System

Abstract

An object of the system according to the embodiment is to learn and predict a behavior pattern of a dog.SOLUTION: A system includes an action data collection part, a learning part, and an action prediction part. The behavioral data collector collects behavioral data of the dog. The learning unit learns a behavior pattern of the dog based on the data collected by the behavior data collection unit. The behavior prediction unit predicts the behavior of the dog based on the behavior pattern learned by the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to accurately understand and predict a dog's behavior patterns.

[0005] The system according to the embodiment aims to learn and predict the behavioral patterns of dogs. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral data collection unit, a learning unit, and a behavior prediction unit. The behavioral data collection unit collects behavioral data of the dog. The learning unit learns the behavioral patterns of the dog based on the data collected by the behavioral data collection unit. The behavior prediction unit predicts the behavior of the dog based on the behavioral patterns learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment is able to learn and predict the behavioral patterns of dogs. [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) The dog behavior prediction system according to an embodiment of the present invention is a system that collects dog behavior data, uses AI to learn, and predicts dog behavior, thereby enabling owners to optimize their dog's exercise, play, toilet breaks, etc.

[0029] A dog behavior prediction system according to an embodiment includes a behavioral data collection unit, a learning unit, and a behavior prediction unit. The behavioral data collection unit collects behavioral data of the dog. For example, the behavioral data collection unit measures the dog's activity level using a sensor and collects the data. The behavioral data collection unit can also record the dog's meal times and toilet frequency. The behavioral data collection unit can also collect data manually entered by the owner. The learning unit learns the dog's behavioral patterns based on the data collected by the behavioral data collection unit. For example, the learning unit analyzes the dog's behavioral patterns using a machine learning algorithm. The learning unit can also learn more complex behavioral patterns using deep learning. The learning unit can also predict future behavioral patterns based on past data. The behavior prediction unit predicts the dog's behavior based on the behavioral patterns learned by the learning unit. For example, the behavior prediction unit predicts that the dog will be active at a specific time of day. The behavior prediction unit can also predict that the dog prefers a specific type of play. The behavior prediction unit can also predict when the dog will want to go to the toilet. As a result, the dog behavior prediction system according to the embodiment allows owners to optimize their dog's exercise, play, toilet breaks, and the like. For example, owners can plan walks for times when their dog is most active. Owners can also reduce stress for their dogs by providing play activities that their dogs enjoy. Owners can also predict when their dogs will need to go to the toilet and take toilet breaks at appropriate times.

[0030] The behavioral data collection unit collects not only the behavioral data of the dog but also the behavioral data of the owner, and can learn behavioral patterns based on mutual influence. For example, the behavioral data collection unit simultaneously collects the behavioral data of the dog and the behavioral data of the owner and analyzes the mutual influence. For example, it learns the relationship between the time of day the owner is at home and the dog's activity level, and takes into account the influence of the owner's behavior on the dog. This makes it possible to learn behavioral patterns that take into account the influence of the owner's behavior on the dog.

[0031] When learning the dog's behavioral patterns, the behavioral data collection unit can make more accurate predictions based on changes in seasons and weather. The behavioral data collection unit, for example, collects data on seasons and weather and reflects it in the dog's behavioral patterns. For example, the behavioral data collection unit learns seasonal behavioral patterns, taking into account that the dog's activity level differs between summer and winter. This enables highly accurate behavioral predictions that take into account changes in seasons and weather.

[0032] The behavioral data collection unit can apply the learning of dog behavioral patterns to other pets to predict the behavior of different animal species. For example, the behavioral data collection unit applies the dog behavioral pattern learning system to cats and birds to collect behavioral data of different animal species. For example, it analyzes the activity times of cats and the singing patterns of birds to predict their behavior. This makes it possible to predict the behavior of different animal species.

[0033] The behavioral data collection unit can work in conjunction with the owner's smart home devices to learn behavioral patterns based on environmental data within the home. The behavioral data collection unit, for example, works in conjunction with the owner's smart home devices to collect environmental data within the home. For example, it learns the dog's behavioral patterns based on room temperature and lighting conditions. This makes it possible to learn behavioral patterns that take into account environmental data within the home.

[0034] The behavior prediction unit can provide the owner with specific behavior suggestions based on the predicted behavior of the dog. For example, the behavior prediction unit can suggest an optimal walking route for the owner based on the predicted behavior of the dog. For example, an appropriate walking route is provided based on the time of day when the dog is most active. This makes it possible to provide the owner with specific behavior suggestions.

[0035] The behavior prediction unit can collect dog behavior data in real time to improve the accuracy of behavior prediction and continuously update the prediction model. The behavior prediction unit, for example, builds a system that collects dog behavior data in real time and continuously updates the prediction model. For example, the behavior prediction unit analyzes the dog's activity level and meal times in real time and performs behavior prediction. This allows behavior data to be collected in real time and the prediction model to be continuously updated.

[0036] The behavior prediction unit can notify the owner of the behavior prediction result via a smartphone app, allowing the owner to respond immediately. The behavior prediction unit can, for example, build a system that notifies the owner of the behavior prediction result via a smartphone app, allowing the owner to respond immediately. For example, a notification can be sent at times when the dog needs exercise. This allows the owner to be notified of the behavior prediction result immediately.

[0037] The behavior prediction unit can provide comprehensive pet care by coordinating the behavior prediction with other pet care services. The behavior prediction unit, for example, shares the results of the behavior prediction with pet sitters and trainers to build a system that provides comprehensive pet care. For example, dog exercise and training schedules can be shared. This allows comprehensive pet care to be provided in cooperation with other pet care services.

[0038] The behavior prediction unit can monitor the dog's weight and health condition in real time and adjust the exercise plan based on that. The behavior prediction unit, for example, builds a system that monitors the dog's weight and health condition in real time and adjusts the exercise plan based on that data. For example, if the dog's weight increases, the intensity of the exercise can be increased. This makes it possible to monitor the dog's weight and health condition in real time and adjust the exercise plan.

[0039] The behavior prediction unit can combine games and exercises that dogs like and provide a plan that allows them to maintain their health while having fun. The behavior prediction unit, for example, builds a system that provides an exercise plan that allows dogs to maintain their health while having fun, based on the games and exercises that dogs like. For example, it suggests exercises that incorporate ball games, which dogs like. This makes it possible to provide an exercise plan that allows dogs to maintain their health while having fun.

[0040] The behavior prediction unit can also apply the exercise optimization to other pets, allowing health management of different animal species. For example, the behavior prediction unit can apply the dog exercise optimization system to cats and birds, collecting health data for different animal species. For example, the behavior prediction unit can analyze the play time of cats and the flight patterns of birds to manage their health. This makes it possible to manage the health of different animal species.

[0041] The behavior prediction unit can link the exercise plan with the owner's fitness plan and provide a plan that allows the owner and dog to exercise together. For example, the behavior prediction unit can build a system that links with the owner's fitness plan and provides an exercise plan that allows the owner and dog to exercise together. For example, the behavior prediction unit can provide a plan to take the dog with the owner on a jog. This makes it possible to provide an exercise plan that allows the owner and dog to exercise together.

[0042] The behavior prediction unit can suggest the intensity and type of play depending on the age and physical condition of the dog. The behavior prediction unit builds a system that suggests the intensity and type of play based on, for example, the age and physical condition of the dog. For example, it suggests lighter play for older dogs and more active play for younger dogs. This makes it possible to suggest the intensity and type of play depending on the age and physical condition of the dog.

[0043] The behavior prediction unit makes suggestions to encourage the owner's participation, thereby deepening the bond between the owner and the dog. The behavior prediction unit, for example, builds a system that makes suggestions for play that encourage the owner's participation. For example, it suggests games and activities that the owner and the dog can enjoy together. This makes suggestions that encourage the owner's participation, thereby deepening the bond between the owner and the dog.

[0044] The behavior prediction unit can apply play optimization to other pets and suggest play activities for different animal species. For example, the behavior prediction unit applies the dog play optimization system to cats and birds to collect play data for different animal species. For example, the behavior prediction unit analyzes the play time of cats and the flight patterns of birds to suggest play activities. This makes it possible to suggest play activities for different animal species.

[0045] The behavior prediction unit can notify the owner of play suggestions via a smartphone app, allowing the owner to respond immediately. The behavior prediction unit, for example, builds a system that notifies a smartphone app of play suggestions, allowing the owner to respond immediately. For example, it sends a notification during the time period when the dog needs to play. This allows the owner to be notified of play suggestions immediately.

[0046] The behavior prediction unit monitors the dog's eating and drinking patterns and can predict the timing of a toilet break based on the patterns. The behavior prediction unit, for example, monitors the dog's eating and drinking patterns in real time and builds a system that predicts the timing of toilet breaks based on the data. For example, it predicts the timing of a dog going to the toilet after drinking water. This makes it possible to monitor the dog's eating and drinking patterns and predict the timing of a toilet break.

[0047] The behavior prediction unit can take the dog's health condition into consideration and suggest toilet breaks at appropriate times. The behavior prediction unit, for example, monitors the dog's health condition in real time and builds a system that suggests the timing of toilet breaks based on that data. For example, it predicts the timing when the dog will want to go to the toilet based on its health condition. This makes it possible to take the dog's health condition into consideration and suggest toilet breaks at appropriate times.

[0048] The behavior prediction unit can also apply the toilet break optimization to other pets, allowing for toilet management for different animal species. For example, the behavior prediction unit can apply the dog toilet break optimization system to cats and birds, collecting toilet data for different animal species. For example, the behavior prediction unit can analyze cat toilet time and bird excretion patterns to perform toilet management. This allows for toilet management for different animal species.

[0049] The behavior prediction unit can notify the owner's smartphone app of the toilet break suggestion, allowing the owner to respond immediately. The behavior prediction unit, for example, builds a system that notifies the smartphone app of the toilet break suggestion, allowing the owner to respond immediately. For example, it sends a notification at the time when the dog wants to go to the toilet. This allows the owner to be notified of the toilet break suggestion immediately.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The behavioral data collection unit can learn behavioral patterns by collecting not only the dog's behavioral data but also the surrounding sound environment data. For example, the behavioral data collection unit records and learns how the dog reacts to specific sounds. This makes it possible to predict how the dog will behave in response to specific sounds. The behavioral data collection unit can also learn the dog's behavioral patterns when listening to music and suggest relaxing music. Furthermore, the behavioral data collection unit can learn how the dog reacts to noise and suggest noise control measures.

[0052] The behavioral data collection unit can collect not only the dog's behavioral data but also its health data and learn its behavioral patterns. For example, the behavioral data collection unit monitors the dog's body temperature and heart rate and learns its behavioral patterns based on its health condition. This makes it possible to predict its behavior while taking into account its health condition. The behavioral data collection unit can also record the dog's diet and calorie intake to manage its health. Furthermore, the behavioral data collection unit can learn the relationship between the dog's exercise volume and its health condition and suggest an appropriate exercise plan.

[0053] In addition to the dog's behavioral data, the behavioral data collection unit can also collect data on the dog's social interactions and learn behavioral patterns. For example, the behavioral data collection unit records and learns how the dog interacts with other dogs and people. This allows it to predict how the dog will behave in a particular situation. The behavioral data collection unit can also learn how the dog adapts to a new environment and provide support for adaptation. Furthermore, the behavioral data collection unit can identify situations that cause stress to the dog and suggest stress reduction measures.

[0054] The behavioral data collection unit can collect not only the dog's behavioral data but also the dog's sleep data and learn its behavioral patterns. For example, the behavioral data collection unit monitors and learns the dog's sleep duration and sleep quality. This makes it possible to predict the dog's behavior taking into account the dog's sleep patterns. The behavioral data collection unit can also learn what kind of environment the dog sleeps well in and suggest the optimal sleeping environment. Furthermore, the behavioral data collection unit can learn the impact of lack of sleep on the dog's behavior and suggest appropriate rest.

[0055] The behavioral data collection unit can collect not only the dog's behavioral data but also the dog's dietary data and learn the dog's behavioral patterns. For example, the behavioral data collection unit records and learns the dog's preferred foods. This makes it possible to predict the dog's behavior taking into account the dog's dietary patterns. The behavioral data collection unit can also monitor the dog's meal times and food amounts and propose an appropriate meal plan. Furthermore, the behavioral data collection unit can learn the relationship between the dog's diet and behavior and predict changes in behavior due to diet.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The behavioral data collection unit collects behavioral data of the dog. For example, the behavioral data collection unit may measure the dog's activity level using a sensor and collect data. The behavioral data collection unit may also record the dog's meal times and toilet frequency. Furthermore, the behavioral data collection unit may also collect data manually entered by the owner. Step 2: The learning unit learns the dog's behavioral patterns based on the data collected by the behavioral data collection unit. For example, the learning unit uses a machine learning algorithm to analyze the dog's behavioral patterns. The learning unit can also use deep learning to learn more complex behavioral patterns. Furthermore, the learning unit can predict future behavioral patterns based on past data. Step 3: The behavior prediction unit predicts the dog's behavior based on the behavioral patterns learned by the learning unit. For example, the behavior prediction unit predicts that the dog will be active at a specific time of day. The behavior prediction unit can also predict that the dog prefers a specific type of play. Furthermore, the behavior prediction unit can also predict when the dog will want to go to the toilet.

[0058] (Example 2) The dog behavior prediction system according to an embodiment of the present invention is a system that collects dog behavior data, uses AI to learn, and predicts dog behavior, thereby enabling owners to optimize their dog's exercise, play, toilet breaks, etc.

[0059] A dog behavior prediction system according to an embodiment includes a behavioral data collection unit, a learning unit, and a behavior prediction unit. The behavioral data collection unit collects behavioral data of the dog. For example, the behavioral data collection unit measures the dog's activity level using a sensor and collects the data. The behavioral data collection unit can also record the dog's meal times and toilet frequency. The behavioral data collection unit can also collect data manually entered by the owner. The learning unit learns the dog's behavioral patterns based on the data collected by the behavioral data collection unit. For example, the learning unit analyzes the dog's behavioral patterns using a machine learning algorithm. The learning unit can also learn more complex behavioral patterns using deep learning. The learning unit can also predict future behavioral patterns based on past data. The behavior prediction unit predicts the dog's behavior based on the behavioral patterns learned by the learning unit. For example, the behavior prediction unit predicts that the dog will be active at a specific time of day. The behavior prediction unit can also predict that the dog prefers a specific type of play. The behavior prediction unit can also predict when the dog will want to go to the toilet. As a result, the dog behavior prediction system according to the embodiment allows owners to optimize their dog's exercise, play, toilet breaks, and the like. For example, owners can plan walks for times when their dog is most active. Owners can also reduce stress for their dogs by providing play activities that their dogs enjoy. Owners can also predict when their dogs will need to go to the toilet and take toilet breaks at appropriate times.

[0060] The behavioral data collection unit collects not only the behavioral data of the dog but also the behavioral data of the owner, and can learn behavioral patterns based on mutual influence. For example, the behavioral data collection unit simultaneously collects the behavioral data of the dog and the behavioral data of the owner and analyzes the mutual influence. For example, it learns the relationship between the time of day the owner is at home and the dog's activity level, and takes into account the influence of the owner's behavior on the dog. This makes it possible to learn behavioral patterns that take into account the influence of the owner's behavior on the dog.

[0061] When learning the dog's behavioral patterns, the behavioral data collection unit can make more accurate predictions based on changes in seasons and weather. The behavioral data collection unit, for example, collects data on seasons and weather and reflects it in the dog's behavioral patterns. For example, the behavioral data collection unit learns seasonal behavioral patterns, taking into account that the dog's activity level differs between summer and winter. This enables highly accurate behavioral predictions that take into account changes in seasons and weather.

[0062] The behavioral data collection unit can use the emotion estimation function to estimate the emotional state of the dog and learn behavioral patterns based on that emotion. The behavioral data collection unit estimates emotions from the dog's facial expressions and behavior, for example, and learns behavioral patterns based on that data. For example, it analyzes the behavioral patterns of a dog when it is happy and when it is anxious, and predicts behavior based on emotions. This makes it possible to learn behavioral patterns that take the dog's emotional state into consideration.

[0063] The behavioral data collection unit can apply the learning of dog behavioral patterns to other pets to predict the behavior of different animal species. For example, the behavioral data collection unit applies the dog behavioral pattern learning system to cats and birds to collect behavioral data of different animal species. For example, it analyzes the activity times of cats and the singing patterns of birds to predict their behavior. This makes it possible to predict the behavior of different animal species.

[0064] The behavioral data collection unit can work in conjunction with the owner's smart home devices to learn behavioral patterns based on environmental data within the home. The behavioral data collection unit, for example, works in conjunction with the owner's smart home devices to collect environmental data within the home. For example, it learns the dog's behavioral patterns based on room temperature and lighting conditions. This makes it possible to learn behavioral patterns that take into account environmental data within the home.

[0065] The behavioral data collection unit can use the emotion estimation function to learn the dog's behavioral patterns based on the owner's emotional state and optimize the interaction. The behavioral data collection unit, for example, estimates the owner's emotional state and learns the dog's behavioral patterns based on that data. For example, it analyzes the dog's behavioral patterns when the owner is stressed and when the owner is relaxed, and optimizes the interaction. This makes it possible to learn the dog's behavioral patterns taking the owner's emotional state into consideration.

[0066] The behavior prediction unit can provide the owner with specific behavior suggestions based on the predicted behavior of the dog. For example, the behavior prediction unit can suggest an optimal walking route for the owner based on the predicted behavior of the dog. For example, an appropriate walking route is provided based on the time of day when the dog is most active. This makes it possible to provide the owner with specific behavior suggestions.

[0067] The behavior prediction unit can collect dog behavior data in real time to improve the accuracy of behavior prediction and continuously update the prediction model. The behavior prediction unit, for example, builds a system that collects dog behavior data in real time and continuously updates the prediction model. For example, the behavior prediction unit analyzes the dog's activity level and meal times in real time and performs behavior prediction. This allows behavior data to be collected in real time and the prediction model to be continuously updated.

[0068] The behavior prediction unit uses the emotion estimation function to provide behavior predictions that take into account the emotional state of the dog and can make suggestions to reduce stress in the dog. The behavior prediction unit, for example, uses the emotion estimation function to analyze the emotional state of the dog in real time and predicts behavior based on that data. For example, it suggests actions that will help the dog relax when it is feeling stressed. This makes it possible to predict behavior that takes into account the emotional state of the dog, thereby reducing stress.

[0069] The behavior prediction unit can notify the owner of the behavior prediction result via a smartphone app, allowing the owner to respond immediately. The behavior prediction unit can, for example, build a system that notifies the owner of the behavior prediction result via a smartphone app, allowing the owner to respond immediately. For example, a notification can be sent at times when the dog needs exercise. This allows the owner to be notified of the behavior prediction result immediately.

[0070] The behavior prediction unit can provide comprehensive pet care by coordinating the behavior prediction with other pet care services. The behavior prediction unit, for example, shares the results of the behavior prediction with pet sitters and trainers to build a system that provides comprehensive pet care. For example, dog exercise and training schedules can be shared. This allows comprehensive pet care to be provided in cooperation with other pet care services.

[0071] The behavior prediction unit uses the emotion estimation function to provide behavior predictions based on the owner's emotional state and can make suggestions to reduce the owner's stress. The behavior prediction unit, for example, uses the emotion estimation function to analyze the owner's emotional state in real time and predicts behavior based on that data. For example, it suggests actions that will help the owner relax when they are feeling stressed. This makes it possible to predict behavior based on the owner's emotional state and reduce the owner's stress.

[0072] The behavior prediction unit can monitor the dog's weight and health condition in real time and adjust the exercise plan based on that. The behavior prediction unit, for example, builds a system that monitors the dog's weight and health condition in real time and adjusts the exercise plan based on that data. For example, if the dog's weight increases, the intensity of the exercise can be increased. This makes it possible to monitor the dog's weight and health condition in real time and adjust the exercise plan.

[0073] The behavior prediction unit can combine games and exercises that dogs like and provide a plan that allows them to maintain their health while having fun. The behavior prediction unit, for example, builds a system that provides an exercise plan that allows dogs to maintain their health while having fun, based on the games and exercises that dogs like. For example, it suggests exercises that incorporate ball games, which dogs like. This makes it possible to provide an exercise plan that allows dogs to maintain their health while having fun.

[0074] The behavior prediction unit uses the emotion estimation function to provide an exercise plan that takes into account the dog's emotional state, thereby increasing the dog's motivation. For example, the behavior prediction unit uses the emotion estimation function to analyze the dog's emotional state in real time and provide an exercise plan based on that data. For example, when the dog is excited, the unit suggests exercises that will help the dog release energy. This allows the dog to be provided with an exercise plan that takes into account the dog's emotional state, thereby increasing its motivation.

[0075] The behavior prediction unit can also apply the exercise optimization to other pets, allowing health management of different animal species. For example, the behavior prediction unit can apply the dog exercise optimization system to cats and birds, collecting health data for different animal species. For example, the behavior prediction unit can analyze the play time of cats and the flight patterns of birds to manage their health. This makes it possible to manage the health of different animal species.

[0076] The behavior prediction unit can link the exercise plan with the owner's fitness plan and provide a plan that allows the owner and dog to exercise together. For example, the behavior prediction unit can build a system that links with the owner's fitness plan and provides an exercise plan that allows the owner and dog to exercise together. For example, the behavior prediction unit can provide a plan to take the dog with the owner on a jog. This makes it possible to provide an exercise plan that allows the owner and dog to exercise together.

[0077] The behavior prediction unit uses the emotion estimation function to provide an exercise plan that corresponds to the owner's emotional state, thereby deepening the bond between the owner and the dog. For example, the behavior prediction unit uses the emotion estimation function to analyze the owner's emotional state in real time and provide an exercise plan based on that data. For example, it suggests exercises that the owner can enjoy together with their dog when they are relaxed. This provides an exercise plan that corresponds to the owner's emotional state, thereby deepening the bond between the owner and the dog.

[0078] The behavior prediction unit can suggest the intensity and type of play depending on the age and physical condition of the dog. The behavior prediction unit builds a system that suggests the intensity and type of play based on, for example, the age and physical condition of the dog. For example, it suggests lighter play for older dogs and more active play for younger dogs. This makes it possible to suggest the intensity and type of play depending on the age and physical condition of the dog.

[0079] The behavior prediction unit makes suggestions to encourage the owner's participation, thereby deepening the bond between the owner and the dog. The behavior prediction unit, for example, builds a system that makes suggestions for play that encourage the owner's participation. For example, it suggests games and activities that the owner and the dog can enjoy together. This makes suggestions that encourage the owner's participation, thereby deepening the bond between the owner and the dog.

[0080] The behavior prediction unit uses the emotion estimation function to suggest games that take into account the emotional state of the dog, thereby reducing the dog's stress. The behavior prediction unit, for example, uses the emotion estimation function to analyze the emotional state of the dog in real time and suggests games based on that data. For example, it suggests games that will help the dog relax when it is feeling stressed. This allows the dog to be suggested games that take into account the emotional state of the dog, thereby reducing stress.

[0081] The behavior prediction unit can apply play optimization to other pets and suggest play activities for different animal species. For example, the behavior prediction unit applies the dog play optimization system to cats and birds to collect play data for different animal species. For example, the behavior prediction unit analyzes the play time of cats and the flight patterns of birds to suggest play activities. This makes it possible to suggest play activities for different animal species.

[0082] The behavior prediction unit can notify the owner of play suggestions via a smartphone app, allowing the owner to respond immediately. The behavior prediction unit, for example, builds a system that notifies a smartphone app of play suggestions, allowing the owner to respond immediately. For example, it sends a notification during the time period when the dog needs to play. This allows the owner to be notified of play suggestions immediately.

[0083] The behavior prediction unit uses the emotion estimation function to suggest games that correspond to the owner's emotional state, thereby deepening the bond between the owner and the dog. For example, the behavior prediction unit uses the emotion estimation function to analyze the owner's emotional state in real time and suggest games based on that data. For example, it suggests games that the owner can enjoy together with the dog when they are relaxed. This makes it possible to suggest games that correspond to the owner's emotional state and deepen the bond between the owner and the dog.

[0084] The behavior prediction unit monitors the dog's eating and drinking patterns and can predict the timing of a toilet break based on the patterns. The behavior prediction unit, for example, monitors the dog's eating and drinking patterns in real time and builds a system that predicts the timing of toilet breaks based on the data. For example, it predicts the timing of a dog going to the toilet after drinking water. This makes it possible to monitor the dog's eating and drinking patterns and predict the timing of a toilet break.

[0085] The behavior prediction unit can take the dog's health condition into consideration and suggest toilet breaks at appropriate times. The behavior prediction unit, for example, monitors the dog's health condition in real time and builds a system that suggests the timing of toilet breaks based on that data. For example, it predicts the timing when the dog will want to go to the toilet based on its health condition. This makes it possible to take the dog's health condition into consideration and suggest toilet breaks at appropriate times.

[0086] The behavior prediction unit uses the emotion estimation function to suggest toilet breaks that take into account the emotional state of the dog, thereby maintaining the dog's comfort. The behavior prediction unit, for example, uses the emotion estimation function to analyze the emotional state of the dog in real time and suggest toilet breaks based on that data. For example, it suggests toilet breaks that allow the dog to relax when it is feeling stressed. This allows the dog's comfort to be maintained by suggesting toilet breaks that take into account the emotional state of the dog.

[0087] The behavior prediction unit can also apply the toilet break optimization to other pets, allowing for toilet management for different animal species. For example, the behavior prediction unit can apply the dog toilet break optimization system to cats and birds, collecting toilet data for different animal species. For example, the behavior prediction unit can analyze cat toilet time and bird excretion patterns to perform toilet management. This allows for toilet management for different animal species.

[0088] The behavior prediction unit can notify the owner's smartphone app of the toilet break suggestion, allowing the owner to respond immediately. The behavior prediction unit, for example, builds a system that notifies the smartphone app of the toilet break suggestion, allowing the owner to respond immediately. For example, it sends a notification at the time when the dog wants to go to the toilet. This allows the owner to be notified of the toilet break suggestion immediately.

[0089] The behavior prediction unit uses the emotion estimation function to suggest a toilet break according to the owner's emotional state, thereby maintaining the comfort of both the owner and the dog. The behavior prediction unit, for example, uses the emotion estimation function to analyze the owner's emotional state in real time and suggest a toilet break based on that data. For example, the behavior prediction unit suggests taking a toilet break with the dog when the owner is relaxed. This allows the behavior prediction unit to suggest a toilet break according to the owner's emotional state, thereby maintaining the comfort of both the owner and the dog.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The behavioral data collection unit can learn behavioral patterns by collecting not only the dog's behavioral data but also the surrounding sound environment data. For example, the behavioral data collection unit records and learns how the dog reacts to specific sounds. This makes it possible to predict how the dog will behave in response to specific sounds. The behavioral data collection unit can also learn the dog's behavioral patterns when listening to music and suggest relaxing music. Furthermore, the behavioral data collection unit can learn how the dog reacts to noise and suggest noise control measures.

[0092] The behavioral data collection unit can collect not only the dog's behavioral data but also its health data and learn its behavioral patterns. For example, the behavioral data collection unit monitors the dog's body temperature and heart rate and learns its behavioral patterns based on its health condition. This makes it possible to predict its behavior while taking into account its health condition. The behavioral data collection unit can also record the dog's diet and calorie intake to manage its health. Furthermore, the behavioral data collection unit can learn the relationship between the dog's exercise volume and its health condition and suggest an appropriate exercise plan.

[0093] In addition to the dog's behavioral data, the behavioral data collection unit can also collect data on the dog's social interactions and learn behavioral patterns. For example, the behavioral data collection unit records and learns how the dog interacts with other dogs and people. This allows it to predict how the dog will behave in a particular situation. The behavioral data collection unit can also learn how the dog adapts to a new environment and provide support for adaptation. Furthermore, the behavioral data collection unit can identify situations that cause stress to the dog and suggest stress reduction measures.

[0094] The behavioral data collection unit can collect not only the dog's behavioral data but also the dog's sleep data and learn its behavioral patterns. For example, the behavioral data collection unit monitors and learns the dog's sleep duration and sleep quality. This makes it possible to predict the dog's behavior taking into account the dog's sleep patterns. The behavioral data collection unit can also learn what kind of environment the dog sleeps well in and suggest the optimal sleeping environment. Furthermore, the behavioral data collection unit can learn the impact of lack of sleep on the dog's behavior and suggest appropriate rest.

[0095] The behavioral data collection unit can collect not only the dog's behavioral data but also the dog's dietary data and learn the dog's behavioral patterns. For example, the behavioral data collection unit records and learns the dog's preferred foods. This makes it possible to predict the dog's behavior taking into account the dog's dietary patterns. The behavioral data collection unit can also monitor the dog's meal times and food amounts and propose an appropriate meal plan. Furthermore, the behavioral data collection unit can learn the relationship between the dog's diet and behavior and predict changes in behavior due to diet.

[0096] The behavior prediction unit uses the emotion estimation function to provide behavior predictions that take into account the emotional state of the dog and can make suggestions to reduce the dog's anxiety. For example, the behavior prediction unit suggests actions that will help the dog relax when it is feeling anxious. This makes it possible to predict behaviors that take into account the emotional state of the dog and reduce anxiety. The behavior prediction unit can also suggest an environment where the dog can feel safe. Furthermore, the behavior prediction unit can identify situations in which the dog feels stressed and suggest stress reduction measures.

[0097] The behavior prediction unit uses the emotion estimation function to provide behavior predictions that take into account the emotional state of the dog and can make suggestions to control the dog's excitement. For example, the behavior prediction unit suggests actions to calm the dog when it is excited. This makes it possible to predict behaviors that take into account the dog's emotional state and control excitement. The behavior prediction unit can also identify situations in which the dog is likely to become excited and suggest preventive measures. Furthermore, the behavior prediction unit can also suggest environments in which the dog can relax.

[0098] The behavior prediction unit uses the emotion estimation function to provide behavior prediction that takes into account the emotional state of the dog, and can make suggestions to amplify the dog's joy. For example, the behavior prediction unit suggests behavior that is even more enjoyable when the dog is happy. This makes it possible to predict behavior that takes into account the emotional state of the dog, amplifying its joy. The behavior prediction unit can also suggest games that the dog likes. Furthermore, the behavior prediction unit can also suggest environments that the dog finds enjoyable.

[0099] The behavior prediction unit uses the emotion estimation function to provide behavior prediction that takes into account the emotional state of the dog, and can make suggestions to reduce the dog's sense of loneliness. For example, the behavior prediction unit suggests activities that can be done together when the dog feels lonely. This makes it possible to predict behavior that takes into account the emotional state of the dog, thereby reducing the dog's sense of loneliness. The behavior prediction unit can also suggest an environment where the dog can feel safe. Furthermore, the behavior prediction unit can also suggest opportunities for the dog to interact with other dogs and people.

[0100] The behavior prediction unit uses the emotion estimation function to provide behavior prediction that takes into account the emotional state of the dog, and can make suggestions to reduce the dog's fatigue. For example, the behavior prediction unit suggests behavior that allows the dog to rest when it is tired. This makes it possible to predict behavior that takes into account the emotional state of the dog, thereby reducing fatigue. The behavior prediction unit can also suggest an environment where the dog can relax. Furthermore, the behavior prediction unit can also suggest an exercise plan that will prevent the dog from getting tired.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The behavioral data collection unit collects behavioral data of the dog. For example, the behavioral data collection unit may measure the dog's activity level using a sensor and collect data. The behavioral data collection unit may also record the dog's meal times and toilet frequency. Furthermore, the behavioral data collection unit may also collect data manually entered by the owner. Step 2: The learning unit learns the dog's behavioral patterns based on the data collected by the behavioral data collection unit. For example, the learning unit uses a machine learning algorithm to analyze the dog's behavioral patterns. The learning unit can also use deep learning to learn more complex behavioral patterns. Furthermore, the learning unit can predict future behavioral patterns based on past data. Step 3: The behavior prediction unit predicts the dog's behavior based on the behavioral patterns learned by the learning unit. For example, the behavior prediction unit predicts that the dog will be active at a specific time of day. The behavior prediction unit can also predict that the dog prefers a specific type of play. Furthermore, the behavior prediction unit can also predict when the dog will want to go to the toilet.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 department that collects dog behavior data, a learning unit that learns the behavioral patterns of dogs based on the data collected by the behavioral data collecting unit; a behavior prediction unit that predicts the behavior of the dog based on the behavior pattern learned by the learning unit. A system characterized by:

2. The behavioral data collection unit In addition to the behavioral data of the dog, behavioral data of the owner is also collected, and behavioral patterns based on mutual influence are learned.

2. The system of claim 1.

3. The behavioral data collection unit Applying the learning of dog behavior patterns to other pets to predict the behavior of different animal species 2. The system of claim 1.

4. The behavior prediction unit Providing specific behavioral suggestions to the owner based on the behavior prediction of the dog 2. The system of claim 1.

5. The behavior prediction unit Providing behavior predictions that take into account the emotional state of the dog and making suggestions to reduce stress for the dog.

2. The system of claim 1.

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