit analyzes the owner's facial expressions and tone of voice and displays a message that emphasizes positive feedback. Positive feedback is provided based on specific content and format, such as praise, encouragement, or rewards. This makes it possible to improve motivation for training by taking the owner's emotions into consideration and emphasizing positive feedback.

[0064] The feedback providing unit can emphasize not only specific areas for improvement but also success stories and positive aspects when providing feedback. For example, the feedback providing unit adds a function to emphasize not only specific areas for improvement but also success stories and positive aspects when providing feedback. For example, examples of successful dog training are emphasized to give the owner confidence. Success stories are provided based on specific content and evaluation criteria, such as past successful experiences and success stories of other owners. Positive aspects are provided based on specific content and evaluation criteria, such as areas for improvement, successful experiences, and positive feedback. In this way, the owner's motivation can be improved by emphasizing success stories and positive aspects.

[0065] The feedback providing unit can provide consistent advice by referring to past feedback history when providing feedback. The feedback providing unit adds a function for providing consistent advice by referring to past feedback history when providing feedback. For example, the feedback providing unit adjusts current feedback based on the content of past feedback. The feedback history is provided based on specific content and management methods, such as the content of past feedback, a method for saving the history, and a search function. This makes it possible to provide consistent advice by referring to the past feedback history.

[0066] The feedback providing unit can provide community-based feedback that takes into account success stories and advice from other pet owners when providing feedback. The feedback providing unit, for example, adds a function to provide community-based feedback that takes into account success stories and advice from other pet owners when providing feedback. For example, the feedback providing unit may share success stories from other pet owners to provide reference for the pet owner. The community-based feedback is provided based on specific content and implementation methods, such as opinions, advice, and sharing of success stories from other pet owners. This allows community-based feedback to be provided by taking into account success stories and advice from other pet owners.

[0067] The feedback providing unit can suggest different training methods and approaches when providing feedback, allowing the owner to select one. For example, the feedback providing unit adds a function to suggest different training methods and approaches when providing feedback, allowing the owner to select one. For example, the feedback providing unit presents multiple training methods, allowing the owner to select the most suitable method. The different training methods are suggested based on specific types and evaluation criteria, such as positive reinforcement, negative reinforcement, and behavior modification techniques. The approaches are suggested based on specific types and evaluation criteria, such as training approach, teaching method, and communication style. In this way, by suggesting different training methods and approaches, the owner can select the most suitable method.

[0068] The feedback providing unit can use the emotion estimation function to select and provide the feedback format most acceptable to the owner when providing feedback. The feedback providing unit adds a function to select and provide the feedback format most acceptable to the owner when providing feedback using the emotion estimation function, for example. For example, the feedback providing unit selects a format such as text, audio, or video depending on the emotional state of the owner. The feedback format is provided based on specific types and evaluation criteria, such as text, audio, video, or real-time feedback. This allows the effectiveness of training to be improved by providing feedback in the format most acceptable to the owner.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The recording and video sharing unit can automatically evaluate the owner's training environment when recording or sharing videos and suggest the optimal training environment. For example, it can analyze the background sound and lighting conditions of the recording or video and suggest a quiet environment and appropriate lighting conditions. The recording and video sharing unit can also evaluate the size and safety of the training location and suggest the optimal training location. This allows the owner to train in the optimal environment, improving the effectiveness of training.

[0071] The recording and video sharing unit can estimate the owner's emotions when recording or sharing videos and provide motivational messages according to the progress of training. For example, if the owner feels anxious or stressed about training, an encouraging message can be displayed. Also, if the owner has positive feelings about training, a message encouraging further challenge can be displayed. In this way, by providing appropriate messages according to the owner's emotions, training motivation can be improved.

[0072] The recording and video sharing module can generate graphs and charts that visualize training progress in real time when recording or uploading videos. For example, it can graph a dog's reaction time and success rate, allowing owners to check training progress at a glance. The recording and video sharing module can also compare progress with past training data and indicate areas for improvement. This allows owners to visually grasp training progress and conduct effective training.

[0073] When recording or sharing videos, the recording and video sharing unit can simultaneously analyze not only the dog's behavior, but also the owner's movements and instructions, and suggest areas for improvement. For example, it can analyze the timing and tone of the owner's instructions to suggest more effective ways of giving instructions. It can also analyze the owner's movements and posture to suggest appropriate ways to approach the dog. This can improve the owner's training skills and support more effective training.

[0074] The audio and video sharing section adds a community function with other pet owners to the audio and video sharing platform, and can promote positive interactions using the emotion estimation function. For example, pet owners can share their training progress and concerns with each other and provide positive feedback using the emotion estimation function. Furthermore, encouragement and advice within the community can increase owners' motivation. This can improve the effectiveness of training through positive interactions with other pet owners.

[0075] The recording and video sharing section can integrate data from different devices when recording and sharing videos, allowing for a multifaceted evaluation of training progress. For example, it can combine heart rate data from a smartwatch with aerial footage from a drone to analyze a dog's behavior and the owner's stress level. Integrating data from different devices also allows for a comprehensive evaluation of the effectiveness of training and suggestions for improvement. This allows for more detailed training evaluations by utilizing data from different devices.

[0076] The recording and video sharing unit can use its emotion estimation function to suggest relaxation music to reduce the stress that owners feel during training when recording or sharing videos. For example, it can analyze the owner's facial expressions and tone of voice and play relaxation music if the owner's stress level is high. It can also adjust the genre and tempo of the relaxation music according to the owner's emotional state. This reduces the owner's stress and improves the effectiveness of training.

[0077] When analyzing a dog's behavior, the behavior analysis unit can estimate the dog's emotions and evaluate the behavior based on those emotions. For example, it can analyze the dog's facial expressions and body movements to estimate emotions such as joy or anxiety. It can also evaluate the progress and effectiveness of training based on the dog's emotions and provide appropriate feedback. This makes it possible to evaluate training that takes the dog's emotions into account, thereby supporting more effective training.

[0078] When analyzing behavior, the behavior analysis unit can compare it with past training data and quantitatively evaluate progress. For example, it can compare past video data with current video data to evaluate the dog's reaction speed and accuracy. It can also visualize progress in graphs and charts, allowing owners to see the effects of training at a glance. This allows progress to be quantitatively evaluated by comparing it with past data, supporting effective training.

[0079] When analyzing behavior, the behavior analysis unit can also take into account the dog's physical condition and health status to make a comprehensive evaluation. For example, it can analyze data on the dog's diet and exercise volume to evaluate the health status behind the behavior. It can also evaluate the dog's physical condition based on health check results and vital signs, and suggest appropriate timing and methods for training. This enables a comprehensive training evaluation that takes into account the dog's physical condition and health status, supporting more effective training.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The recording and video sharing unit receives recordings and videos from the owner. For example, the owner may record and upload videos of their dog training using a smartphone. The recording and video sharing unit can also receive recordings and videos in various formats, including audio and video file formats, resolutions, and frame rates. Step 2: The behavior analysis unit analyzes the recordings and videos received by the recording and video sharing unit to analyze the dog's behavior. For example, the generation AI can analyze the video to evaluate how the dog responds to a specific command. The generation AI can also analyze the behavior based on the type of behavior, analysis algorithm, and evaluation criteria. Step 3: The feedback providing unit provides feedback based on the results of the analysis by the behavior analysis unit. For example, specific feedback such as "The dog is responding correctly to the sit command, but is not responding sufficiently to the wait command" can be provided. The feedback providing unit can provide feedback in the form of text, audio, video, real-time feedback, etc.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The 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.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 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 recording and video sharing department that receives recordings and videos from owners; a behavior analysis unit that analyzes the recordings and videos received by the recording and video sharing unit to analyze the behavior of the dog; a feedback providing unit that provides feedback based on the results of the analysis by the behavior analysis unit. A system characterized by:

2. The recording and video sharing unit Add a filtering function that removes noise and improves image quality in real time when recording and uploading the video.

2. The system of claim 1.

3. The recording and video sharing unit Add a community function to the audio and video sharing platform to enable communication and advice with other pet owners.

2. The system of claim 1.

4. The behavior analysis unit When analyzing the behavior of the dog, the emotion of the dog is estimated and the behavior is evaluated based on the emotion.

2. The system of claim 1.

5. The feedback providing unit: When providing feedback, the emotions of the owner are estimated and positive feedback is emphasized. The system of claim 1 .

6. The recording and video sharing unit To provide an interface for estimating the owner's emotions and eliciting positive emotions. The system of claim 1 .

7. The behavior analysis unit When analyzing the behavior, the behavior is compared with past training data to quantitatively evaluate progress.

2. The system of claim 1.

8. The feedback providing unit: When providing feedback, refer to past feedback history to provide consistent advice 2. The system of claim 1.

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