System

Abstract

An object of the system according to the embodiment is to analyze the behavior of a pet and provide an individually optimal training plan.SOLUTION: In one embodiment, a system includes a behavior analyzer, a training plan generator, a training support, and a progress visualizer. The behavior analysis unit analyzes the behavior of the pet. The training plan generation unit generates a training plan based on the behavior data of the companion animal analyzed by the behavior analysis unit. The training support unit supports training based on the training plan generated by the training plan generation unit. The progress visualizer visualizes the progress of the training.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 to efficiently analyze pet behavior and provide individually optimized training plans.

[0005] The system according to the embodiment aims to analyze the behavior of pets and provide an optimal individual training plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior analysis unit, a training plan generation unit, a training support unit, and a progress visualization unit. The behavior analysis unit analyzes the behavior of the pet. The training plan generation unit generates a training plan based on the behavior data of the pet analyzed by the behavior analysis unit. The training support unit supports training based on the training plan generated by the training plan generation unit. The progress visualization unit visualizes the progress of training. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the behavior of a pet and provide an optimal individual training plan. [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) A pet training support system according to an embodiment of the present invention is a system that automatically analyzes pet behavior, generates a training plan using a generation AI, supports training, and visualizes progress. As a result, the pet training support system makes pet training more efficient and makes life with pets more comfortable and enjoyable, especially for busy owners living alone.

[0029] A pet training support system according to an embodiment includes a behavior analysis unit, a training plan generation unit, a training support unit, and a progress visualization unit. The behavior analysis unit analyzes the behavior of a pet. For example, it analyzes the behavior of the pet, the time of day the pet is active, and the environment in which the pet feels stressed. The behavior analysis unit also collects behavioral data of the pet and extracts behavioral patterns. For example, it uses a sensor to monitor the pet's movements in real time and collects behavioral data. Video analysis technology can also be used to analyze the pet's behavior from video data. The training plan generation unit generates a training plan based on the behavioral data of the pet analyzed by the behavior analysis unit. For example, the generation AI generates a training plan tailored to the pet's personality. The generation AI uses a text generation AI (e.g., LLM) to suggest specific training methods for improving the pet's behavior. The generation AI can also use a multimodal generation AI to suggest game-style training that allows the pet to learn while having fun. The training support unit supports training based on the training plan generated by the training plan generation unit. For example, the generation AI suggests to the owner how to respond when the pet exhibits a specific behavior. The generation AI can also suggest training methods and toys and tools that the pet can enjoy. The generation AI adjusts the training support content based on owner feedback. The progress visualization unit visualizes training progress. For example, the generation AI displays, in graphs or charts, how much the pet has progressed in training, which behaviors have improved, and which areas still need improvement. The generation AI visualizes progress based on training implementation data and progress status. As a result, the pet training support system according to the embodiment can streamline pet training and make life with pets more comfortable and enjoyable, especially for busy owners living alone. For example, by accurately understanding pet behavior and providing individually customized training plans, problem behaviors of pets can be effectively improved. Furthermore, visualizing training progress makes it easier for owners to see their pet's growth.

[0030] The behavior analysis unit monitors pet behavior in real time, instantly detecting any abnormal behavior and notifying the owner. For example, the behavior analysis unit uses AI to monitor pet behavior 24 hours a day and sends a notification to the owner's smartphone if it detects abnormal behavior. For example, it issues an alert if the pet behaves in an unusual manner or remains motionless for an extended period of time. The behavior analysis unit also uses AI to learn pet behavior patterns and develop algorithms to detect abnormal behavior. For example, it sends a notification if the pet suddenly starts barking loudly or shows an unusual appetite. The behavior analysis unit also uses AI to analyze pet behavior data and build a system that instantly detects abnormal behavior. For example, it issues an alert if the pet remains motionless during normal activity times or repeatedly behaves abnormally. This allows for immediate detection of abnormal pet behavior and notifies the owner, enabling a rapid response.

[0031] The behavior analysis unit can accumulate pet behavior data over a long period of time and analyze changes in behavior patterns in response to seasonal and environmental changes. For example, the behavior analysis unit uses AI to collect pet behavior data throughout the year and analyze changes in behavior patterns by season. For example, it analyzes differences in activity levels between summer and winter, and seasonal changes in appetite. The behavior analysis unit also accumulates pet behavior data and analyzes changes in behavior patterns in response to environmental changes. For example, it detects changes in behavior due to moving or the introduction of a new pet. The behavior analysis unit also builds a system in which AI collects pet behavior data over a long period of time and analyzes changes in behavior patterns in response to seasonal and environmental changes. For example, it analyzes differences in behavior due to changes in temperature and humidity. By analyzing changes in pet behavior patterns, it becomes possible to respond appropriately to changes in seasons and the environment.

[0032] The behavior analysis unit can compare a pet's behavioral data with that of other pets and promote information sharing with owners of pets with similar behavioral patterns. For example, the behavior analysis unit could build a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, behavioral data of pets of the same breed or age could be shared. The behavior analysis unit could also develop a platform in which AI analyzes pet behavioral data and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could provide forums and communities related to pet behavior. The behavior analysis unit could also develop a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could share advice and experiences related to pet behavior. By promoting information sharing with owners of pets with similar behavioral patterns, this could deepen interactions between owners and provide information useful for training their pets.

[0033] The behavior analysis unit can predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the behavior analysis unit uses AI to analyze the pet's behavioral data and develop an algorithm to predict the pet's health condition. For example, the behavior analysis unit evaluates the pet's health condition based on changes in the pet's activity level and appetite, and suggests the timing of regular health checks. The behavior analysis unit also builds a system using AI to predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the system detects changes in the pet's behavioral patterns and notifies the pet of the need for health checks. The behavior analysis unit also develops a system using AI to analyze a pet's behavioral data and predict the pet's health condition. For example, the system evaluates health risks based on the pet's behavioral data and suggests the timing of regular health checks. This makes it easier to manage a pet's health by predicting the pet's health condition and suggesting the timing of regular health checks.

[0034] The training plan generation unit can continuously update the optimal training plan based on the pet's behavioral data and past training results. The training plan generation unit, for example, builds a system in which the generation AI analyzes the pet's behavioral data and past training results and continuously updates the optimal training plan. For example, the training content is adjusted according to the pet's growth. The training plan generation unit also develops an algorithm in which the generation AI automatically updates the training plan based on the pet's behavioral data and past training results. For example, the training content is changed according to changes in the pet's behavioral patterns. The training plan generation unit also develops a system in which the generation AI analyzes the pet's behavioral data and past training results and continuously updates the optimal training plan. For example, the plan is adjusted according to the pet's training progress. This makes it possible to train in accordance with the pet's growth by continuously updating the optimal training plan.

[0035] The training plan generation unit can automatically adjust the difficulty of training according to the pet's behavioral patterns and provide a plan that matches the pet's growth. The training plan generation unit, for example, constructs a system in which the generation AI analyzes the pet's behavioral patterns and automatically adjusts the difficulty of training. For example, the training content and frequency are changed according to the pet's growth. The training plan generation unit also develops an algorithm in which the generation AI automatically adjusts the difficulty of training based on the pet's behavioral data. For example, once the pet has completed a specific training session, it moves on to the next step. The training plan generation unit also develops a system in which the generation AI automatically adjusts the difficulty of training according to the pet's behavioral patterns. For example, the training content is adjusted according to the pet's reaction and progress. This automatically adjusts the difficulty of training, making it possible to train in accordance with the pet's growth.

[0036] The training plan generation unit can propose a group training plan for joint training with other pets based on the pet's behavioral data. The training plan generation unit, for example, builds a system in which a generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, pets of the same breed or age are divided into groups for training. The training plan generation unit also develops an algorithm in which the generation AI proposes a group training plan based on the pet's behavioral data. For example, groups are formed taking into consideration the compatibility and behavior patterns of the pets. The training plan generation unit also develops a system in which the generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, training content that promotes cooperation and competition between pets is proposed. As a result, by proposing a group training plan for joint training with other pets, interaction between pets deepens and the effectiveness of training improves.

[0037] The training plan generation unit can suggest activities that owners and pets can enjoy together based on pet behavior data. For example, the training plan generation unit builds a system in which the generation AI analyzes pet behavior data and suggests activities that owners and pets can enjoy together. For example, it suggests walks and playtime. The training plan generation unit also develops an algorithm in which the generation AI analyzes pet behavior data and suggests activities that owners and pets can enjoy together. For example, it suggests activities that match the pet's preferences and personality. The training plan generation unit also develops a system in which the generation AI analyzes pet behavior data and suggests activities that owners and pets can enjoy together. For example, it suggests events and classes that owners can participate in together with their pets. This deepens the bond between owners and pets by suggesting activities that owners and pets can enjoy together.

[0038] The training support unit can analyze a pet's reactions during training in real time and provide instant feedback. For example, the training support unit will build a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will notify the pet when it is time to praise it when it behaves correctly. The training support unit will also develop an algorithm in which the generation AI analyzes the pet's reaction data and provides feedback in real time. For example, it will suggest how to correct the pet's incorrect behavior. The training support unit will also develop a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will adjust the training content according to the pet's reaction. This will increase the effectiveness of training by analyzing a pet's reactions in real time and providing instant feedback.

[0039] The training support unit can automatically determine when to move on to the next step based on the training progress. For example, the training support unit builds a system in which the generation AI analyzes the training progress and automatically determines when to move on to the next step. For example, it suggests the next step when the pet completes a specific training. The training support unit also develops an algorithm in which the generation AI automatically determines when to move on to the next step based on the training data. For example, it adjusts the training content according to the pet's progress. The training support unit also develops a system in which the generation AI analyzes the training progress and automatically determines when to move on to the next step. For example, it updates the training plan according to the pet's response and progress. This maximizes the effectiveness of training by automatically determining when to move on to the next step based on the training progress.

[0040] The training support unit can suggest suitable times and locations for training based on pet behavior data. For example, the training support unit builds a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted during the time when the pet is most active. The training support unit also develops an algorithm in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted in a place where the pet can relax. The training support unit also develops a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, the system suggests the optimal training environment based on the pet's behavior patterns. This maximizes the effectiveness of training by suggesting suitable times and locations for training.

[0041] The training support unit can support the selection of tools and toys to use for training based on pet behavior data. For example, the training support unit constructs a system in which a generative AI analyzes pet behavior data and supports the selection of tools and toys to use for training. For example, it suggests toys that suit the pet's preferences and personality. The training support unit also develops an algorithm in which a generative AI analyzes pet behavior data and supports the selection of tools and toys to use for training. For example, it suggests training tools that the pet will enjoy. The training support unit also develops a system in which a generative AI analyzes pet behavior data and supports the selection of tools and toys to use for training. For example, it suggests optimal training tools based on the pet's behavior patterns. This supports the selection of tools and toys to use for training, thereby enhancing the effectiveness of training.

[0042] The progress visualization unit can visualize training progress in real time and provide instant feedback to the owner. For example, the progress visualization unit builds a system in which the generation AI visualizes training progress in real time and provides instant feedback to the owner. For example, it displays a pet's training progress in graphs and charts. The progress visualization unit also develops an algorithm that uses the generation AI to visualize progress in real time based on training data. For example, it visually displays the status of improvement in a pet's behavior. The progress visualization unit also develops a system in which the generation AI visualizes training progress in real time and provides instant feedback to the owner. For example, it displays a pet's training progress on a dashboard. This allows training progress to be visualized in real time and instant feedback to be provided to the owner, thereby improving the effectiveness of training.

[0043] The progress visualization unit can predict a pet's growth based on training progress data and propose future training plans. For example, the progress visualization unit constructs a system in which a generation AI analyzes training progress data and predicts a pet's growth. For example, it proposes a future training plan based on the pet's behavior improvement status. The progress visualization unit also develops an algorithm in which a generation AI predicts a pet's growth based on training data and proposes a future training plan. For example, it adjusts the training content according to the pet's growth. The progress visualization unit also develops a system in which a generation AI analyzes training progress data and predicts a pet's growth. For example, it proposes a future training plan based on changes in the pet's behavior patterns. This makes it possible to predict a pet's growth and propose a future training plan, enabling long-term training planning.

[0044] The progress visualization unit can evaluate the degree of growth of a pet in comparison with other pets based on the training progress data. For example, the generation AI in the progress visualization unit constructs a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the degree of growth is evaluated in comparison with pets of the same breed or age. The progress visualization unit also develops an algorithm that evaluates the degree of growth in comparison with other pets based on the training data. For example, the progress of improvement in a pet's behavior is evaluated in comparison with other pets. The progress visualization unit also develops a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the change in a pet's behavior pattern is evaluated in comparison with other pets. This allows owners to easily feel their pet's growth by evaluating the degree of growth in comparison with other pets.

[0045] The progress visualization unit can visualize changes in a pet's health condition and behavioral patterns based on training progress data. For example, the progress visualization unit constructs a system in which a generation AI analyzes training progress data and visualizes changes in a pet's health condition and behavioral patterns. For example, it displays changes in a pet's activity level and appetite in a graph. The progress visualization unit also develops an algorithm in which the generation AI visualizes changes in a pet's health condition and behavioral patterns based on training data. For example, it visually displays changes in a pet's weight and amount of exercise. The progress visualization unit also develops a system in which a generation AI analyzes training progress data and visualizes changes in a pet's health condition and behavioral patterns. For example, it displays changes in a pet's behavioral patterns in a chart. This makes it easier for owners to manage their pet's health by visualizing changes in a pet's health condition and behavioral patterns.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The behavior analysis unit can predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the behavior analysis unit uses AI to analyze the pet's behavioral data and develop an algorithm to predict the pet's health condition. For example, the behavior analysis unit evaluates the pet's health condition based on changes in the pet's activity level and appetite, and suggests the timing of regular health checks. The behavior analysis unit also builds a system using AI to predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the system detects changes in the pet's behavioral patterns and notifies the pet of the need for health checks. The behavior analysis unit also develops a system using AI to analyze a pet's behavioral data and predict the pet's health condition. For example, the system evaluates health risks based on the pet's behavioral data and suggests the timing of regular health checks. This makes it easier to manage a pet's health by predicting the pet's health condition and suggesting the timing of regular health checks.

[0048] The behavior analysis unit can compare a pet's behavioral data with that of other pets and promote information sharing with owners of pets with similar behavioral patterns. For example, the behavior analysis unit could build a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, behavioral data of pets of the same breed or age could be shared. The behavior analysis unit could also develop a platform in which AI analyzes pet behavioral data and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could provide forums and communities related to pet behavior. The behavior analysis unit could also develop a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could share advice and experiences related to pet behavior. By promoting information sharing with owners of pets with similar behavioral patterns, this could deepen interactions between owners and provide information useful for training their pets.

[0049] The training plan generation unit can propose a group training plan for joint training with other pets based on the pet's behavioral data. The training plan generation unit, for example, builds a system in which a generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, pets of the same breed or age are divided into groups for training. The training plan generation unit also develops an algorithm in which the generation AI proposes a group training plan based on the pet's behavioral data. For example, groups are formed taking into consideration the compatibility and behavior patterns of the pets. The training plan generation unit also develops a system in which the generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, training content that promotes cooperation and competition between pets is proposed. As a result, by proposing a group training plan for joint training with other pets, interaction between pets deepens and the effectiveness of training improves.

[0050] The training support unit can analyze a pet's reactions during training in real time and provide instant feedback. For example, the training support unit will build a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will notify the pet when it is time to praise it when it behaves correctly. The training support unit will also develop an algorithm in which the generation AI analyzes the pet's reaction data and provides feedback in real time. For example, it will suggest how to correct the pet's incorrect behavior. The training support unit will also develop a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will adjust the training content according to the pet's reaction. This will increase the effectiveness of training by analyzing a pet's reactions in real time and providing instant feedback.

[0051] The training support unit can suggest suitable times and locations for training based on pet behavior data. For example, the training support unit builds a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted during the time when the pet is most active. The training support unit also develops an algorithm in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted in a place where the pet can relax. The training support unit also develops a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, the system suggests the optimal training environment based on the pet's behavior patterns. This maximizes the effectiveness of training by suggesting suitable times and locations for training.

[0052] The progress visualization unit can evaluate the degree of growth of a pet in comparison with other pets based on the training progress data. For example, the generation AI in the progress visualization unit constructs a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the degree of growth is evaluated in comparison with pets of the same breed or age. The progress visualization unit also develops an algorithm that evaluates the degree of growth in comparison with other pets based on the training data. For example, the progress of improvement in a pet's behavior is evaluated in comparison with other pets. The progress visualization unit also develops a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the change in a pet's behavior pattern is evaluated in comparison with other pets. This allows owners to easily feel their pet's growth by evaluating the degree of growth in comparison with other pets.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The behavior analysis unit analyzes the behavior of the pet. For example, it analyzes what behaviors the pet exhibits, what time of day the pet is most active, and what kind of environment causes stress. The behavior analysis unit also collects data on the pet's behavior and extracts behavioral patterns. For example, it uses sensors to monitor the pet's movements in real time and collects behavioral data. Video analysis technology can also be used to analyze the pet's behavior from video data. Step 2: The training plan generation unit generates a training plan based on the pet's behavioral data analyzed by the behavior analysis unit. For example, the generation AI generates a training plan tailored to the pet's personality. The generation AI uses text generation AI (e.g., LLM) to suggest specific training methods for improving the pet's behavior. The generation AI can also use multimodal generation AI to suggest game-style training that allows the pet to learn while having fun. Step 3: The training support unit supports training based on the training plan generated by the training plan generation unit. For example, the generation AI suggests to the owner how to respond when the pet exhibits a specific behavior. The generation AI can also suggest how to proceed with training and toys and tools that the pet can enjoy. The generation AI adjusts the training support content based on feedback from the owner. Step 4: The progress visualization unit visualizes the training progress. For example, the generation AI displays in graphs and charts how far the pet has progressed in training, which behaviors have improved, and which areas still need improvement. The generation AI visualizes progress based on the training implementation data and progress status.

[0055] (Example 2) A pet training support system according to an embodiment of the present invention is a system that automatically analyzes pet behavior, generates a training plan using a generation AI, supports training, and visualizes progress. As a result, the pet training support system makes pet training more efficient and makes life with pets more comfortable and enjoyable, especially for busy owners living alone.

[0056] A pet training support system according to an embodiment includes a behavior analysis unit, a training plan generation unit, a training support unit, and a progress visualization unit. The behavior analysis unit analyzes the behavior of a pet. For example, it analyzes the behavior of the pet, the time of day the pet is active, and the environment in which the pet feels stressed. The behavior analysis unit also collects behavioral data of the pet and extracts behavioral patterns. For example, it uses a sensor to monitor the pet's movements in real time and collects behavioral data. Video analysis technology can also be used to analyze the pet's behavior from video data. The training plan generation unit generates a training plan based on the behavioral data of the pet analyzed by the behavior analysis unit. For example, the generation AI generates a training plan tailored to the pet's personality. The generation AI uses a text generation AI (e.g., LLM) to suggest specific training methods for improving the pet's behavior. The generation AI can also use a multimodal generation AI to suggest game-style training that allows the pet to learn while having fun. The training support unit supports training based on the training plan generated by the training plan generation unit. For example, the generation AI suggests to the owner how to respond when the pet exhibits a specific behavior. The generation AI can also suggest training methods and toys and tools that the pet can enjoy. The generation AI adjusts the training support content based on owner feedback. The progress visualization unit visualizes training progress. For example, the generation AI displays, in graphs or charts, how much the pet has progressed in training, which behaviors have improved, and which areas still need improvement. The generation AI visualizes progress based on training implementation data and progress status. As a result, the pet training support system according to the embodiment can streamline pet training and make life with pets more comfortable and enjoyable, especially for busy owners living alone. For example, by accurately understanding pet behavior and providing individually customized training plans, problem behaviors of pets can be effectively improved. Furthermore, visualizing training progress makes it easier for owners to see their pet's growth.

[0057] The behavior analysis unit monitors pet behavior in real time, instantly detecting any abnormal behavior and notifying the owner. For example, the behavior analysis unit uses AI to monitor pet behavior 24 hours a day and sends a notification to the owner's smartphone if it detects abnormal behavior. For example, it issues an alert if the pet behaves in an unusual manner or remains motionless for an extended period of time. The behavior analysis unit also uses AI to learn pet behavior patterns and develop algorithms to detect abnormal behavior. For example, it sends a notification if the pet suddenly starts barking loudly or shows an unusual appetite. The behavior analysis unit also uses AI to analyze pet behavior data and build a system that instantly detects abnormal behavior. For example, it issues an alert if the pet remains motionless during normal activity times or repeatedly behaves abnormally. This allows for immediate detection of abnormal pet behavior and notifies the owner, enabling a rapid response.

[0058] The behavior analysis unit can accumulate pet behavior data over a long period of time and analyze changes in behavior patterns in response to seasonal and environmental changes. For example, the behavior analysis unit uses AI to collect pet behavior data throughout the year and analyze changes in behavior patterns by season. For example, it analyzes differences in activity levels between summer and winter, and seasonal changes in appetite. The behavior analysis unit also accumulates pet behavior data and analyzes changes in behavior patterns in response to environmental changes. For example, it detects changes in behavior due to moving or the introduction of a new pet. The behavior analysis unit also builds a system in which AI collects pet behavior data over a long period of time and analyzes changes in behavior patterns in response to seasonal and environmental changes. For example, it analyzes differences in behavior due to changes in temperature and humidity. By analyzing changes in pet behavior patterns, it becomes possible to respond appropriately to changes in seasons and the environment.

[0059] The behavior analysis unit uses the emotion estimation function to infer the emotional state from a pet's behavior and can suggest appropriate measures if the pet is feeling stressed or anxious. For example, the behavior analysis unit uses the emotion estimation function to develop a system that infers the emotional state from pet behavior data. For example, it suggests a relaxing environment if the pet is feeling stressed. The behavior analysis unit also uses AI to analyze pet behavior data and build an algorithm that infers the emotional state. For example, it suggests toys or music that will comfort a pet if it is feeling anxious. The behavior analysis unit also uses the emotion estimation function to develop a system that infers the emotional state from a pet's behavior and suggests appropriate measures if the pet is feeling stressed or anxious. For example, it provides advice on creating an environment where the pet can relax. In this way, the pet's stress and anxiety are reduced by inferring its emotional state and suggesting appropriate measures.

[0060] The behavior analysis unit can compare a pet's behavioral data with that of other pets and promote information sharing with owners of pets with similar behavioral patterns. For example, the behavior analysis unit could build a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, behavioral data of pets of the same breed or age could be shared. The behavior analysis unit could also develop a platform in which AI analyzes pet behavioral data and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could provide forums and communities related to pet behavior. The behavior analysis unit could also develop a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could share advice and experiences related to pet behavior. By promoting information sharing with owners of pets with similar behavioral patterns, this could deepen interactions between owners and provide information useful for training their pets.

[0061] The behavior analysis unit can predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the behavior analysis unit uses AI to analyze the pet's behavioral data and develop an algorithm to predict the pet's health condition. For example, the behavior analysis unit evaluates the pet's health condition based on changes in the pet's activity level and appetite, and suggests the timing of regular health checks. The behavior analysis unit also builds a system using AI to predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the system detects changes in the pet's behavioral patterns and notifies the pet of the need for health checks. The behavior analysis unit also develops a system using AI to analyze a pet's behavioral data and predict the pet's health condition. For example, the system evaluates health risks based on the pet's behavioral data and suggests the timing of regular health checks. This makes it easier to manage a pet's health by predicting the pet's health condition and suggesting the timing of regular health checks.

[0062] The behavior analysis unit uses the emotion estimation function to analyze the owner's emotional response to their pet's behavior and can suggest stress reduction measures for the owner. For example, the behavior analysis unit uses the emotion estimation function to develop a system that analyzes the owner's emotional response to their pet's behavior. For example, it suggests relaxing activities when the owner is feeling stressed. The behavior analysis unit also uses AI to analyze the pet's behavior data and the owner's emotional response and builds an algorithm that suggests stress reduction measures for the owner. For example, it provides advice on creating an environment where the owner can relax. The behavior analysis unit also uses the emotion estimation function to analyze the owner's emotional response to their pet's behavior and develops a system that suggests stress reduction measures for the owner. For example, it suggests music or aromas that will help the owner relax. In this way, the owner's emotional response is analyzed and stress reduction measures are suggested, thereby reducing stress for the owner.

[0063] The training plan generation unit can continuously update the optimal training plan based on the pet's behavioral data and past training results. The training plan generation unit, for example, builds a system in which the generation AI analyzes the pet's behavioral data and past training results and continuously updates the optimal training plan. For example, the training content is adjusted according to the pet's growth. The training plan generation unit also develops an algorithm in which the generation AI automatically updates the training plan based on the pet's behavioral data and past training results. For example, the training content is changed according to changes in the pet's behavioral patterns. The training plan generation unit also develops a system in which the generation AI analyzes the pet's behavioral data and past training results and continuously updates the optimal training plan. For example, the plan is adjusted according to the pet's training progress. This makes it possible to train in accordance with the pet's growth by continuously updating the optimal training plan.

[0064] The training plan generation unit can automatically adjust the difficulty of training according to the pet's behavioral patterns and provide a plan that matches the pet's growth. The training plan generation unit, for example, constructs a system in which the generation AI analyzes the pet's behavioral patterns and automatically adjusts the difficulty of training. For example, the training content and frequency are changed according to the pet's growth. The training plan generation unit also develops an algorithm in which the generation AI automatically adjusts the difficulty of training based on the pet's behavioral data. For example, once the pet has completed a specific training session, it moves on to the next step. The training plan generation unit also develops a system in which the generation AI automatically adjusts the difficulty of training according to the pet's behavioral patterns. For example, the training content is adjusted according to the pet's reaction and progress. This automatically adjusts the difficulty of training, making it possible to train in accordance with the pet's growth.

[0065] The training plan generation unit can use the emotion estimation function to evaluate the stress and enjoyment felt by a pet during training and generate a training plan based on the emotions. The training plan generation unit, for example, uses the emotion estimation function to develop a system that evaluates the stress and enjoyment felt by a pet during training. For example, the emotional state is estimated from the pet's facial expressions and behavior and reflected in the training plan. The training plan generation unit also constructs an algorithm in which the generation AI analyzes the pet's emotional data and generates a training plan based on the emotions. For example, it suggests training content that the pet will enjoy. The training plan generation unit also uses the emotion estimation function to develop a system that evaluates the stress and enjoyment felt by a pet during training and generates a training plan based on the emotions. For example, it creates a training environment where the pet can relax. In this way, by generating a training plan based on the pet's emotions, the pet can enjoy training.

[0066] The training plan generation unit can propose a group training plan for joint training with other pets based on the pet's behavioral data. The training plan generation unit, for example, builds a system in which a generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, pets of the same breed or age are divided into groups for training. The training plan generation unit also develops an algorithm in which the generation AI proposes a group training plan based on the pet's behavioral data. For example, groups are formed taking into consideration the compatibility and behavior patterns of the pets. The training plan generation unit also develops a system in which the generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, training content that promotes cooperation and competition between pets is proposed. As a result, by proposing a group training plan for joint training with other pets, interaction between pets deepens and the effectiveness of training improves.

[0067] The training plan generation unit can suggest activities that owners and pets can enjoy together based on pet behavior data. For example, the training plan generation unit builds a system in which the generation AI analyzes pet behavior data and suggests activities that owners and pets can enjoy together. For example, it suggests walks and playtime. The training plan generation unit also develops an algorithm in which the generation AI analyzes pet behavior data and suggests activities that owners and pets can enjoy together. For example, it suggests activities that match the pet's preferences and personality. The training plan generation unit also develops a system in which the generation AI analyzes pet behavior data and suggests activities that owners and pets can enjoy together. For example, it suggests events and classes that owners can participate in together with their pets. This deepens the bond between owners and pets by suggesting activities that owners and pets can enjoy together.

[0068] The training plan generation unit uses the emotion estimation function to generate a training plan that takes into account the owner's emotional state, thereby maintaining the owner's motivation. The training plan generation unit, for example, uses the emotion estimation function to develop a system that generates a training plan that takes into account the owner's emotional state. For example, if the owner is feeling stressed, the training plan generation unit suggests training content that will help the owner relax. The training plan generation unit also constructs an algorithm in which the generation AI analyzes the owner's emotional data and generates a training plan based on the owner's emotional state. For example, the training plan generation unit incorporates activities that the owner can enjoy into the training. The training plan generation unit also uses the emotion estimation function to develop a system that generates a training plan that takes into account the owner's emotional state, thereby maintaining the owner's motivation. For example, the training plan generation unit sets goals that will give the owner a sense of accomplishment. In this way, the owner's motivation is maintained by generating a training plan that takes into account the owner's emotional state.

[0069] The training support unit can analyze a pet's reactions during training in real time and provide instant feedback. For example, the training support unit will build a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will notify the pet when it is time to praise it when it behaves correctly. The training support unit will also develop an algorithm in which the generation AI analyzes the pet's reaction data and provides feedback in real time. For example, it will suggest how to correct the pet's incorrect behavior. The training support unit will also develop a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will adjust the training content according to the pet's reaction. This will increase the effectiveness of training by analyzing a pet's reactions in real time and providing instant feedback.

[0070] The training support unit can automatically determine when to move on to the next step based on the training progress. For example, the training support unit builds a system in which the generation AI analyzes the training progress and automatically determines when to move on to the next step. For example, it suggests the next step when the pet completes a specific training. The training support unit also develops an algorithm in which the generation AI automatically determines when to move on to the next step based on the training data. For example, it adjusts the training content according to the pet's progress. The training support unit also develops a system in which the generation AI analyzes the training progress and automatically determines when to move on to the next step. For example, it updates the training plan according to the pet's response and progress. This maximizes the effectiveness of training by automatically determining when to move on to the next step based on the training progress.

[0071] The training support unit uses the emotion estimation function to suggest training methods according to the emotional state of the pet, thereby reducing the pet's stress. The training support unit, for example, uses the emotion estimation function to develop a system that suggests training methods according to the pet's emotional state. For example, if the pet is feeling stressed, it suggests training content that will help the pet relax. The training support unit also constructs an algorithm in which the generative AI analyzes the pet's emotional data and suggests training methods based on the emotional state. For example, it suggests training content that the pet will enjoy. The training support unit also uses the emotion estimation function to suggest training methods according to the pet's emotional state, thereby developing a system that reduces the pet's stress. For example, it creates an environment where the pet can relax. This reduces the pet's stress by suggesting training methods according to the pet's emotional state.

[0072] The training support unit can suggest suitable times and locations for training based on pet behavior data. For example, the training support unit builds a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted during the time when the pet is most active. The training support unit also develops an algorithm in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted in a place where the pet can relax. The training support unit also develops a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, the system suggests the optimal training environment based on the pet's behavior patterns. This maximizes the effectiveness of training by suggesting suitable times and locations for training.

[0073] The training support unit can support the selection of tools and toys to use for training based on pet behavior data. For example, the training support unit constructs a system in which a generative AI analyzes pet behavior data and supports the selection of tools and toys to use for training. For example, it suggests toys that suit the pet's preferences and personality. The training support unit also develops an algorithm in which a generative AI analyzes pet behavior data and supports the selection of tools and toys to use for training. For example, it suggests training tools that the pet will enjoy. The training support unit also develops a system in which a generative AI analyzes pet behavior data and supports the selection of tools and toys to use for training. For example, it suggests optimal training tools based on the pet's behavior patterns. This supports the selection of tools and toys to use for training, thereby enhancing the effectiveness of training.

[0074] The training support unit uses the emotion estimation function to suggest training support that takes into account the owner's emotional state, thereby reducing the owner's stress. For example, the training support unit uses the emotion estimation function to develop a system that suggests training support that takes into account the owner's emotional state. For example, it suggests a training method that helps the owner relax when they are feeling stressed. The training support unit also uses the generative AI to analyze the owner's emotional data and build an algorithm that suggests training support based on the owner's emotional state. For example, it suggests training content that the owner can enjoy. The training support unit also uses the emotion estimation function to suggest training support that takes into account the owner's emotional state, thereby developing a system that reduces the owner's stress. For example, it creates an environment where the owner can relax. In this way, the training support that takes into account the owner's emotional state is suggested, thereby reducing the owner's stress.

[0075] The progress visualization unit can visualize training progress in real time and provide instant feedback to the owner. For example, the progress visualization unit builds a system in which the generation AI visualizes training progress in real time and provides instant feedback to the owner. For example, it displays a pet's training progress in graphs and charts. The progress visualization unit also develops an algorithm that uses the generation AI to visualize progress in real time based on training data. For example, it visually displays the status of improvement in a pet's behavior. The progress visualization unit also develops a system in which the generation AI visualizes training progress in real time and provides instant feedback to the owner. For example, it displays a pet's training progress on a dashboard. This allows training progress to be visualized in real time and instant feedback to be provided to the owner, thereby improving the effectiveness of training.

[0076] The progress visualization unit can predict a pet's growth based on training progress data and propose future training plans. For example, the progress visualization unit constructs a system in which a generation AI analyzes training progress data and predicts a pet's growth. For example, it proposes a future training plan based on the pet's behavior improvement status. The progress visualization unit also develops an algorithm in which a generation AI predicts a pet's growth based on training data and proposes a future training plan. For example, it adjusts the training content according to the pet's growth. The progress visualization unit also develops a system in which a generation AI analyzes training progress data and predicts a pet's growth. For example, it proposes a future training plan based on changes in the pet's behavior patterns. This makes it possible to predict a pet's growth and propose a future training plan, enabling long-term training planning.

[0077] The progress visualization unit uses the emotion estimation function to visualize the emotional state of a pet and evaluate the effectiveness of training from an emotional perspective. The progress visualization unit, for example, uses the emotion estimation function to develop a system that visualizes the emotional state of a pet. For example, the emotional state is estimated from the pet's facial expressions and behavior, and the effectiveness of training is evaluated. The progress visualization unit also constructs an algorithm in which the generation AI analyzes the pet's emotional data and visualizes the emotional state. For example, the pet's emotional state is displayed in a graph or chart. The progress visualization unit also uses the emotion estimation function to develop a system that visualizes the pet's emotional state and evaluates the effectiveness of training from an emotional perspective. For example, the effectiveness of training is evaluated based on the pet's emotional state. In this way, the quality of training is improved by visualizing the pet's emotional state and evaluating the effectiveness of training from an emotional perspective.

[0078] The progress visualization unit can evaluate the degree of growth of a pet in comparison with other pets based on the training progress data. For example, the generation AI in the progress visualization unit constructs a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the degree of growth is evaluated in comparison with pets of the same breed or age. The progress visualization unit also develops an algorithm that evaluates the degree of growth in comparison with other pets based on the training data. For example, the progress of improvement in a pet's behavior is evaluated in comparison with other pets. The progress visualization unit also develops a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the change in a pet's behavior pattern is evaluated in comparison with other pets. This allows owners to easily feel their pet's growth by evaluating the degree of growth in comparison with other pets.

[0079] The progress visualization unit can visualize changes in a pet's health condition and behavioral patterns based on training progress data. For example, the progress visualization unit constructs a system in which a generation AI analyzes training progress data and visualizes changes in a pet's health condition and behavioral patterns. For example, it displays changes in a pet's activity level and appetite in a graph. The progress visualization unit also develops an algorithm in which the generation AI visualizes changes in a pet's health condition and behavioral patterns based on training data. For example, it visually displays changes in a pet's weight and amount of exercise. The progress visualization unit also develops a system in which a generation AI analyzes training progress data and visualizes changes in a pet's health condition and behavioral patterns. For example, it displays changes in a pet's behavioral patterns in a chart. This makes it easier for owners to manage their pet's health by visualizing changes in a pet's health condition and behavioral patterns.

[0080] The progress visualization unit uses the emotion estimation function to visualize the owner's emotional state and evaluate the owner's satisfaction with the training progress. The progress visualization unit, for example, uses the emotion estimation function to develop a system that visualizes the owner's emotional state. For example, the emotional state is estimated from the owner's facial expressions and behavior, and the satisfaction with the training progress is evaluated. The progress visualization unit also uses a generation AI to analyze the owner's emotional data and build an algorithm that visualizes the emotional state. For example, the owner's emotional state is displayed in a graph or chart. The progress visualization unit also uses the emotion estimation function to develop a system that visualizes the owner's emotional state and evaluates the owner's satisfaction with the training progress. For example, the effectiveness of the training is evaluated based on the owner's emotional state. In this way, the owner's emotional state is visualized and the satisfaction with the training progress is evaluated, thereby maintaining the owner's motivation.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The behavior analysis unit can predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the behavior analysis unit uses AI to analyze the pet's behavioral data and develop an algorithm to predict the pet's health condition. For example, the behavior analysis unit evaluates the pet's health condition based on changes in the pet's activity level and appetite, and suggests the timing of regular health checks. The behavior analysis unit also builds a system using AI to predict a pet's health condition based on the pet's behavioral data and suggest the timing of regular health checks. For example, the system detects changes in the pet's behavioral patterns and notifies the pet of the need for health checks. The behavior analysis unit also develops a system using AI to analyze a pet's behavioral data and predict the pet's health condition. For example, the system evaluates health risks based on the pet's behavioral data and suggests the timing of regular health checks. This makes it easier to manage a pet's health by predicting the pet's health condition and suggesting the timing of regular health checks.

[0083] The behavior analysis unit can compare a pet's behavioral data with that of other pets and promote information sharing with owners of pets with similar behavioral patterns. For example, the behavior analysis unit could build a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, behavioral data of pets of the same breed or age could be shared. The behavior analysis unit could also develop a platform in which AI analyzes pet behavioral data and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could provide forums and communities related to pet behavior. The behavior analysis unit could also develop a system in which AI compares a pet's behavioral data with that of other pets and promotes information sharing with owners of pets with similar behavioral patterns. For example, it could share advice and experiences related to pet behavior. By promoting information sharing with owners of pets with similar behavioral patterns, this could deepen interactions between owners and provide information useful for training their pets.

[0084] The behavior analysis unit uses the emotion estimation function to infer the emotional state from a pet's behavior and can suggest appropriate measures if the pet is feeling stressed or anxious. For example, the behavior analysis unit uses the emotion estimation function to develop a system that infers the emotional state from pet behavior data. For example, it suggests a relaxing environment if the pet is feeling stressed. The behavior analysis unit also uses AI to analyze pet behavior data and build an algorithm that infers the emotional state. For example, it suggests toys or music that will comfort a pet if it is feeling anxious. The behavior analysis unit also uses the emotion estimation function to develop a system that infers the emotional state from a pet's behavior and suggests appropriate measures if the pet is feeling stressed or anxious. For example, it provides advice on creating an environment where the pet can relax. In this way, the pet's stress and anxiety are reduced by inferring its emotional state and suggesting appropriate measures.

[0085] The training plan generation unit can propose a group training plan for joint training with other pets based on the pet's behavioral data. The training plan generation unit, for example, builds a system in which a generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, pets of the same breed or age are divided into groups for training. The training plan generation unit also develops an algorithm in which the generation AI proposes a group training plan based on the pet's behavioral data. For example, groups are formed taking into consideration the compatibility and behavior patterns of the pets. The training plan generation unit also develops a system in which the generation AI analyzes the pet's behavioral data and proposes a group training plan for joint training with other pets. For example, training content that promotes cooperation and competition between pets is proposed. As a result, by proposing a group training plan for joint training with other pets, interaction between pets deepens and the effectiveness of training improves.

[0086] The training plan generation unit can use the emotion estimation function to evaluate the stress and enjoyment felt by a pet during training and generate a training plan based on the emotions. The training plan generation unit, for example, uses the emotion estimation function to develop a system that evaluates the stress and enjoyment felt by a pet during training. For example, the emotional state is estimated from the pet's facial expressions and behavior and reflected in the training plan. The training plan generation unit also constructs an algorithm in which the generation AI analyzes the pet's emotional data and generates a training plan based on the emotions. For example, it suggests training content that the pet will enjoy. The training plan generation unit also uses the emotion estimation function to develop a system that evaluates the stress and enjoyment felt by a pet during training and generates a training plan based on the emotions. For example, it creates a training environment where the pet can relax. In this way, by generating a training plan based on the pet's emotions, the pet can enjoy training.

[0087] The training support unit can analyze a pet's reactions during training in real time and provide instant feedback. For example, the training support unit will build a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will notify the pet when it is time to praise it when it behaves correctly. The training support unit will also develop an algorithm in which the generation AI analyzes the pet's reaction data and provides feedback in real time. For example, it will suggest how to correct the pet's incorrect behavior. The training support unit will also develop a system in which the generation AI analyzes a pet's reactions during training in real time and provides instant feedback. For example, it will adjust the training content according to the pet's reaction. This will increase the effectiveness of training by analyzing a pet's reactions in real time and providing instant feedback.

[0088] The training support unit uses the emotion estimation function to suggest training methods according to the emotional state of the pet, thereby reducing the pet's stress. The training support unit, for example, uses the emotion estimation function to develop a system that suggests training methods according to the pet's emotional state. For example, if the pet is feeling stressed, it suggests training content that will help the pet relax. The training support unit also constructs an algorithm in which the generative AI analyzes the pet's emotional data and suggests training methods based on the emotional state. For example, it suggests training content that the pet will enjoy. The training support unit also uses the emotion estimation function to suggest training methods according to the pet's emotional state, thereby developing a system that reduces the pet's stress. For example, it creates an environment where the pet can relax. This reduces the pet's stress by suggesting training methods according to the pet's emotional state.

[0089] The training support unit can suggest suitable times and locations for training based on pet behavior data. For example, the training support unit builds a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted during the time when the pet is most active. The training support unit also develops an algorithm in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, training is conducted in a place where the pet can relax. The training support unit also develops a system in which a generation AI analyzes pet behavior data and suggests suitable times and locations for training. For example, the system suggests the optimal training environment based on the pet's behavior patterns. This maximizes the effectiveness of training by suggesting suitable times and locations for training.

[0090] The progress visualization unit uses the emotion estimation function to visualize the emotional state of a pet and evaluate the effectiveness of training from an emotional perspective. The progress visualization unit, for example, uses the emotion estimation function to develop a system that visualizes the emotional state of a pet. For example, the emotional state is estimated from the pet's facial expressions and behavior, and the effectiveness of training is evaluated. The progress visualization unit also constructs an algorithm in which the generation AI analyzes the pet's emotional data and visualizes the emotional state. For example, the pet's emotional state is displayed in a graph or chart. The progress visualization unit also uses the emotion estimation function to develop a system that visualizes the pet's emotional state and evaluates the effectiveness of training from an emotional perspective. For example, the effectiveness of training is evaluated based on the pet's emotional state. In this way, the quality of training is improved by visualizing the pet's emotional state and evaluating the effectiveness of training from an emotional perspective.

[0091] The progress visualization unit can evaluate the degree of growth of a pet in comparison with other pets based on the training progress data. For example, the generation AI in the progress visualization unit constructs a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the degree of growth is evaluated in comparison with pets of the same breed or age. The progress visualization unit also develops an algorithm that evaluates the degree of growth in comparison with other pets based on the training data. For example, the progress of improvement in a pet's behavior is evaluated in comparison with other pets. The progress visualization unit also develops a system that analyzes the training progress data and evaluates the degree of growth of a pet in comparison with other pets. For example, the change in a pet's behavior pattern is evaluated in comparison with other pets. This allows owners to easily feel their pet's growth by evaluating the degree of growth in comparison with other pets.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The behavior analysis unit analyzes the behavior of the pet. For example, it analyzes what behaviors the pet exhibits, what time of day the pet is most active, and what kind of environment causes stress. The behavior analysis unit also collects data on the pet's behavior and extracts behavioral patterns. For example, it uses sensors to monitor the pet's movements in real time and collects behavioral data. Video analysis technology can also be used to analyze the pet's behavior from video data. Step 2: The training plan generation unit generates a training plan based on the pet's behavioral data analyzed by the behavior analysis unit. For example, the generation AI generates a training plan tailored to the pet's personality. The generation AI uses text generation AI (e.g., LLM) to suggest specific training methods for improving the pet's behavior. The generation AI can also use multimodal generation AI to suggest game-style training that allows the pet to learn while having fun. Step 3: The training support unit supports training based on the training plan generated by the training plan generation unit. For example, the generation AI suggests to the owner how to respond when the pet exhibits a specific behavior. The generation AI can also suggest how to proceed with training and toys and tools that the pet can enjoy. The generation AI adjusts the training support content based on feedback from the owner. Step 4: The progress visualization unit visualizes the training progress. For example, the generation AI displays in graphs and charts how far the pet has progressed in training, which behaviors have improved, and which areas still need improvement. The generation AI visualizes progress based on the training implementation data and progress status.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 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.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] 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.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 behavior analysis department that analyzes pet behavior, a training plan generation unit that generates a training plan based on the behavioral data of the pet analyzed by the behavior analysis unit; a training support unit that supports training based on the training plan generated by the training plan generation unit; a progress visualization unit that visualizes the progress of the training. A system characterized by:

2. The behavior analysis unit Inferring the pet's emotional state from its behavior and suggesting appropriate measures if the pet is feeling stressed or anxious 2. The system of claim 1.

3. The training plan generation unit The optimal training plan is continuously updated based on the pet's behavior data and past training results.

2. The system of claim 1.

4. The training support unit Analyzing the pet's reactions during the training in real time and providing immediate feedback 2. The system of claim 1.

5. The progress visualization unit Visualizing the progress of the training in real time and providing immediate feedback to the owner.

2. The system of claim 1.

Images