system

Abstract

The system according to this embodiment aims to notify the appropriate timing for praise and to visualize the progress of training. [Solution] The system according to the embodiment comprises a notification unit, a display unit, a voice analysis unit, and a recording unit. The notification unit notifies the appropriate timing for haptic praise through wearable device linkage. The display unit displays training results and behavior success rates in a graph. The voice analysis unit analyzes the pronunciation and tone of commands and supports areas for improvement. The recording unit records command success rates and areas for improvement, visualizing growth.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there are problems that the timing and method of giving praise are unknown, and it is difficult to confirm the progress of training.

[0005] The system according to the embodiment aims to notify the appropriate timing of giving praise and visualize the progress of training.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a notification unit, a display unit, a voice analysis unit, and a recording unit. The notification unit notifies the user of the appropriate timing for haptic praise through a wearable device connection. The display unit displays training results and behavior success rates in graph form. The voice analysis unit analyzes the pronunciation and tone of commands to support areas for improvement. The recording unit records command success rates and areas for improvement to visualize progress. [Effects of the Invention]

[0007] The system according to this embodiment can notify the appropriate timing for praise and visualize the progress of training. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The smart dog trainer AI agent system according to an embodiment of the present invention is a system that efficiently supports dog training. This system includes wearable device connectivity, a training visualization unit, a voice analysis unit and guidance unit, and a growth record unit. Conventional dog training has had challenges such as difficulty in timing praise, insufficient monitoring of training progress, and accuracy of command pronunciation. To solve these problems, the present invention includes a notification unit that notifies the appropriate timing for praise using haptics through wearable device connectivity. This allows owners to provide feedback at an effective time. Next, it includes a display unit that uses a training visualization unit to display training results and behavior success rates in graph form. This allows owners to check training progress at a glance and maintain motivation. Furthermore, the voice analysis unit and guidance unit analyze the pronunciation and tone of commands and support areas for improvement. This allows for improved communication with dogs through correct voice commands. Finally, it includes a growth record unit that records command success rates and areas for improvement, visualizing growth. This makes it easier to feel the effects of training. For example, the Smart Dog Trainer AI Agent System caters to a wide range of users, from novice owners to professional trainers, and can be used in pet shops, veterinary clinics, and other similar establishments. The Smart Dog Trainer AI Agent integrates voice analysis and behavioral data to create a personalized feedback system, efficiently supporting dog training. This leads to better training for dogs and a stronger bond of trust between owners and their pets. In short, the Smart Dog Trainer AI Agent System efficiently supports dog training and improves communication with owners.

[0029] The smart dog trainer AI agent system according to this embodiment comprises a notification unit, a display unit, a voice analysis unit, and a recording unit. The notification unit notifies the owner of the appropriate timing to praise the dog using haptics through a wearable device connection. For example, the notification unit notifies the owner with vibration or sound the moment the dog performs the correct action. The notification unit can also notify the owner of the appropriate timing to praise the dog based on the results analyzed by the voice analysis unit. For example, the notification unit notifies the owner at the appropriate time after the voice analysis unit analyzes the pronunciation and tone of the command. Furthermore, the notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit reduces the frequency of notifications and only notifies at important times. The display unit displays training results and behavior success rates in graphs. For example, the display unit displays success rates and reaction speeds in graphs so that the progress of training can be visually confirmed. The display unit can also estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, the display unit reduces visual stress by using calming colors when the user is feeling anxious. The voice analysis unit analyzes the pronunciation and tone of commands and supports areas for improvement. For example, the voice analysis unit analyzes the accuracy and appropriateness of the pronunciation and tone of commands and provides feedback on areas for improvement. The voice analysis unit can also estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is feeling anxious, the voice analysis unit will relax the voice analysis criteria to reduce stress. The recording unit records the success rate of commands and areas for improvement, visualizing progress. For example, the recording unit quantifies training results and behavior success rates and displays them in graphs. The recording unit can also estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is feeling stressed, the recording unit will provide a simpler recording method. As a result, the smart dog trainer AI agent system according to this embodiment can efficiently support dog training and improve communication with the owner.

[0030] The notification unit, in conjunction with wearable devices, uses haptics to notify owners of the appropriate timing for praising their dogs. Specifically, the notification unit vibrates or sounds to notify the owner the moment the dog performs the correct behavior. For example, when a dog obeys a command such as "sit" or "stay," the notification unit sends a vibration to the owner's wearable device, immediately indicating the right time to praise. This allows the owner to praise the dog in a timely manner, improving the effectiveness of training. The notification unit can also notify owners of the appropriate timing for praise based on the results analyzed by the voice analysis unit. For example, the voice analysis unit analyzes the pronunciation and tone of the owner's commands, and if the dog responds correctly, the notification unit vibrates or sounds to notify the owner. Furthermore, the notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions of the user. For example, if the user is feeling stressed, the notification unit reduces the frequency of notifications and only notifies at important times. This reduces user stress and improves the quality of training. The notification unit uses AI to analyze the user's voice and behavior patterns to estimate their emotions. For example, it analyzes the user's voice tone, speaking speed, and body movements to estimate emotions such as stress, tension, and joy. This allows the notification unit to provide appropriate notifications based on the user's emotional state. Furthermore, the notification unit can continuously improve the accuracy and timing of notifications based on user feedback. For example, when a user provides feedback on the timing and content of notifications, the notification unit learns from that information and adjusts to provide more appropriate notifications. This allows the notification unit to respond flexibly to user needs and maximizes the effectiveness of training.

[0031] The display unit shows training results and behavior success rates in graphs. Specifically, the display unit shows success rates and reaction speeds in graphs so that training progress can be visually confirmed. For example, it can display the success rate of a dog following commands such as "sit" or "stay" in line graphs or bar graphs, allowing owners to grasp training results at a glance. The display unit can also estimate the user's emotions and adjust the display method based on the estimated emotions of the user. For example, if the user is tense, the display unit will use calming colors to reduce visual stress. This allows the user to check training progress in a relaxed state. The display unit uses AI to estimate the user's emotions and adjust the display content. For example, it analyzes the user's facial expressions, voice, and behavior patterns to estimate emotions. If the user is happy, it will display bright colors and positive messages to increase the user's motivation. On the other hand, if the user is stressed, it will display calming colors and relaxing messages to reduce the user's stress. Furthermore, the display unit analyzes training results in detail and provides feedback to the user. For example, the system can display changes in success rate and reaction speed for specific commands over time, showing which training methods were effective. This allows users to objectively evaluate the effectiveness of their training and use this information to plan future training sessions. The display unit can also continuously improve its content based on user feedback. For instance, users can provide feedback on the display content and format, allowing the unit to learn from this information and adjust the display to be more user-friendly and easier to understand. This enables the display unit to respond flexibly to user needs, maximizing the effectiveness of training.

[0032] The voice analysis unit analyzes the pronunciation and tone of commands to support improvements. Specifically, the voice analysis unit analyzes the accuracy and appropriateness of the pronunciation and tone of commands and provides feedback on areas for improvement. For example, when an owner gives the command "sit," the unit analyzes whether the pronunciation is accurate and the tone is appropriate, and suggests areas for improvement as needed. This allows the owner to give more effective commands and improve the effectiveness of dog training. The voice analysis unit can also estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is nervous, the voice analysis unit will relax the voice analysis criteria to reduce stress. This allows the user to train in a relaxed state. The voice analysis unit uses AI to analyze the user's voice and estimate emotions. For example, it analyzes the tone, speaking speed, and volume of the user's voice to estimate emotions such as stress, tension, and joy. This allows the voice analysis unit to provide appropriate feedback according to the user's emotional state. Furthermore, the voice analysis unit can continuously improve the accuracy and standards of its voice analysis based on user feedback. For example, when a user provides feedback on the results of the voice analysis, the voice analysis unit learns from that information and adjusts to perform more accurate analysis. This allows the voice analysis unit to respond flexibly to user needs and maximize the effectiveness of its training.

[0033] The recording unit tracks command success rates and areas for improvement, visualizing progress. Specifically, it quantifies training results and behavioral success rates, displaying them in graphs. For example, it can display the success rate of a dog following commands like "sit" or "stay" using line or bar graphs, allowing owners to grasp training results at a glance. The recording unit can also estimate the user's emotions and adjust the recording method based on that estimation. For example, if the user is feeling stressed, the recording unit provides a concise recording method. This allows the user to record training results without feeling stressed. The recording unit uses AI to estimate the user's emotions and adjust the recording content. For example, it analyzes the user's facial expressions, voice, and behavioral patterns to estimate emotions. If the user is happy, it provides detailed records and positive messages to increase user motivation. On the other hand, if the user is stressed, it provides concise records and relaxing messages to reduce user stress. Furthermore, the recording unit analyzes training results in detail and provides feedback to the user. For example, the system can display changes in success rate and reaction speed for specific commands over time, showing which training methods were effective. This allows users to objectively evaluate the effectiveness of their training and use this information to plan future training sessions. The recording unit can also continuously improve its recordings based on user feedback. For instance, when users provide feedback on the content and format of their recordings, the unit learns from this information and adjusts its recordings to be more user-friendly and easier to understand. This allows the recording unit to respond flexibly to user needs and maximize the effectiveness of training.

[0034] The notification unit can notify the owner of the appropriate timing for praise based on the results analyzed by the voice analysis unit. For example, the notification unit can use the voice analysis unit to analyze the pronunciation and tone of commands and notify the owner at the appropriate time. For example, the notification unit can notify the owner with vibration or sound the moment the dog performs the correct behavior. The notification unit can also estimate the user's emotions and adjust the timing of notifications based on the estimated emotions of the user. For example, if the user is feeling stressed, the notification unit will reduce the frequency of notifications and only notify at important times. This enhances the effectiveness of training by providing notifications at the appropriate time based on the voice analysis results.

[0035] The display unit can show training results and behavioral success rates in graphs. For example, the display unit can show success rates and reaction speeds in graphs so that training progress can be visually confirmed. For example, the display unit can quantify training results and display them in graphs. The display unit can also estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, the display unit will use calming colors to reduce visual stress. This allows users to visually confirm their training progress and maintain motivation.

[0036] The voice analysis unit can analyze the pronunciation and tone of commands and provide support for improvement. For example, it can analyze the accuracy and appropriateness of command pronunciation and provide feedback on areas for improvement. For example, it can analyze the user's pronunciation and provide guidance on correct pronunciation methods. Furthermore, the voice analysis unit can estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is tense, the voice analysis unit will relax the voice analysis criteria to reduce stress. This can improve communication with the dog through correct voice commands.

[0037] The recording unit can record command success rates and areas for improvement, making growth visible. For example, it can quantify training results and behavioral success rates and display them in graphs. It can also record training progress and visualize growth. Furthermore, the recording unit can estimate the user's emotions and adjust the recording method based on those emotions. For example, if the user is feeling stressed, the recording unit can provide a simpler recording method. This makes it easier for the user to feel the effects of the training.

[0038] The notification unit can select the optimal notification method by referring to the dog's behavioral history when issuing a notification. For example, the notification unit can issue notifications that encourage similar behaviors based on the dog's past successful behavioral patterns. For example, the notification unit can issue notifications with a different approach to avoid behavioral patterns that the dog has previously failed at. The notification unit can also analyze the dog's behavioral history and select the most effective notification method. This enhances the effectiveness of training by selecting the optimal notification method based on the dog's behavioral history.

[0039] The notification unit can customize the content of notifications based on the dog's current activity level. For example, if the dog is playing, it will send a short notification to avoid interrupting play. If the dog is resting, it will send a quiet notification to avoid disturbing rest. If the dog is training, it will send a detailed notification to help the dog concentrate on training. By customizing notification content according to the dog's current activity level, the effectiveness of training is enhanced.

[0040] The notification unit can prioritize highly relevant notifications by considering the user's geographical location. For example, if the user is at home, it will prioritize notifications related to training. If the user is out, it will prioritize notifications related to their activities while out. Furthermore, if the user is in a specific location, it will prioritize notifications related to that location. This enhances the effectiveness of training by providing highly relevant notifications based on the user's geographical location.

[0041] The notification unit can analyze the user's social media activity and send relevant notifications when sending notifications. For example, if the user posts about training on social media, the notification unit will send a notification related to that content. For example, if the user posts about dog behavior on social media, the notification unit will send a notification related to that content. The notification unit can also analyze the user's social media activity and send the most relevant notifications. This enhances the effectiveness of training by sending relevant notifications based on the user's social media activity.

[0042] The display unit can adjust the level of detail based on the importance of the training. For example, for important training, the display unit will show detailed information to make it easier for the user to understand. For example, for less important training, the display unit will show concise information to allow the user to quickly understand it. The display unit can also adjust the level of detail of the displayed content according to the importance of the training. This makes it easier for the user to understand by adjusting the level of detail according to the importance of the training.

[0043] The display unit can apply different display algorithms depending on the training category during display. For example, for basic training, the display unit applies a simple display algorithm. For example, for advanced training, the display unit applies a detailed display algorithm. The display unit can also apply the optimal display algorithm depending on the training category. This makes it easier for the user to understand by applying the most suitable display algorithm according to the training category.

[0044] The display unit can determine the display priority based on when the training was performed. For example, the display unit may prioritize displaying recently performed training. For example, the display unit may prioritize displaying training that has not been performed for a long period of time. The display unit can also determine the display priority based on when the training was performed. This allows users to prioritize checking important information by determining the display priority based on when the training was performed.

[0045] The display unit can adjust the display order based on the relevance of the training sessions. For example, it may prioritize displaying highly relevant training sessions, or postpone displaying less relevant training sessions. The display unit can also adjust the display order based on the relevance of the training sessions, allowing users to prioritize and view important information.

[0046] The voice analysis unit can improve the accuracy of its analysis by considering the interrelationships between commands during voice analysis. For example, the voice analysis unit performs accurate analysis by considering the context in which commands are spoken. For example, when multiple commands are issued in succession, the voice analysis unit considers their interrelationships during analysis. Furthermore, the voice analysis unit can analyze the interrelationships between commands and provide the most appropriate feedback. As a result, the accuracy of the analysis is improved by considering the interrelationships between commands.

[0047] The speech analysis unit can perform speech analysis while considering the attribute information of the person speaking the command. For example, the speech analysis unit can adjust the criteria for speech analysis by considering the speaker's age and gender. For example, the speech analysis unit can perform speech analysis while considering the speaker's accent and dialect. In addition, the speech analysis unit can provide optimal feedback based on the speaker's attribute information. As a result, the accuracy of the analysis is improved by performing the analysis based on the speaker's attribute information.

[0048] The voice analysis unit can perform voice analysis while considering the geographical distribution of commands. For example, the voice analysis unit can prioritize the analysis of commands used in a specific region. For example, the voice analysis unit can select the optimal analysis method based on geographical distribution. Furthermore, the voice analysis unit can improve the accuracy of the analysis by considering the geographical distribution of commands. As a result, the accuracy of the analysis is improved by considering the geographical distribution of commands.

[0049] The speech analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the command during speech analysis. For example, the speech analysis unit can refer to relevant literature related to the command to improve the accuracy of the analysis. For example, the speech analysis unit can select the optimal analysis method based on the relevant literature. The speech analysis unit can also analyze relevant literature related to the command and provide the most appropriate feedback. As a result, the accuracy of the analysis is improved by performing the analysis while referring to relevant literature related to the command.

[0050] The recording unit can optimize its recording algorithm by referring to past recording data during recording. For example, the recording unit can analyze past recording data and select the most effective recording method. For example, the recording unit can adjust the recording algorithm based on past recording data to improve accuracy. The recording unit can also optimize the frequency and content of recordings by referring to past recording data. As a result, the accuracy of recordings is improved by optimizing the recording algorithm by referring to past recording data.

[0051] The recording unit can customize the recording method based on the training performance during recording. For example, the recording unit adjusts the recording method according to the frequency of training. For example, the recording unit customizes the recording method according to the content of training. The recording unit can also analyze the training performance and select the optimal recording method. By customizing the recording method according to the training performance, the accuracy of the recording is improved.

[0052] The recording unit can select the optimal recording method when recording, taking into account the geographical distribution of the training. For example, the recording unit may prioritize recording training conducted in a specific region. For example, the recording unit may select the optimal recording method based on geographical distribution. Furthermore, the recording unit can improve the accuracy of the recording by taking into account the geographical distribution of the training. As a result, the accuracy of the recording is improved by recording while considering the geographical distribution of the training.

[0053] The recording unit can improve the accuracy of recordings by referring to relevant training literature during the recording process. For example, the recording unit can improve the accuracy of recordings by referring to training-related literature. For example, the recording unit can select the optimal recording method based on relevant literature. Furthermore, the recording unit can analyze training-related literature and provide the most appropriate recording method. As a result, recording accuracy is improved by referring to training-related literature during the recording process.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The notification unit can select the optimal notification method by referring to the dog's behavioral history. For example, it can send notifications that encourage similar behaviors based on the dog's past successful behavioral patterns. It can also send notifications with a different approach to help the dog avoid behavioral patterns that have previously failed. Furthermore, it can analyze the dog's behavioral history and select the most effective notification method. By selecting the optimal notification method based on the dog's behavioral history, the effectiveness of training can be enhanced.

[0056] The notification unit can customize the content of notifications based on the dog's current activity level. For example, if the dog is playing, it can send short notifications to avoid interrupting play. If the dog is resting, it can send quiet notifications to avoid disturbing their rest. Furthermore, if the dog is training, it can send detailed notifications to help them concentrate on training. By customizing notification content according to the dog's current activity level, the effectiveness of training can be enhanced.

[0057] The notification system can prioritize highly relevant notifications by considering the user's geographical location. For example, if the user is at home, notifications related to training can be prioritized. Similarly, if the user is out, notifications related to their activities at their current location can be prioritized. Furthermore, if the user is in a specific location, notifications related to that location can be prioritized. This allows for more relevant notifications based on the user's geographical location, thereby enhancing the effectiveness of training.

[0058] The notification unit can analyze a user's social media activity and send relevant notifications. For example, if a user posts about training on social media, it can send a notification related to that content. Similarly, if a user posts about their dog's behavior on social media, it can send a notification related to that content. Furthermore, it can analyze the user's social media activity and send the most relevant notifications. This allows for improved training effectiveness by providing relevant notifications based on the user's social media activity.

[0059] The display unit can adjust the level of detail based on the importance of the training. For example, for important training sessions, detailed information can be displayed to make it easier for the user to understand. Conversely, for less important training sessions, concise information can be displayed to allow the user to quickly understand it. Furthermore, the level of detail of the displayed content can be adjusted according to the importance of the training. This allows the user to understand the information more easily by adjusting the level of detail according to the importance of the training.

[0060] The display unit can apply different display algorithms depending on the training category. For example, a simple display algorithm can be applied for basic training, while a more detailed display algorithm can be applied for advanced training. Furthermore, the optimal display algorithm can be applied depending on the training category. This makes it easier for the user to understand the information by applying the most suitable display algorithm for each training category.

[0061] The display unit can prioritize the display based on when the training was performed. For example, it can prioritize the display of recently performed training. It can also prioritize the display of training that has not been performed for a long time. Furthermore, it can prioritize the display based on when the training was performed. This allows users to check important information first by prioritizing the display based on when the training was performed.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The notification unit, in conjunction with the wearable device, notifies the owner of the appropriate timing for haptic praise. For example, it notifies the owner with vibration or sound the moment the dog performs the correct behavior. It can also notify the owner of the appropriate timing for praise based on the results analyzed by the voice analysis unit. Furthermore, it can estimate the user's emotions and adjust the timing of notifications based on those estimated emotions. Step 2: The display unit shows training results and behavioral success rates in graphs. For example, success rates and reaction speeds are displayed in graphs to allow users to visually check their training progress. It can also estimate the user's emotions and adjust the display method based on those emotions. Step 3: The voice analysis unit analyzes the pronunciation and tone of commands and provides support for improvement. For example, it analyzes the accuracy of command pronunciation and the appropriateness of tone, and provides feedback on areas for improvement. It can also estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. Step 4: The recording unit records the success rate of commands and areas for improvement, visualizing growth. For example, it quantifies training results and behavioral success rates and displays them in graphs. It can also estimate the user's emotions and adjust the recording method based on those estimated emotions.

[0064] (Example of form 2) The smart dog trainer AI agent system according to an embodiment of the present invention is a system that efficiently supports dog training. This system includes wearable device connectivity, a training visualization unit, a voice analysis unit and guidance unit, and a growth record unit. Conventional dog training has had challenges such as difficulty in timing praise, insufficient monitoring of training progress, and accuracy of command pronunciation. To solve these problems, the present invention includes a notification unit that notifies the appropriate timing for praise using haptics through wearable device connectivity. This allows owners to provide feedback at an effective time. Next, it includes a display unit that uses a training visualization unit to display training results and behavior success rates in graph form. This allows owners to check training progress at a glance and maintain motivation. Furthermore, the voice analysis unit and guidance unit analyze the pronunciation and tone of commands and support areas for improvement. This allows for improved communication with dogs through correct voice commands. Finally, it includes a growth record unit that records command success rates and areas for improvement, visualizing growth. This makes it easier to feel the effects of training. For example, the Smart Dog Trainer AI Agent System caters to a wide range of users, from novice owners to professional trainers, and can be used in pet shops, veterinary clinics, and other similar establishments. The Smart Dog Trainer AI Agent integrates voice analysis and behavioral data to create a personalized feedback system, efficiently supporting dog training. This leads to better training for dogs and a stronger bond of trust between owners and their pets. In short, the Smart Dog Trainer AI Agent System efficiently supports dog training and improves communication with owners.

[0065] The smart dog trainer AI agent system according to this embodiment comprises a notification unit, a display unit, a voice analysis unit, and a recording unit. The notification unit notifies the owner of the appropriate timing to praise the dog using haptics through a wearable device connection. For example, the notification unit notifies the owner with vibration or sound the moment the dog performs the correct action. The notification unit can also notify the owner of the appropriate timing to praise the dog based on the results analyzed by the voice analysis unit. For example, the notification unit notifies the owner at the appropriate time after the voice analysis unit analyzes the pronunciation and tone of the command. Furthermore, the notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, if the user is feeling stressed, the notification unit reduces the frequency of notifications and only notifies at important times. The display unit displays training results and behavior success rates in graphs. For example, the display unit displays success rates and reaction speeds in graphs so that the progress of training can be visually confirmed. The display unit can also estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, the display unit reduces visual stress by using calming colors when the user is feeling anxious. The voice analysis unit analyzes the pronunciation and tone of commands and supports areas for improvement. For example, the voice analysis unit analyzes the accuracy and appropriateness of the pronunciation and tone of commands and provides feedback on areas for improvement. The voice analysis unit can also estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is feeling anxious, the voice analysis unit will relax the voice analysis criteria to reduce stress. The recording unit records the success rate of commands and areas for improvement, visualizing progress. For example, the recording unit quantifies training results and behavior success rates and displays them in graphs. The recording unit can also estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is feeling stressed, the recording unit will provide a simpler recording method. As a result, the smart dog trainer AI agent system according to this embodiment can efficiently support dog training and improve communication with the owner.

[0066] The notification unit, in conjunction with wearable devices, uses haptics to notify owners of the appropriate timing for praising their dogs. Specifically, the notification unit vibrates or sounds to notify the owner the moment the dog performs the correct behavior. For example, when a dog obeys a command such as "sit" or "stay," the notification unit sends a vibration to the owner's wearable device, immediately indicating the right time to praise. This allows the owner to praise the dog in a timely manner, improving the effectiveness of training. The notification unit can also notify owners of the appropriate timing for praise based on the results analyzed by the voice analysis unit. For example, the voice analysis unit analyzes the pronunciation and tone of the owner's commands, and if the dog responds correctly, the notification unit vibrates or sounds to notify the owner. Furthermore, the notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions of the user. For example, if the user is feeling stressed, the notification unit reduces the frequency of notifications and only notifies at important times. This reduces user stress and improves the quality of training. The notification unit uses AI to analyze the user's voice and behavior patterns to estimate their emotions. For example, it analyzes the user's voice tone, speaking speed, and body movements to estimate emotions such as stress, tension, and joy. This allows the notification unit to provide appropriate notifications based on the user's emotional state. Furthermore, the notification unit can continuously improve the accuracy and timing of notifications based on user feedback. For example, when a user provides feedback on the timing and content of notifications, the notification unit learns from that information and adjusts to provide more appropriate notifications. This allows the notification unit to respond flexibly to user needs and maximizes the effectiveness of training.

[0067] The display unit shows training results and behavior success rates in graphs. Specifically, the display unit shows success rates and reaction speeds in graphs so that training progress can be visually confirmed. For example, it can display the success rate of a dog following commands such as "sit" or "stay" in line graphs or bar graphs, allowing owners to grasp training results at a glance. The display unit can also estimate the user's emotions and adjust the display method based on the estimated emotions of the user. For example, if the user is tense, the display unit will use calming colors to reduce visual stress. This allows the user to check training progress in a relaxed state. The display unit uses AI to estimate the user's emotions and adjust the display content. For example, it analyzes the user's facial expressions, voice, and behavior patterns to estimate emotions. If the user is happy, it will display bright colors and positive messages to increase the user's motivation. On the other hand, if the user is stressed, it will display calming colors and relaxing messages to reduce the user's stress. Furthermore, the display unit analyzes training results in detail and provides feedback to the user. For example, the system can display changes in success rate and reaction speed for specific commands over time, showing which training methods were effective. This allows users to objectively evaluate the effectiveness of their training and use this information to plan future training sessions. The display unit can also continuously improve its content based on user feedback. For instance, users can provide feedback on the display content and format, allowing the unit to learn from this information and adjust the display to be more user-friendly and easier to understand. This enables the display unit to respond flexibly to user needs, maximizing the effectiveness of training.

[0068] The voice analysis unit analyzes the pronunciation and tone of commands to support improvements. Specifically, the voice analysis unit analyzes the accuracy and appropriateness of the pronunciation and tone of commands and provides feedback on areas for improvement. For example, when an owner gives the command "sit," the unit analyzes whether the pronunciation is accurate and the tone is appropriate, and suggests areas for improvement as needed. This allows the owner to give more effective commands and improve the effectiveness of dog training. The voice analysis unit can also estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is nervous, the voice analysis unit will relax the voice analysis criteria to reduce stress. This allows the user to train in a relaxed state. The voice analysis unit uses AI to analyze the user's voice and estimate emotions. For example, it analyzes the tone, speaking speed, and volume of the user's voice to estimate emotions such as stress, tension, and joy. This allows the voice analysis unit to provide appropriate feedback according to the user's emotional state. Furthermore, the voice analysis unit can continuously improve the accuracy and standards of its voice analysis based on user feedback. For example, when a user provides feedback on the results of the voice analysis, the voice analysis unit learns from that information and adjusts to perform more accurate analysis. This allows the voice analysis unit to respond flexibly to user needs and maximize the effectiveness of its training.

[0069] The recording unit tracks command success rates and areas for improvement, visualizing progress. Specifically, it quantifies training results and behavioral success rates, displaying them in graphs. For example, it can display the success rate of a dog following commands like "sit" or "stay" using line or bar graphs, allowing owners to grasp training results at a glance. The recording unit can also estimate the user's emotions and adjust the recording method based on that estimation. For example, if the user is feeling stressed, the recording unit provides a concise recording method. This allows the user to record training results without feeling stressed. The recording unit uses AI to estimate the user's emotions and adjust the recording content. For example, it analyzes the user's facial expressions, voice, and behavioral patterns to estimate emotions. If the user is happy, it provides detailed records and positive messages to increase user motivation. On the other hand, if the user is stressed, it provides concise records and relaxing messages to reduce user stress. Furthermore, the recording unit analyzes training results in detail and provides feedback to the user. For example, the system can display changes in success rate and reaction speed for specific commands over time, showing which training methods were effective. This allows users to objectively evaluate the effectiveness of their training and use this information to plan future training sessions. The recording unit can also continuously improve its recordings based on user feedback. For instance, when users provide feedback on the content and format of their recordings, the unit learns from this information and adjusts its recordings to be more user-friendly and easier to understand. This allows the recording unit to respond flexibly to user needs and maximize the effectiveness of training.

[0070] The notification unit can notify the owner of the appropriate timing for praise based on the results analyzed by the voice analysis unit. For example, the notification unit can use the voice analysis unit to analyze the pronunciation and tone of commands and notify the owner at the appropriate time. For example, the notification unit can notify the owner with vibration or sound the moment the dog performs the correct behavior. The notification unit can also estimate the user's emotions and adjust the timing of notifications based on the estimated emotions of the user. For example, if the user is feeling stressed, the notification unit will reduce the frequency of notifications and only notify at important times. This enhances the effectiveness of training by providing notifications at the appropriate time based on the voice analysis results.

[0071] The display unit can show training results and behavioral success rates in graphs. For example, the display unit can show success rates and reaction speeds in graphs so that training progress can be visually confirmed. For example, the display unit can quantify training results and display them in graphs. The display unit can also estimate the user's emotions and adjust the display method based on the estimated user emotions. For example, if the user is nervous, the display unit will use calming colors to reduce visual stress. This allows users to visually confirm their training progress and maintain motivation.

[0072] The voice analysis unit can analyze the pronunciation and tone of commands and provide support for improvement. For example, it can analyze the accuracy and appropriateness of command pronunciation and provide feedback on areas for improvement. For example, it can analyze the user's pronunciation and provide guidance on correct pronunciation methods. Furthermore, the voice analysis unit can estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is tense, the voice analysis unit will relax the voice analysis criteria to reduce stress. This can improve communication with the dog through correct voice commands.

[0073] The recording unit can record command success rates and areas for improvement, making growth visible. For example, it can quantify training results and behavioral success rates and display them in graphs. It can also record training progress and visualize growth. Furthermore, the recording unit can estimate the user's emotions and adjust the recording method based on those emotions. For example, if the user is feeling stressed, the recording unit can provide a simpler recording method. This makes it easier for the user to feel the effects of the training.

[0074] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit will reduce the frequency of notifications and only send notifications at important times. For example, if the user is relaxed, the notification unit will increase the frequency of notifications and provide detailed feedback. Also, if the user is in a hurry, the notification unit will make notifications concise and provide quick feedback. This allows for more effective feedback by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The notification unit can select the optimal notification method by referring to the dog's behavioral history when issuing a notification. For example, the notification unit can issue notifications that encourage similar behaviors based on the dog's past successful behavioral patterns. For example, the notification unit can issue notifications with a different approach to avoid behavioral patterns that the dog has previously failed at. The notification unit can also analyze the dog's behavioral history and select the most effective notification method. This enhances the effectiveness of training by selecting the optimal notification method based on the dog's behavioral history.

[0076] The notification unit can customize the content of notifications based on the dog's current activity level. For example, if the dog is playing, it will send a short notification to avoid interrupting play. If the dog is resting, it will send a quiet notification to avoid disturbing rest. If the dog is training, it will send a detailed notification to help the dog concentrate on training. By customizing notification content according to the dog's current activity level, the effectiveness of training is enhanced.

[0077] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, the notification unit will prioritize only important notifications. If the user is relaxed, the notification unit will provide all notifications and detailed feedback. If the user is in a hurry, the notification unit will prioritize the most important notifications. This allows for more effective feedback by prioritizing notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The notification unit can prioritize highly relevant notifications by considering the user's geographical location. For example, if the user is at home, it will prioritize notifications related to training. If the user is out, it will prioritize notifications related to their activities while out. Furthermore, if the user is in a specific location, it will prioritize notifications related to that location. This enhances the effectiveness of training by providing highly relevant notifications based on the user's geographical location.

[0079] The notification unit can analyze the user's social media activity and send relevant notifications when sending notifications. For example, if the user posts about training on social media, the notification unit will send a notification related to that content. For example, if the user posts about dog behavior on social media, the notification unit will send a notification related to that content. The notification unit can also analyze the user's social media activity and send the most relevant notifications. This enhances the effectiveness of training by sending relevant notifications based on the user's social media activity.

[0080] The display unit can estimate the user's emotions and adjust the display's presentation based on the estimated emotions. For example, if the user is tense, the display unit will use calming colors to reduce visual stress. If the user is enjoying themselves, the display unit will use bright colors to make the content more enjoyable. If the user is tired, the display unit will use simple, highly visible displays to make the content easy to understand. In this way, visual stress is reduced by adjusting the display's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The display unit can adjust the level of detail based on the importance of the training. For example, for important training, the display unit will show detailed information to make it easier for the user to understand. For example, for less important training, the display unit will show concise information to allow the user to quickly understand it. The display unit can also adjust the level of detail of the displayed content according to the importance of the training. This makes it easier for the user to understand by adjusting the level of detail according to the importance of the training.

[0082] The display unit can apply different display algorithms depending on the training category during display. For example, for basic training, the display unit applies a simple display algorithm. For example, for advanced training, the display unit applies a detailed display algorithm. The display unit can also apply the optimal display algorithm depending on the training category. This makes it easier for the user to understand by applying the most suitable display algorithm according to the training category.

[0083] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated emotions. For example, if the user is in a hurry, the display unit will show a short, concise display. If the user is relaxed, the display unit will show a longer display that includes detailed explanations. If the user is excited, the display unit will show a visually stimulating display. By adjusting the length of the display according to the user's emotions, it becomes easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The display unit can determine the display priority based on when the training was performed. For example, the display unit may prioritize displaying recently performed training. For example, the display unit may prioritize displaying training that has not been performed for a long period of time. The display unit can also determine the display priority based on when the training was performed. This allows users to prioritize checking important information by determining the display priority based on when the training was performed.

[0085] The display unit can adjust the display order based on the relevance of the training sessions. For example, it may prioritize displaying highly relevant training sessions, or postpone displaying less relevant training sessions. The display unit can also adjust the display order based on the relevance of the training sessions, allowing users to prioritize and view important information.

[0086] The voice analysis unit can estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. For example, if the user is tense, the voice analysis unit will loosen the voice analysis criteria to reduce stress. For example, if the user is relaxed, the voice analysis unit will tighten the voice analysis criteria to provide accurate feedback. Also, if the user is in a hurry, the voice analysis unit will perform a rapid analysis and provide concise feedback. In this way, adjusting the voice analysis criteria according to the user's emotions enables more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The voice analysis unit can improve the accuracy of its analysis by considering the interrelationships between commands during voice analysis. For example, the voice analysis unit performs accurate analysis by considering the context in which commands are spoken. For example, when multiple commands are issued in succession, the voice analysis unit considers their interrelationships during analysis. Furthermore, the voice analysis unit can analyze the interrelationships between commands and provide the most appropriate feedback. As a result, the accuracy of the analysis is improved by considering the interrelationships between commands.

[0088] The speech analysis unit can perform speech analysis while considering the attribute information of the person speaking the command. For example, the speech analysis unit can adjust the criteria for speech analysis by considering the speaker's age and gender. For example, the speech analysis unit can perform speech analysis while considering the speaker's accent and dialect. In addition, the speech analysis unit can provide optimal feedback based on the speaker's attribute information. As a result, the accuracy of the analysis is improved by performing the analysis based on the speaker's attribute information.

[0089] The voice analysis unit can estimate the user's emotions and adjust the order in which the voice analysis results are displayed based on the estimated emotions. For example, if the user is in a hurry, the voice analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the voice analysis unit will display detailed analysis results. Furthermore, if the user is excited, the voice analysis unit will display visually stimulating content. By adjusting the order in which the voice analysis results are displayed according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The voice analysis unit can perform voice analysis while considering the geographical distribution of commands. For example, the voice analysis unit can prioritize the analysis of commands used in a specific region. For example, the voice analysis unit can select the optimal analysis method based on geographical distribution. Furthermore, the voice analysis unit can improve the accuracy of the analysis by considering the geographical distribution of commands. As a result, the accuracy of the analysis is improved by considering the geographical distribution of commands.

[0091] The speech analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the command during speech analysis. For example, the speech analysis unit can refer to relevant literature related to the command to improve the accuracy of the analysis. For example, the speech analysis unit can select the optimal analysis method based on the relevant literature. The speech analysis unit can also analyze relevant literature related to the command and provide the most appropriate feedback. As a result, the accuracy of the analysis is improved by performing the analysis while referring to relevant literature related to the command.

[0092] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit provides a concise recording method. For example, if the user is relaxed, the recording unit provides a detailed recording method. Furthermore, if the user is in a hurry, the recording unit provides a method for quick recording. This allows for more appropriate recording by adjusting the recording method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The recording unit can optimize its recording algorithm by referring to past recording data during recording. For example, the recording unit can analyze past recording data and select the most effective recording method. For example, the recording unit can adjust the recording algorithm based on past recording data to improve accuracy. The recording unit can also optimize the frequency and content of recordings by referring to past recording data. As a result, the accuracy of recordings is improved by optimizing the recording algorithm by referring to past recording data.

[0094] The recording unit can customize the recording method based on the training performance during recording. For example, the recording unit adjusts the recording method according to the frequency of training. For example, the recording unit customizes the recording method according to the content of training. The recording unit can also analyze the training performance and select the optimal recording method. By customizing the recording method according to the training performance, the accuracy of the recording is improved.

[0095] The recording unit can estimate the user's emotions and determine recording priorities based on those emotions. For example, if the user is stressed, the recording unit will prioritize recording only the most important information. If the user is relaxed, the recording unit will record everything and provide detailed data. If the user is in a hurry, the recording unit will prioritize recording only the most important information. This allows for more appropriate recording by prioritizing recordings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The recording unit can select the optimal recording method when recording, taking into account the geographical distribution of the training. For example, the recording unit may prioritize recording training conducted in a specific region. For example, the recording unit may select the optimal recording method based on geographical distribution. Furthermore, the recording unit can improve the accuracy of the recording by taking into account the geographical distribution of the training. As a result, the accuracy of the recording is improved by recording while considering the geographical distribution of the training.

[0097] The recording unit can improve the accuracy of recordings by referring to relevant training literature during the recording process. For example, the recording unit can improve the accuracy of recordings by referring to training-related literature. For example, the recording unit can select the optimal recording method based on relevant literature. Furthermore, the recording unit can analyze training-related literature and provide the most appropriate recording method. As a result, recording accuracy is improved by referring to training-related literature during the recording process.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the frequency of notifications can be reduced, and notifications can only be sent at important times. Conversely, if the user is relaxed, the frequency of notifications can be increased, and detailed feedback can be provided. Furthermore, if the user is in a hurry, notifications can be made concise, and feedback can be provided quickly. This allows for more effective feedback by adjusting the timing of notifications according to the user's emotions.

[0100] The notification unit can select the optimal notification method by referring to the dog's behavioral history. For example, it can send notifications that encourage similar behaviors based on the dog's past successful behavioral patterns. It can also send notifications with a different approach to help the dog avoid behavioral patterns that have previously failed. Furthermore, it can analyze the dog's behavioral history and select the most effective notification method. By selecting the optimal notification method based on the dog's behavioral history, the effectiveness of training can be enhanced.

[0101] The notification unit can customize the content of notifications based on the dog's current activity level. For example, if the dog is playing, it can send short notifications to avoid interrupting play. If the dog is resting, it can send quiet notifications to avoid disturbing their rest. Furthermore, if the dog is training, it can send detailed notifications to help them concentrate on training. By customizing notification content according to the dog's current activity level, the effectiveness of training can be enhanced.

[0102] The notification system can prioritize highly relevant notifications by considering the user's geographical location. For example, if the user is at home, notifications related to training can be prioritized. Similarly, if the user is out, notifications related to their activities at their current location can be prioritized. Furthermore, if the user is in a specific location, notifications related to that location can be prioritized. This allows for more relevant notifications based on the user's geographical location, thereby enhancing the effectiveness of training.

[0103] The notification unit can analyze a user's social media activity and send relevant notifications. For example, if a user posts about training on social media, it can send a notification related to that content. Similarly, if a user posts about their dog's behavior on social media, it can send a notification related to that content. Furthermore, it can analyze the user's social media activity and send the most relevant notifications. This allows for improved training effectiveness by providing relevant notifications based on the user's social media activity.

[0104] The display unit can estimate the user's emotions and adjust the display's presentation based on those emotions. For example, if the user is stressed, it can display calm colors to reduce visual stress. If the user is enjoying themselves, it can display bright colors to make the content more enjoyable. Furthermore, if the user is tired, it can display a simple and highly visible presentation to make the content easy to understand. In this way, by adjusting the display's presentation according to the user's emotions, visual stress can be reduced.

[0105] The display unit can adjust the level of detail based on the importance of the training. For example, for important training sessions, detailed information can be displayed to make it easier for the user to understand. Conversely, for less important training sessions, concise information can be displayed to allow the user to quickly understand it. Furthermore, the level of detail of the displayed content can be adjusted according to the importance of the training. This allows the user to understand the information more easily by adjusting the level of detail according to the importance of the training.

[0106] The display unit can apply different display algorithms depending on the training category. For example, a simple display algorithm can be applied for basic training, while a more detailed display algorithm can be applied for advanced training. Furthermore, the optimal display algorithm can be applied depending on the training category. This makes it easier for the user to understand the information by applying the most suitable display algorithm for each training category.

[0107] The display unit can estimate the user's emotions and adjust the length of the display based on those emotions. For example, if the user is in a hurry, it can display a short, concise message. If the user is relaxed, it can display a longer message with more detailed explanations. Furthermore, if the user is excited, it can display a visually stimulating message. By adjusting the length of the display according to the user's emotions, it makes the information easier for the user to understand.

[0108] The display unit can prioritize the display based on when the training was performed. For example, it can prioritize the display of recently performed training. It can also prioritize the display of training that has not been performed for a long time. Furthermore, it can prioritize the display based on when the training was performed. This allows users to check important information first by prioritizing the display based on when the training was performed.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The notification unit, in conjunction with the wearable device, notifies the owner of the appropriate timing for haptic praise. For example, it notifies the owner with vibration or sound the moment the dog performs the correct behavior. It can also notify the owner of the appropriate timing for praise based on the results analyzed by the voice analysis unit. Furthermore, it can estimate the user's emotions and adjust the timing of notifications based on those estimated emotions. Step 2: The display unit shows training results and behavioral success rates in graphs. For example, success rates and reaction speeds are displayed in graphs to allow users to visually check their training progress. It can also estimate the user's emotions and adjust the display method based on those emotions. Step 3: The voice analysis unit analyzes the pronunciation and tone of commands and provides support for improvement. For example, it analyzes the accuracy of command pronunciation and the appropriateness of tone, and provides feedback on areas for improvement. It can also estimate the user's emotions and adjust the voice analysis criteria based on the estimated emotions. Step 4: The recording unit records the success rate of commands and areas for improvement, visualizing growth. For example, it quantifies training results and behavioral success rates and displays them in graphs. It can also estimate the user's emotions and adjust the recording method based on those estimated emotions.

[0111] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0114] Each of the multiple elements described above, including the notification unit, display unit, voice analysis unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the notification unit is implemented by the control unit 46A of the smart device 14 and notifies the appropriate timing for haptic praise. The display unit displays training results and behavior success rates in a graph on the display 40A of the smart device 14. The voice analysis unit analyzes the pronunciation and tone of commands by the specific processing unit 290 of the data processing unit 12 and supports points for improvement. The recording unit records the success rate of commands and points of improvement in the database 24 of the data processing unit 12, visualizing growth. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 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.

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

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the notification unit, display unit, voice analysis unit, and recording unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the appropriate timing for haptic praise. The display unit displays training results and behavior success rates in a graph on the display of the smart glasses 214. The voice analysis unit analyzes the pronunciation and tone of commands by the specific processing unit 290 of the data processing unit 12 and supports points for improvement. The recording unit records the success rate of commands and points of improvement in the database 24 of the data processing unit 12, visualizing growth. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the notification unit, display unit, voice analysis unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the appropriate timing for haptic praise. The display unit displays training results and behavior success rates in a graph on the display 343 of the headset terminal 314. The voice analysis unit analyzes the pronunciation and tone of commands by the specific processing unit 290 of the data processing unit 12 and supports points for improvement. The recording unit records the success rate of commands and points of improvement in the database 24 of the data processing unit 12, visualizing growth. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the notification unit, display unit, voice analysis unit, and recording unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the notification unit is implemented by the control unit 46A of the robot 414 and notifies the robot of the appropriate timing for haptic praise. The display unit displays training results and behavior success rates in a graph on the robot 414's display. The voice analysis unit analyzes the pronunciation and tone of commands by the specific processing unit 290 of the data processing unit 12 and supports points for improvement. The recording unit records the command success rate and points for improvement in the database 24 of the data processing unit 12, visualizing growth. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) Through integration with wearable devices, the notification unit notifies the appropriate timing for haptic praise, A display unit that shows training results and success rates of actions in graph form, The voice analysis unit analyzes the pronunciation and tone of commands and provides support for areas of improvement. It includes a recording unit that records the success rate and areas for improvement of commands, and visualizes growth. A system characterized by the following features. (Note 2) The aforementioned notification unit, Based on the results of the voice analysis unit, the system notifies you of the appropriate timing for giving praise. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is The training results and success rate of the behavior are displayed in a graph. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned voice analysis unit, It analyzes the pronunciation and tone of commands and provides support for areas of improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The recording unit is, Record the success rate of commands and areas for improvement to visualize growth. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, When sending a notification, the system will refer to the dog's behavior history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the dog's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned notification unit, When sending notifications, the system prioritizes relevant notifications by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned notification unit, When sending notifications, the system analyzes the user's social media activity and sends relevant notifications. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned display unit is When displaying, adjust the level of detail based on the importance of the training. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned display unit is When displaying data, different display algorithms are applied depending on the training category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is It estimates the user's emotions and adjusts the display length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is When displaying information, the display priority is determined based on when the training was conducted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is When displaying, adjust the display order based on the relevance of the training. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the criteria for voice analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned voice analysis unit, When analyzing voice data, consider the interrelationships between commands to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned voice analysis unit, During voice analysis, the analysis takes into account the attribute information of the person who pronounced the command. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the order in which the voice analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned voice analysis unit, During voice analysis, the analysis takes into account the geographical distribution of commands. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned voice analysis unit, During speech analysis, we improve the accuracy of the analysis by referring to relevant literature for the commands. The system described in Appendix 1, characterized by the features described herein. (Note 24) The recording unit is, The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recording unit is, During recording, the recording algorithm is optimized by referring to past recording data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The recording unit is, When recording, customize the recording method based on the training performance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The recording unit is, The system estimates the user's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The recording unit is, When recording data, select the optimal recording method considering the geographical distribution of the training area. The system described in Appendix 1, characterized by the features described herein. (Note 29) The recording unit is, When recording, refer to relevant training literature to improve the accuracy of your recordings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. Through integration with wearable devices, the notification unit notifies the appropriate timing for haptic praise, A display unit that shows training results and success rates of actions in graph form, The voice analysis unit analyzes the pronunciation and tone of commands and provides support for areas of improvement. It includes a recording unit that records the success rate and areas for improvement of commands, and visualizes growth. A system characterized by the following features.

2. The aforementioned notification unit, Based on the results analyzed by the aforementioned voice analysis unit, the appropriate timing for giving praise is notified. The system according to feature 1.

3. The aforementioned display unit is The training results and success rate of the behavior are displayed in a graph. The system according to feature 1.

4. The aforementioned voice analysis unit, It analyzes the pronunciation and tone of commands and provides support for areas of improvement. The system according to feature 1.

5. The recording unit is, Record the success rate of commands and areas for improvement to visualize growth. The system according to feature 1.

6. The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system according to feature 1.

7. The aforementioned notification unit, When sending a notification, the system will refer to the dog's behavior history to select the most suitable notification method. The system according to feature 1.

8. The aforementioned notification unit, When a notification is sent, the content of the notification will be customized based on the dog's current activity level. The system according to feature 1.

9. The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system according to feature 1.

10. The aforementioned notification unit, When sending notifications, the system prioritizes relevant notifications by considering the user's geographical location. The system according to feature 1.

Images