system

Abstract

The system according to this embodiment aims to estimate the emotions of pet owners and provide appropriate advice based on that estimation. [Solution] The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the pet owner. The emotion estimation unit estimates the emotions of the pet owner based on the emotion estimation information acquired by the acquisition unit. The provision unit provides advice to the pet owner based on the emotions estimated by the emotion estimation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, 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 conventional technology, it has not been fully achieved to accurately grasp the feelings of pet owners and provide appropriate advice based on them, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the feelings of pet owners and provide appropriate advice based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the pet owner. The emotion estimation unit estimates the emotions of the pet owner based on the emotion estimation information acquired by the acquisition unit. The provision unit provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. [Effects of the Invention]

[0007] The system according to this embodiment can estimate the emotions of a pet owner and provide appropriate advice based on that. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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 pet care support robot system according to an embodiment of the present invention is a system that reads the emotions of pet owners using an emotion engine and provides appropriate pet care advice. The pet care support robot system reads the emotions of pet owners and provides appropriate pet care advice. For example, to an owner who is anxious about their pet's health, it provides hints for health management and methods to reduce stress, giving them a sense of security. It also learns the emotions of pet owners based on their pets' behavior and proposes advice to improve the pet's behavior in accordance with the pet owner's emotions, as well as methods to calm the pet owner's emotions. First, there is an acquisition unit that acquires information for estimating the pet owner's emotions. This acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. For example, it acquires the tone of voice and facial expressions when the pet owner is talking about their pet's health. Next, there is an emotion estimation unit that estimates the pet owner's emotions based on the emotion estimation information acquired by the acquisition unit. This emotion estimation unit analyzes the collected information and estimates which emotions the pet owner is feeling, such as anxiety, joy, or sadness. For example, if a pet owner's tone of voice indicates anxiety when discussing their pet's health, the emotion estimation unit estimates that the pet owner is feeling anxious. Furthermore, there is a provision unit that provides advice to the pet owner based on the emotion estimated by the emotion estimation unit. This provision unit provides appropriate advice tailored to the pet owner's emotions. For example, if the pet owner is anxious about their pet's health, the provision unit provides tips on health management and stress reduction methods. There is also a learning unit that learns the relationship between the pet owner's emotions and the pet's behavior. This learning unit learns the relationship between the pet owner's emotions and the pet's behavior patterns and provides advice to improve the pet's behavior in accordance with the pet owner's emotions. For example, if the pet owner is dissatisfied with the pet's behavior, the learning unit provides advice to improve that behavior. Furthermore, the learning unit learns the relationship between the pet owner's emotions and the behaviors of multiple pets, and the provision unit provides advice to improve the behaviors of multiple pets based on the learning results.For example, it can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement advice based on the pet owner's emotions. This allows the pet care support robot system to estimate the pet owner's emotions and provide appropriate advice.

[0029] The pet care support robot system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of a pet owner. The acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. For example, the acquisition unit acquires the tone of voice and facial expressions when the pet owner is talking about the pet's health. The acquisition unit can also acquire social media posts and survey results from the pet owner as emotion estimation information. The emotion estimation unit estimates the pet owner's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the collected information to estimate which emotions the pet owner is feeling, such as anxiety, joy, or sadness. For example, if the tone of voice of the pet owner when talking about the pet's health indicates anxiety, the emotion estimation unit estimates that the pet owner is feeling anxious. The emotion estimation unit can also analyze the pet owner's facial expressions and behavioral patterns to estimate emotions. The provision unit provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. The providing unit, for example, provides appropriate advice in response to the pet owner's emotions. For example, if the pet owner is anxious about their pet's health, the providing unit can provide health management tips and stress reduction methods. The providing unit can also provide advice on how to improve the pet's behavior if the pet owner is dissatisfied with it. In this way, the pet care support robot system according to the embodiment can estimate the pet owner's emotions and provide appropriate advice. Some or all of the above-described processes in the acquisition unit, emotion estimation unit, and providing unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the pet owner's facial expression data into a generation AI and cause the generation AI to acquire information for emotion estimation. The emotion estimation unit can input the information for emotion estimation acquired by the acquisition unit into the generation AI and cause the generation AI to estimate emotions. The providing unit can input the emotions estimated by the emotion estimation unit into the generation AI and cause the generation AI to provide advice.

[0030] The data acquisition unit acquires emotion estimation information, which is used to estimate the emotions of pet owners. For example, the data acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. Specifically, the data acquisition unit uses a high-resolution camera and microphone to capture the pet owner's facial expressions and tone of voice in real time. The camera can capture subtle facial movements and changes in expression, and the microphone records changes in tone and volume of voice with high precision. This allows for detailed acquisition of the pet owner's tone of voice and facial expressions when they talk about their pet's health. The data acquisition unit can also acquire emotion estimation information from the pet owner's social media posts and survey results. Social media posts can reveal the pet owner's daily emotions and feelings towards their pet, and survey results can collect specific emotions and opinions from the pet owner. Furthermore, the data acquisition unit also collects data from smartphones and wearable devices to monitor the pet owner's behavioral patterns. This allows for the acquisition of biometric information such as the pet owner's movement history, activity level, and heart rate, which can then be used as emotion estimation information. By integrating these diverse data sources, the data acquisition unit can collect a wealth of information necessary to accurately estimate the emotions of pet owners.

[0031] The emotion estimation unit estimates the pet owner's emotions based on emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the collected information to estimate which emotions the pet owner is experiencing, such as anxiety, joy, or sadness. Specifically, the emotion estimation unit uses AI to perform facial recognition and voice analysis. In facial recognition, it extracts facial feature points and analyzes subtle changes in facial expression to estimate the pet owner's emotions. For example, it analyzes eyebrow movements and the degree to which the corners of the mouth are raised to identify emotions such as joy, sadness, or anxiety. In voice analysis, it analyzes voice tone, volume, and speaking speed to detect changes in emotion. For example, if the voice tone becomes higher or the speaking speed increases, it is estimated that the person is showing excitement or anxiety. The emotion estimation unit also analyzes the pet owner's behavior patterns and social media posts. In the behavior pattern analysis, it estimates the degree of stress and fatigue based on daily activity levels and travel history. In the social media post analysis, it uses natural language processing technology to extract emotions from the posts. For example, a large number of positive words is estimated to indicate joy, while a large number of negative words is estimated to indicate anxiety or sadness. This allows the emotion estimation unit to comprehensively analyze the diverse data provided by the acquisition unit and estimate the pet owner's emotions with high accuracy.

[0032] The service provider provides advice to pet owners based on the emotions estimated by the emotion estimation unit. For example, the service provider provides appropriate advice tailored to the pet owner's emotions. Specifically, if the pet owner is worried about their pet's health, the service provider provides tips on health management and stress reduction methods. For example, it provides specific advice on pet diet and exercise, and the importance of regular health checks. Also, if the pet owner is dissatisfied with their pet's behavior, the service provider provides advice on how to improve that behavior. For example, it suggests methods for training pets, behavior modification techniques, and appropriate training methods. The service provider uses AI to generate advice that is best suited to the pet owner's emotions. For example, the emotions estimated by the emotion estimation unit are input into the generating AI to generate specific advice tailored to the pet owner's situation. Based on past data and expertise, the generating AI can provide the most effective advice for the pet owner. Furthermore, the service provider collects feedback from pet owners and evaluates the effectiveness of the advice. For example, it records how pet owners reacted to the advice provided and incorporates this into future advice. This allows the service provider to consistently provide pet owners with the best possible advice and improve the quality of pet care.

[0033] The learning unit can learn the relationship between pet owners' emotions and their pets' behavior. For example, the learning unit can learn the relationship between pet owners' emotions and their pets' behavioral patterns and provide advice to improve the pet's behavior in accordance with the pet owner's emotions. For example, if a pet owner is dissatisfied with their pet's behavior, the learning unit can provide advice on how to improve that behavior. The learning unit can also learn the relationship between pet owners' emotions and their pets' behavioral patterns over the long term and provide advice on how to improve behavior in accordance with the pet owner's emotions. For example, if a pet owner is dissatisfied with their pet's behavior, the learning unit can provide training methods to improve that behavior. This allows the learning unit to learn the relationship between pet owners' emotions and their pets' behavior and provide more appropriate advice. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet owner's emotional data and pet behavioral data into a generating AI and have the generating AI perform the learning.

[0034] The service provider can provide advice to improve the pet's behavior in accordance with the emotions estimated by the emotion estimation unit, based on the learning results from the learning unit. For example, the service provider can provide advice to improve the pet's behavior in accordance with the pet owner's emotions, based on the learning results from the learning unit. For example, if the pet owner is dissatisfied with the pet's behavior, the service provider can provide training methods to improve that behavior. The service provider can also provide specific advice to improve the pet's behavior in accordance with the pet owner's emotions, based on the learning results from the learning unit. For example, if the pet owner is dissatisfied with the pet's behavior, the service provider can provide behavior modification techniques to improve that behavior. This allows the service provider to provide advice to improve the pet's behavior. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the learning results from the learning unit into a generating AI and have the generating AI provide the advice.

[0035] The learning unit can learn the relationship between the pet owner's emotions and the behavior of multiple pets. For example, the learning unit can learn the pet owner's emotions and the behavioral patterns of multiple pets, and provide advice to improve the behavior of multiple pets in response to the pet owner's emotions. For example, the learning unit can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement advice in response to the pet owner's emotions. The learning unit can also learn the pet owner's emotions and the behavioral patterns of multiple pets over the long term and provide advice to improve behavior in response to the pet owner's emotions. For example, the learning unit can learn the behavioral patterns of multiple pets when they are together and provide appropriate training methods for behavioral improvement in response to the pet owner's emotions. This allows the learning unit to learn the behavior of multiple pets and provide more appropriate advice. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet owner emotion data and multiple pet behavioral data into a generating AI and have the generating AI perform the learning.

[0036] The service provider can provide advice to improve the behavior of multiple pets in accordance with the emotions estimated by the emotion estimation unit, based on the learning results from the learning unit. For example, the service provider can provide advice to improve the behavior of multiple pets in accordance with the emotions of the pet owner, based on the learning results from the learning unit. For example, the service provider can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement advice in accordance with the emotions of the pet owner. The service provider can also provide specific advice to improve the behavior of multiple pets in accordance with the emotions of the pet owner, based on the learning results from the learning unit. For example, the service provider can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement training methods in accordance with the emotions of the pet owner. This allows the service provider to provide advice to improve the behavior of multiple pets. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the learning results from the learning unit into a generating AI and have the generating AI provide the advice.

[0037] The data acquisition unit can analyze the pet owner's past emotional history and select the optimal data acquisition method. For example, the data acquisition unit can identify time periods in the past when the pet owner felt anxious and focus on acquiring emotional estimation information during those times. The data acquisition unit can also analyze emotional changes in specific events or situations from the pet owner's past emotional history and select an acquisition method appropriate to that situation. Furthermore, based on the pet owner's past emotional history, the data acquisition unit can intensively acquire emotional estimation information during periods of intense emotional change. This allows for the selection of the optimal data acquisition method based on the pet owner's past emotional history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the pet owner's past emotional data into a generating AI and have the generating AI select the optimal data acquisition method.

[0038] The data acquisition unit can filter the data for emotion estimation based on the pet owner's current living situation and areas of interest. For example, if the pet owner is at work, the data acquisition unit will prioritize acquiring work-related stressors. Furthermore, if the pet owner is engrossed in a hobby, the data acquisition unit can prioritize acquiring emotion estimation information related to that hobby. Additionally, if the pet owner is spending time with family, the data acquisition unit can acquire emotion estimation information based on their relationship with family. This allows for filtering of emotion estimation information based on the pet owner's living situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the pet owner's living situation data into a generating AI and have the generating AI perform the filtering.

[0039] The acquisition unit can prioritize acquiring highly relevant information by considering the pet owner's geographical location when acquiring information for emotion estimation. For example, if the pet owner is at home, the acquisition unit will prioritize acquiring information about the area around their home. Furthermore, if the pet owner is traveling, the acquisition unit can prioritize acquiring information about their travel destination. Additionally, if the pet owner is at work, the acquisition unit can prioritize acquiring information about the area around their workplace. This allows the acquisition unit to acquire highly relevant information by considering the pet owner's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the pet owner's geographical location data into a generating AI and have the generating AI acquire highly relevant information.

[0040] The acquisition unit can analyze the pet owner's social media activity and acquire relevant information when acquiring information for emotion estimation. For example, if the pet owner posts about their pet's health on social media, the acquisition unit can acquire the content of that post. The acquisition unit can also acquire information related to stress if the pet owner mentions stress on social media. Furthermore, if the acquisition unit expresses joy on social media, it can acquire information related to that joy. In this way, relevant information can be acquired by analyzing the pet owner's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the pet owner's social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0041] The emotion estimation unit can improve its estimation accuracy by referring to the pet owner's past emotional data during emotion estimation. For example, the emotion estimation unit estimates the current emotion based on the pet owner's past emotional data. The emotion estimation unit can also analyze the pet owner's past emotional data to identify patterns of emotion change and improve estimation accuracy. Furthermore, the emotion estimation unit can optimize the emotion estimation algorithm using the pet owner's past emotional data. This allows for improved estimation accuracy by referring to the pet owner's past emotional data. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the pet owner's past emotional data into a generating AI and have the generating AI perform the improvement of estimation accuracy.

[0042] The emotion estimation unit can customize its estimation method based on the pet owner's lifestyle and areas of interest during emotion estimation. For example, if the pet owner is at work, the emotion estimation unit will prioritize estimating emotions related to work. Similarly, if the pet owner is engrossed in a hobby, the emotion estimation unit can prioritize estimating emotions related to that hobby. Furthermore, if the pet owner is spending time with family, the emotion estimation unit can prioritize estimating emotions based on their relationship with family. This allows for the customization of the emotion estimation method based on the pet owner's lifestyle and areas of interest. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input pet owner lifestyle data into a generating AI and have the generating AI perform the customization of the estimation method.

[0043] The emotion estimation unit can improve its estimation accuracy by considering the pet owner's geographical location information during emotion estimation. For example, if the pet owner is at home, the emotion estimation unit will prioritize estimating the pet's emotions at home. Furthermore, if the pet owner is traveling, the emotion estimation unit can prioritize estimating the pet's emotions at their travel destination. Additionally, if the pet owner is at work, the emotion estimation unit can prioritize estimating the pet's emotions at work. This allows for improved emotion estimation accuracy by considering the pet owner's geographical location information. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the pet owner's geographical location data into a generating AI and have the generating AI perform the improvement of estimation accuracy.

[0044] The emotion estimation unit can improve its estimation accuracy by analyzing the pet owner's social media activity during emotion estimation. For example, if the pet owner posts about their pet's health on social media, the emotion estimation unit can estimate the pet owner's emotions based on the content of those posts. The emotion estimation unit can also estimate emotions related to stress if the pet owner mentions stress on social media. Furthermore, if the pet owner expresses joy on social media, the emotion estimation unit can estimate emotions related to that joy. This allows for improved emotion estimation accuracy by analyzing the pet owner's social media activity. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the pet owner's social media data into a generating AI and have the generating AI perform the task of improving estimation accuracy.

[0045] The service provider can select the most appropriate advice by referring to the pet owner's past emotional data when providing advice. For example, the service provider can provide advice that is best suited to the pet owner's current emotions based on their past emotional data. The service provider can also analyze the pet owner's past emotional data and provide advice based on patterns of emotional change. Furthermore, the service provider can use the pet owner's past emotional data to optimize the content of the advice. This allows the service provider to provide the most appropriate advice based on the pet owner's past emotional data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet owner's past emotional data into a generating AI and have the generating AI select the most appropriate advice.

[0046] The service provider can customize the advice given based on the pet owner's lifestyle and areas of interest. For example, if the pet owner is at work, the service provider can provide work-related advice. It can also provide advice related to a hobby if the pet owner is engrossed in one. Furthermore, if the pet owner is spending time with family, the service provider can provide advice based on their relationship with family. This allows for the customization of advice based on the pet owner's lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input pet owner lifestyle data into a generating AI and have the generating AI customize the advice.

[0047] The service provider can provide optimal advice by considering the pet owner's geographical location when providing advice. For example, if the pet owner is at home, the service provider can provide advice that can be implemented at home. If the pet owner is traveling, the service provider can also provide advice that can be implemented at their travel destination. Furthermore, if the pet owner is at work, the service provider can provide advice that can be implemented at work. This allows the service provider to provide optimal advice by considering the pet owner's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet owner's geographical location data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0048] The service provider can analyze the pet owner's social media activity and adjust the advice accordingly. For example, if the pet owner posts about their pet's health on social media, the service provider can provide advice based on that post. It can also provide advice related to stress if the pet owner mentions stress on social media. Furthermore, if the pet owner expresses joy on social media, the service provider can provide advice on maintaining that joy. This allows the service provider to analyze the pet owner's social media activity and adjust the advice accordingly. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet owner's social media data into a generating AI and have the generating AI adjust the advice.

[0049] The learning unit can optimize its learning algorithm by referring to the pet owner's past emotional data during training. For example, the learning unit adjusts the learning algorithm based on the pet owner's past emotional data. The learning unit can also analyze the pet owner's past emotional data, identify patterns of emotional change, and optimize the learning algorithm. Furthermore, the learning unit can use the pet owner's past emotional data to improve the accuracy of the learning algorithm. This allows the learning algorithm to be optimized based on the pet owner's past emotional data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the pet owner's past emotional data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0050] The learning unit can weight the learning data based on the pet owner's lifestyle and areas of interest during the learning process. For example, if the pet owner is at work, the learning unit will weight the data related to work during the learning process. Similarly, if the pet owner is engrossed in a hobby, the learning unit can weight the data related to that hobby during the learning process. Furthermore, if the pet owner is spending time with family, the learning unit can weight the data based on their relationship with family. This allows the learning data to be weighted based on the pet owner's lifestyle and areas of interest. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the pet owner's lifestyle data into a generating AI and have the generating AI perform the weighting of the learning data.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] A pet care support robot system can be equipped with the ability to automatically adjust the pet care schedule based on the pet owner's lifestyle. For example, if the pet owner is busy, the system can shorten the pet care schedule to provide more efficient care. If the pet owner is on vacation, the system can suggest a schedule that allows for more time spent with the pet. Furthermore, if the pet owner is traveling, the system can arrange for a pet care service to be provided. This allows for adjusting the pet care schedule according to the pet owner's lifestyle, maintaining a balance between the pet's health and the owner's well-being.

[0053] A pet care support robot system can have the function to adjust pet care methods based on the pet owner's health condition. For example, if the pet owner is unwell, it can simplify pet care to reduce the owner's burden. If the pet owner is healthy, it can also suggest activities to do with the pet. Furthermore, if the pet owner is undergoing rehabilitation, it can provide a rehabilitation plan to be done with the pet. This allows for adjusting pet care methods according to the pet owner's health condition, reducing the owner's burden while maintaining the pet's health.

[0054] A pet care support robot system can be equipped with the ability to customize pet care methods based on the pet owner's living environment. For example, if the pet owner lives in an urban area, it can suggest pet care methods suitable for the urban environment. If the pet owner lives in the suburbs, it can provide pet care methods that utilize the natural environment. Furthermore, if the pet owner lives in an apartment building, it can suggest pet care methods that are considerate of neighbors. In this way, pet care methods can be customized according to the pet owner's living environment, improving the quality of life for both pets and owners.

[0055] The pet care support robot system can be equipped with a function to suggest activities for pets based on the pet owner's hobbies and interests. For example, if the pet owner enjoys outdoor activities, it can suggest outdoor activities that can be enjoyed with their pet. If the pet owner prefers indoor activities, it can also provide indoor activities that can be enjoyed with their pet. Furthermore, if the pet owner enjoys a particular sport, it can suggest activities related to that sport. This allows for the suggestion of activities for pets according to the pet owner's hobbies and interests, thereby deepening the bond between pets and their owners.

[0056] A pet care support robot system can have the ability to adjust pet care methods based on the pet owner's social media activity. For example, if a pet owner frequently posts about their pet's health on social media, the system can suggest health management methods based on those posts. It can also provide care methods related to the pet's behavior if the pet owner mentions it on social media. Furthermore, if the pet owner shares enjoyable moments with their pet on social media, the system can suggest care methods to maintain that enjoyment. This allows for the adjustment of pet care methods based on the pet owner's social media activity, improving the quality of life for both pets and their owners.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the pet owner. The acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. For example, the acquisition unit acquires the tone of voice and facial expressions when the pet owner is talking about the pet's health. The acquisition unit can also acquire the pet owner's social media posts and survey results as emotion estimation information. Step 2: The emotion estimation unit estimates the pet owner's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the collected information to estimate which emotions the pet owner is experiencing, such as anxiety, joy, or sadness. For example, if the tone of voice of the pet owner when talking about the pet's health indicates anxiety, the emotion estimation unit estimates that the pet owner is feeling anxious. The emotion estimation unit can also estimate emotions by analyzing the pet owner's facial expressions and behavioral patterns. Step 3: The service provider provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. For example, the service provider provides appropriate advice tailored to the pet owner's emotions. For instance, if the pet owner is worried about their pet's health, the service provider offers health management tips and stress reduction methods. The service provider can also provide advice on how to improve the pet owner's behavior if they are dissatisfied with it.

[0059] (Example of form 2) The pet care support robot system according to an embodiment of the present invention is a system that reads the emotions of pet owners using an emotion engine and provides appropriate pet care advice. The pet care support robot system reads the emotions of pet owners and provides appropriate pet care advice. For example, to an owner who is anxious about their pet's health, it provides hints for health management and methods to reduce stress, giving them a sense of security. It also learns the emotions of pet owners based on their pets' behavior and proposes advice to improve the pet's behavior in accordance with the pet owner's emotions, as well as methods to calm the pet owner's emotions. First, there is an acquisition unit that acquires information for estimating the pet owner's emotions. This acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. For example, it acquires the tone of voice and facial expressions when the pet owner is talking about their pet's health. Next, there is an emotion estimation unit that estimates the pet owner's emotions based on the emotion estimation information acquired by the acquisition unit. This emotion estimation unit analyzes the collected information and estimates which emotions the pet owner is feeling, such as anxiety, joy, or sadness. For example, if a pet owner's tone of voice indicates anxiety when discussing their pet's health, the emotion estimation unit estimates that the pet owner is feeling anxious. Furthermore, there is a provision unit that provides advice to the pet owner based on the emotion estimated by the emotion estimation unit. This provision unit provides appropriate advice tailored to the pet owner's emotions. For example, if the pet owner is anxious about their pet's health, the provision unit provides tips on health management and stress reduction methods. There is also a learning unit that learns the relationship between the pet owner's emotions and the pet's behavior. This learning unit learns the relationship between the pet owner's emotions and the pet's behavior patterns and provides advice to improve the pet's behavior in accordance with the pet owner's emotions. For example, if the pet owner is dissatisfied with the pet's behavior, the learning unit provides advice to improve that behavior. Furthermore, the learning unit learns the relationship between the pet owner's emotions and the behaviors of multiple pets, and the provision unit provides advice to improve the behaviors of multiple pets based on the learning results.For example, it can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement advice based on the pet owner's emotions. This allows the pet care support robot system to estimate the pet owner's emotions and provide appropriate advice.

[0060] The pet care support robot system according to this embodiment comprises an acquisition unit, an emotion estimation unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of a pet owner. The acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. For example, the acquisition unit acquires the tone of voice and facial expressions when the pet owner is talking about the pet's health. The acquisition unit can also acquire social media posts and survey results from the pet owner as emotion estimation information. The emotion estimation unit estimates the pet owner's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the collected information to estimate which emotions the pet owner is feeling, such as anxiety, joy, or sadness. For example, if the tone of voice of the pet owner when talking about the pet's health indicates anxiety, the emotion estimation unit estimates that the pet owner is feeling anxious. The emotion estimation unit can also analyze the pet owner's facial expressions and behavioral patterns to estimate emotions. The provision unit provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. The providing unit, for example, provides appropriate advice in response to the pet owner's emotions. For example, if the pet owner is anxious about their pet's health, the providing unit can provide health management tips and stress reduction methods. The providing unit can also provide advice on how to improve the pet's behavior if the pet owner is dissatisfied with it. In this way, the pet care support robot system according to the embodiment can estimate the pet owner's emotions and provide appropriate advice. Some or all of the above-described processes in the acquisition unit, emotion estimation unit, and providing unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the pet owner's facial expression data into a generation AI and cause the generation AI to acquire information for emotion estimation. The emotion estimation unit can input the information for emotion estimation acquired by the acquisition unit into the generation AI and cause the generation AI to estimate emotions. The providing unit can input the emotions estimated by the emotion estimation unit into the generation AI and cause the generation AI to provide advice.

[0061] The data acquisition unit acquires emotion estimation information, which is used to estimate the emotions of pet owners. For example, the data acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. Specifically, the data acquisition unit uses a high-resolution camera and microphone to capture the pet owner's facial expressions and tone of voice in real time. The camera can capture subtle facial movements and changes in expression, and the microphone records changes in tone and volume of voice with high precision. This allows for detailed acquisition of the pet owner's tone of voice and facial expressions when they talk about their pet's health. The data acquisition unit can also acquire emotion estimation information from the pet owner's social media posts and survey results. Social media posts can reveal the pet owner's daily emotions and feelings towards their pet, and survey results can collect specific emotions and opinions from the pet owner. Furthermore, the data acquisition unit also collects data from smartphones and wearable devices to monitor the pet owner's behavioral patterns. This allows for the acquisition of biometric information such as the pet owner's movement history, activity level, and heart rate, which can then be used as emotion estimation information. By integrating these diverse data sources, the data acquisition unit can collect a wealth of information necessary to accurately estimate the emotions of pet owners.

[0062] The emotion estimation unit estimates the pet owner's emotions based on emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the collected information to estimate which emotions the pet owner is experiencing, such as anxiety, joy, or sadness. Specifically, the emotion estimation unit uses AI to perform facial recognition and voice analysis. In facial recognition, it extracts facial feature points and analyzes subtle changes in facial expression to estimate the pet owner's emotions. For example, it analyzes eyebrow movements and the degree to which the corners of the mouth are raised to identify emotions such as joy, sadness, or anxiety. In voice analysis, it analyzes voice tone, volume, and speaking speed to detect changes in emotion. For example, if the voice tone becomes higher or the speaking speed increases, it is estimated that the person is showing excitement or anxiety. The emotion estimation unit also analyzes the pet owner's behavior patterns and social media posts. In the behavior pattern analysis, it estimates the degree of stress and fatigue based on daily activity levels and travel history. In the social media post analysis, it uses natural language processing technology to extract emotions from the posts. For example, a large number of positive words is estimated to indicate joy, while a large number of negative words is estimated to indicate anxiety or sadness. This allows the emotion estimation unit to comprehensively analyze the diverse data provided by the acquisition unit and estimate the pet owner's emotions with high accuracy.

[0063] The service provider provides advice to pet owners based on the emotions estimated by the emotion estimation unit. For example, the service provider provides appropriate advice tailored to the pet owner's emotions. Specifically, if the pet owner is worried about their pet's health, the service provider provides tips on health management and stress reduction methods. For example, it provides specific advice on pet diet and exercise, and the importance of regular health checks. Also, if the pet owner is dissatisfied with their pet's behavior, the service provider provides advice on how to improve that behavior. For example, it suggests methods for training pets, behavior modification techniques, and appropriate training methods. The service provider uses AI to generate advice that is best suited to the pet owner's emotions. For example, the emotions estimated by the emotion estimation unit are input into the generating AI to generate specific advice tailored to the pet owner's situation. Based on past data and expertise, the generating AI can provide the most effective advice for the pet owner. Furthermore, the service provider collects feedback from pet owners and evaluates the effectiveness of the advice. For example, it records how pet owners reacted to the advice provided and incorporates this into future advice. This allows the service provider to consistently provide pet owners with the best possible advice and improve the quality of pet care.

[0064] The learning unit can learn the relationship between pet owners' emotions and their pets' behavior. For example, the learning unit can learn the relationship between pet owners' emotions and their pets' behavioral patterns and provide advice to improve the pet's behavior in accordance with the pet owner's emotions. For example, if a pet owner is dissatisfied with their pet's behavior, the learning unit can provide advice on how to improve that behavior. The learning unit can also learn the relationship between pet owners' emotions and their pets' behavioral patterns over the long term and provide advice on how to improve behavior in accordance with the pet owner's emotions. For example, if a pet owner is dissatisfied with their pet's behavior, the learning unit can provide training methods to improve that behavior. This allows the learning unit to learn the relationship between pet owners' emotions and their pets' behavior and provide more appropriate advice. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet owner's emotional data and pet behavioral data into a generating AI and have the generating AI perform the learning.

[0065] The service provider can provide advice to improve the pet's behavior in accordance with the emotions estimated by the emotion estimation unit, based on the learning results from the learning unit. For example, the service provider can provide advice to improve the pet's behavior in accordance with the pet owner's emotions, based on the learning results from the learning unit. For example, if the pet owner is dissatisfied with the pet's behavior, the service provider can provide training methods to improve that behavior. The service provider can also provide specific advice to improve the pet's behavior in accordance with the pet owner's emotions, based on the learning results from the learning unit. For example, if the pet owner is dissatisfied with the pet's behavior, the service provider can provide behavior modification techniques to improve that behavior. This allows the service provider to provide advice to improve the pet's behavior. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the learning results from the learning unit into a generating AI and have the generating AI provide the advice.

[0066] The learning unit can learn the relationship between the pet owner's emotions and the behavior of multiple pets. For example, the learning unit can learn the pet owner's emotions and the behavioral patterns of multiple pets, and provide advice to improve the behavior of multiple pets in response to the pet owner's emotions. For example, the learning unit can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement advice in response to the pet owner's emotions. The learning unit can also learn the pet owner's emotions and the behavioral patterns of multiple pets over the long term and provide advice to improve behavior in response to the pet owner's emotions. For example, the learning unit can learn the behavioral patterns of multiple pets when they are together and provide appropriate training methods for behavioral improvement in response to the pet owner's emotions. This allows the learning unit to learn the behavior of multiple pets and provide more appropriate advice. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input pet owner emotion data and multiple pet behavioral data into a generating AI and have the generating AI perform the learning.

[0067] The service provider can provide advice to improve the behavior of multiple pets in accordance with the emotions estimated by the emotion estimation unit, based on the learning results from the learning unit. For example, the service provider can provide advice to improve the behavior of multiple pets in accordance with the emotions of the pet owner, based on the learning results from the learning unit. For example, the service provider can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement advice in accordance with the emotions of the pet owner. The service provider can also provide specific advice to improve the behavior of multiple pets in accordance with the emotions of the pet owner, based on the learning results from the learning unit. For example, the service provider can learn the behavioral patterns of multiple pets when they are together and provide appropriate behavioral improvement training methods in accordance with the emotions of the pet owner. This allows the service provider to provide advice to improve the behavior of multiple pets. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the learning results from the learning unit into a generating AI and have the generating AI provide the advice.

[0068] The acquisition unit can estimate the pet owner's emotions and adjust the timing of acquiring emotion estimation information based on the estimated emotions. For example, if the pet owner is stressed, the acquisition unit can increase the frequency of acquiring emotion estimation information and track emotional changes in real time. Conversely, if the pet owner is relaxed, the acquisition unit can decrease the frequency of acquiring emotion estimation information and acquire only the minimum necessary information. Furthermore, if the pet owner is in a hurry, the acquisition unit can acquire emotion estimation information intensively in a short time to quickly estimate emotions. This allows the timing of acquiring emotion estimation information to be adjusted according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the pet owner's facial expression data into the generative AI and have the generative AI acquire emotion estimation information.

[0069] The data acquisition unit can analyze the pet owner's past emotional history and select the optimal data acquisition method. For example, the data acquisition unit can identify time periods in the past when the pet owner felt anxious and focus on acquiring emotional estimation information during those times. The data acquisition unit can also analyze emotional changes in specific events or situations from the pet owner's past emotional history and select an acquisition method appropriate to that situation. Furthermore, based on the pet owner's past emotional history, the data acquisition unit can intensively acquire emotional estimation information during periods of intense emotional change. This allows for the selection of the optimal data acquisition method based on the pet owner's past emotional history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the pet owner's past emotional data into a generating AI and have the generating AI select the optimal data acquisition method.

[0070] The data acquisition unit can filter the data for emotion estimation based on the pet owner's current living situation and areas of interest. For example, if the pet owner is at work, the data acquisition unit will prioritize acquiring work-related stressors. Furthermore, if the pet owner is engrossed in a hobby, the data acquisition unit can prioritize acquiring emotion estimation information related to that hobby. Additionally, if the pet owner is spending time with family, the data acquisition unit can acquire emotion estimation information based on their relationship with family. This allows for filtering of emotion estimation information based on the pet owner's living situation and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the pet owner's living situation data into a generating AI and have the generating AI perform the filtering.

[0071] The acquisition unit can estimate the pet owner's emotions and determine the priority of emotion estimation information to acquire based on the estimated emotions. For example, if the pet owner is feeling anxious, the acquisition unit will prioritize acquiring information related to anxiety. It can also prioritize acquiring information to maintain joy if the pet owner is feeling happy. Furthermore, if the pet owner is feeling sad, the acquisition unit can prioritize acquiring information to alleviate that sadness. This allows the priority of emotion estimation information to be determined according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input the pet owner's emotion data into a generative AI and have the generative AI determine the priorities.

[0072] The acquisition unit can prioritize acquiring highly relevant information by considering the pet owner's geographical location when acquiring information for emotion estimation. For example, if the pet owner is at home, the acquisition unit will prioritize acquiring information about the area around their home. Furthermore, if the pet owner is traveling, the acquisition unit can prioritize acquiring information about their travel destination. Additionally, if the pet owner is at work, the acquisition unit can prioritize acquiring information about the area around their workplace. This allows the acquisition unit to acquire highly relevant information by considering the pet owner's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the pet owner's geographical location data into a generating AI and have the generating AI acquire highly relevant information.

[0073] The acquisition unit can analyze the pet owner's social media activity and acquire relevant information when acquiring information for emotion estimation. For example, if the pet owner posts about their pet's health on social media, the acquisition unit can acquire the content of that post. The acquisition unit can also acquire information related to stress if the pet owner mentions stress on social media. Furthermore, if the acquisition unit expresses joy on social media, it can acquire information related to that joy. In this way, relevant information can be acquired by analyzing the pet owner's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the pet owner's social media data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0074] The emotion estimation unit can estimate the pet owner's emotions and adjust the emotion estimation algorithm based on the estimated emotions. For example, if the pet owner is feeling anxious, the emotion estimation unit can estimate the emotions using an algorithm specialized for anxiety. It can also estimate the emotions if the pet owner is feeling happy, using an algorithm specialized for happiness. Furthermore, if the pet owner is feeling sad, it can estimate the emotions using an algorithm specialized for sadness. This allows the emotion estimation algorithm to be adjusted according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion estimation unit may be performed using AI, or not. For example, the emotion estimation unit can input the pet owner's emotion data into the generative AI and have the generative AI perform the algorithm adjustments.

[0075] The emotion estimation unit can improve its estimation accuracy by referring to the pet owner's past emotional data during emotion estimation. For example, the emotion estimation unit estimates the current emotion based on the pet owner's past emotional data. The emotion estimation unit can also analyze the pet owner's past emotional data to identify patterns of emotion change and improve estimation accuracy. Furthermore, the emotion estimation unit can optimize the emotion estimation algorithm using the pet owner's past emotional data. This allows for improved estimation accuracy by referring to the pet owner's past emotional data. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the pet owner's past emotional data into a generating AI and have the generating AI perform the improvement of estimation accuracy.

[0076] The emotion estimation unit can customize its estimation method based on the pet owner's lifestyle and areas of interest during emotion estimation. For example, if the pet owner is at work, the emotion estimation unit will prioritize estimating emotions related to work. Similarly, if the pet owner is engrossed in a hobby, the emotion estimation unit can prioritize estimating emotions related to that hobby. Furthermore, if the pet owner is spending time with family, the emotion estimation unit can prioritize estimating emotions based on their relationship with family. This allows for the customization of the emotion estimation method based on the pet owner's lifestyle and areas of interest. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input pet owner lifestyle data into a generating AI and have the generating AI perform the customization of the estimation method.

[0077] The emotion estimation unit can estimate the pet owner's emotions and adjust the display method of the emotion estimation results based on the estimated emotions. For example, if the pet owner is feeling anxious, the emotion estimation unit can provide a display method to alleviate that anxiety. It can also provide a display method to emphasize the joy if the pet owner is feeling happy. Furthermore, if the pet owner is feeling sad, the emotion estimation unit can provide a display method to alleviate that sadness. This allows the display method of the emotion estimation results to be adjusted according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion estimation unit may be performed using AI, or not. For example, the emotion estimation unit can input the pet owner's emotion data into the generative AI and have the generative AI adjust the display method.

[0078] The emotion estimation unit can improve its estimation accuracy by considering the pet owner's geographical location information during emotion estimation. For example, if the pet owner is at home, the emotion estimation unit will prioritize estimating the pet's emotions at home. Furthermore, if the pet owner is traveling, the emotion estimation unit can prioritize estimating the pet's emotions at their travel destination. Additionally, if the pet owner is at work, the emotion estimation unit can prioritize estimating the pet's emotions at work. This allows for improved emotion estimation accuracy by considering the pet owner's geographical location information. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the pet owner's geographical location data into a generating AI and have the generating AI perform the improvement of estimation accuracy.

[0079] The emotion estimation unit can improve its estimation accuracy by analyzing the pet owner's social media activity during emotion estimation. For example, if the pet owner posts about their pet's health on social media, the emotion estimation unit can estimate the pet owner's emotions based on the content of those posts. The emotion estimation unit can also estimate emotions related to stress if the pet owner mentions stress on social media. Furthermore, if the pet owner expresses joy on social media, the emotion estimation unit can estimate emotions related to that joy. This allows for improved emotion estimation accuracy by analyzing the pet owner's social media activity. Some or all of the above processing in the emotion estimation unit may be performed using AI, for example, or without AI. For example, the emotion estimation unit can input the pet owner's social media data into a generating AI and have the generating AI perform the task of improving estimation accuracy.

[0080] The service provider can estimate the pet owner's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the pet owner is feeling anxious, the service provider will provide advice in a way that provides reassurance. If the pet owner is feeling happy, the service provider can also provide advice in a way that helps maintain that happiness. Furthermore, if the pet owner is feeling sad, the service provider can provide advice in a way that alleviates that sadness. This allows the service provider to adjust the way advice is expressed according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the pet owner's emotion data into a generative AI and have the generative AI adjust the way the advice is expressed.

[0081] The service provider can select the most appropriate advice by referring to the pet owner's past emotional data when providing advice. For example, the service provider can provide advice that is best suited to the pet owner's current emotions based on their past emotional data. The service provider can also analyze the pet owner's past emotional data and provide advice based on patterns of emotional change. Furthermore, the service provider can use the pet owner's past emotional data to optimize the content of the advice. This allows the service provider to provide the most appropriate advice based on the pet owner's past emotional data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet owner's past emotional data into a generating AI and have the generating AI select the most appropriate advice.

[0082] The service provider can customize the advice given based on the pet owner's lifestyle and areas of interest. For example, if the pet owner is at work, the service provider can provide work-related advice. It can also provide advice related to a hobby if the pet owner is engrossed in one. Furthermore, if the pet owner is spending time with family, the service provider can provide advice based on their relationship with family. This allows for the customization of advice based on the pet owner's lifestyle and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input pet owner lifestyle data into a generating AI and have the generating AI customize the advice.

[0083] The service provider can estimate the pet owner's emotions and prioritize advice based on those emotions. For example, if the pet owner is feeling anxious, the service provider will prioritize advice to alleviate that anxiety. Similarly, if the pet owner is feeling happy, the service provider can prioritize advice to maintain that happiness. Furthermore, if the pet owner is feeling sad, the service provider can prioritize advice to alleviate that sadness. This allows the service provider to prioritize advice according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the pet owner's emotion data into a generative AI and have the generative AI determine the priority of advice.

[0084] The service provider can provide optimal advice by considering the pet owner's geographical location when providing advice. For example, if the pet owner is at home, the service provider can provide advice that can be implemented at home. If the pet owner is traveling, the service provider can also provide advice that can be implemented at their travel destination. Furthermore, if the pet owner is at work, the service provider can provide advice that can be implemented at work. This allows the service provider to provide optimal advice by considering the pet owner's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet owner's geographical location data into a generating AI and have the generating AI perform the task of providing optimal advice.

[0085] The service provider can analyze the pet owner's social media activity and adjust the advice accordingly. For example, if the pet owner posts about their pet's health on social media, the service provider can provide advice based on that post. It can also provide advice related to stress if the pet owner mentions stress on social media. Furthermore, if the pet owner expresses joy on social media, the service provider can provide advice on maintaining that joy. This allows the service provider to analyze the pet owner's social media activity and adjust the advice accordingly. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet owner's social media data into a generating AI and have the generating AI adjust the advice.

[0086] The learning unit can estimate the pet owner's emotions and select training data based on the estimated emotions. For example, if the pet owner is feeling anxious, the learning unit will prioritize learning data related to anxiety. It can also prioritize learning data to maintain joy if the pet owner is feeling happy. Furthermore, if the pet owner is feeling sad, the learning unit can prioritize learning data to alleviate that sadness. This allows for the selection of training data according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input the pet owner's emotion data into a generative AI and have the generative AI perform the selection of training data.

[0087] The learning unit can optimize its learning algorithm by referring to the pet owner's past emotional data during training. For example, the learning unit adjusts the learning algorithm based on the pet owner's past emotional data. The learning unit can also analyze the pet owner's past emotional data, identify patterns of emotional change, and optimize the learning algorithm. Furthermore, the learning unit can use the pet owner's past emotional data to improve the accuracy of the learning algorithm. This allows the learning algorithm to be optimized based on the pet owner's past emotional data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the pet owner's past emotional data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0088] The learning unit can estimate the pet owner's emotions and adjust the learning frequency based on the estimated emotions. For example, if the pet owner is feeling anxious, the learning unit can increase the learning frequency and learn the emotional changes in real time. Conversely, if the pet owner is relaxed, the learning unit can decrease the learning frequency and learn only the minimum necessary data. Furthermore, if the pet owner is in a hurry, the learning unit can acquire learning data intensively in a short time and learn quickly. This allows the learning frequency to be adjusted according to the pet owner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the pet owner's emotional data into the generative AI and have the generative AI adjust the learning frequency.

[0089] The learning unit can weight the learning data based on the pet owner's lifestyle and areas of interest during the learning process. For example, if the pet owner is at work, the learning unit will weight the data related to work during the learning process. Similarly, if the pet owner is engrossed in a hobby, the learning unit can weight the data related to that hobby during the learning process. Furthermore, if the pet owner is spending time with family, the learning unit can weight the data based on their relationship with family. This allows the learning data to be weighted based on the pet owner's lifestyle and areas of interest. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the pet owner's lifestyle data into a generating AI and have the generating AI perform the weighting of the learning data.

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

[0091] A pet care support robot system can be equipped with the ability to estimate the emotions of pet owners and monitor the pet's health based on those emotions. For example, if the pet owner is feeling anxious, the system can check the pet's health more frequently to ensure there are no abnormalities. If the pet owner is feeling happy, the system can confirm that the pet is in good health and provide that information to the owner. Furthermore, if the pet owner is feeling sad, the system can monitor the pet's health in detail and provide veterinary advice as needed. This allows the system to monitor the pet's health in accordance with the pet owner's emotions and take appropriate action.

[0092] A pet care support robot system can be equipped with the ability to estimate the emotions of pet owners and manage the pet's diet based on those emotions. For example, if the pet owner is feeling anxious, the system can review the pet's diet and provide a nutritionally balanced meal. If the pet owner is feeling happy, the system can provide the pet's favorite treats, strengthening the bond between pet and owner. Furthermore, if the pet owner is feeling sad, the system can meticulously check the pet's diet to ensure it contains the nutrients necessary for maintaining health. In this way, the system can manage the pet's diet according to the pet owner's emotions and support the pet's health.

[0093] The pet care support robot system can be equipped with the ability to estimate the pet owner's emotions and adjust the pet's exercise level based on those emotions. For example, if the pet owner is feeling anxious, it can increase the pet's exercise level to relieve stress. If the pet owner is feeling happy, it can also suggest an exercise plan that the pet can enjoy together. Furthermore, if the pet owner is feeling sad, it can appropriately adjust the pet's exercise level to support its health. In this way, the system can adjust the pet's exercise level according to the pet owner's emotions, thereby promoting both the pet's health and the owner's emotional well-being.

[0094] A pet care support robot system can be equipped with the ability to estimate the pet owner's emotions and customize the pet's training plan based on those emotions. For example, if the pet owner is feeling anxious, the system can revise the training plan and suggest training methods that will make the owner feel more at ease. If the pet owner is feeling happy, the system can also provide a training plan that the owner and pet can enjoy together. Furthermore, if the pet owner is feeling sad, the system can adjust the training plan and suggest training methods that will soothe the owner's feelings. This allows for the customization of the pet's training plan according to the pet owner's emotions, thereby maintaining a better relationship between the pet and the owner.

[0095] The pet care support robot system can be equipped with the ability to estimate the pet owner's emotions and adjust the pet's playtime based on those emotions. For example, if the pet owner is feeling anxious, the system can increase playtime with the pet to reduce the owner's stress. If the pet owner is feeling happy, the system can suggest games that the pet can enjoy playing with. Furthermore, if the pet owner is feeling sad, the system can increase the time spent with the pet to soothe the owner's feelings. In this way, the system can adjust the pet's playtime according to the pet owner's emotions, deepening the bond between the pet and the owner.

[0096] A pet care support robot system can be equipped with the ability to automatically adjust the pet care schedule based on the pet owner's lifestyle. For example, if the pet owner is busy, the system can shorten the pet care schedule to provide more efficient care. If the pet owner is on vacation, the system can suggest a schedule that allows for more time spent with the pet. Furthermore, if the pet owner is traveling, the system can arrange for a pet care service to be provided. This allows for adjusting the pet care schedule according to the pet owner's lifestyle, maintaining a balance between the pet's health and the owner's well-being.

[0097] A pet care support robot system can have the function to adjust pet care methods based on the pet owner's health condition. For example, if the pet owner is unwell, it can simplify pet care to reduce the owner's burden. If the pet owner is healthy, it can also suggest activities to do with the pet. Furthermore, if the pet owner is undergoing rehabilitation, it can provide a rehabilitation plan to be done with the pet. This allows for adjusting pet care methods according to the pet owner's health condition, reducing the owner's burden while maintaining the pet's health.

[0098] A pet care support robot system can be equipped with the ability to customize pet care methods based on the pet owner's living environment. For example, if the pet owner lives in an urban area, it can suggest pet care methods suitable for the urban environment. If the pet owner lives in the suburbs, it can provide pet care methods that utilize the natural environment. Furthermore, if the pet owner lives in an apartment building, it can suggest pet care methods that are considerate of neighbors. In this way, pet care methods can be customized according to the pet owner's living environment, improving the quality of life for both pets and owners.

[0099] The pet care support robot system can be equipped with a function to suggest activities for pets based on the pet owner's hobbies and interests. For example, if the pet owner enjoys outdoor activities, it can suggest outdoor activities that can be enjoyed with their pet. If the pet owner prefers indoor activities, it can also provide indoor activities that can be enjoyed with their pet. Furthermore, if the pet owner enjoys a particular sport, it can suggest activities related to that sport. This allows for the suggestion of activities for pets according to the pet owner's hobbies and interests, thereby deepening the bond between pets and their owners.

[0100] A pet care support robot system can have the ability to adjust pet care methods based on the pet owner's social media activity. For example, if a pet owner frequently posts about their pet's health on social media, the system can suggest health management methods based on those posts. It can also provide care methods related to the pet's behavior if the pet owner mentions it on social media. Furthermore, if the pet owner shares enjoyable moments with their pet on social media, the system can suggest care methods to maintain that enjoyment. This allows for the adjustment of pet care methods based on the pet owner's social media activity, improving the quality of life for both pets and their owners.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the emotions of the pet owner. The acquisition unit collects emotion estimation information such as the pet owner's facial expressions, tone of voice, and behavioral patterns. For example, the acquisition unit acquires the tone of voice and facial expressions when the pet owner is talking about the pet's health. The acquisition unit can also acquire the pet owner's social media posts and survey results as emotion estimation information. Step 2: The emotion estimation unit estimates the pet owner's emotions based on the emotion estimation information acquired by the acquisition unit. For example, the emotion estimation unit analyzes the collected information to estimate which emotions the pet owner is experiencing, such as anxiety, joy, or sadness. For example, if the tone of voice of the pet owner when talking about the pet's health indicates anxiety, the emotion estimation unit estimates that the pet owner is feeling anxious. The emotion estimation unit can also estimate emotions by analyzing the pet owner's facial expressions and behavioral patterns. Step 3: The service provider provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. For example, the service provider provides appropriate advice tailored to the pet owner's emotions. For instance, if the pet owner is worried about their pet's health, the service provider offers health management tips and stress reduction methods. The service provider can also provide advice on how to improve the pet owner's behavior if they are dissatisfied with it.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0104] 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.

[0105] 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.

[0106] For example, the acquisition unit can acquire the pet owner's facial expressions and voice tone using the camera 42 and microphone 38B of the smart device 14. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the pet owner's emotions by analyzing the information from the acquisition unit. The provision unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing device 12 and provides appropriate advice to the pet owner. The learning unit is implemented by the identification processing unit 290 of the data processing device 12 and learns the pet owner's emotions and the pet's behavior patterns and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] For example, the acquisition unit can acquire the pet owner's facial expressions and voice tone using the camera 42 and microphone 238 of the smart glasses 214. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the pet owner's emotions by analyzing the information from the acquisition unit. The provision unit is implemented by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing device 12 and provides appropriate advice to the pet owner. The learning unit is implemented by the identification processing unit 290 of the data processing device 12 and learns the pet owner's emotions and the pet's behavior patterns and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] For example, the acquisition unit can acquire the pet owner's facial expressions and voice tone using the camera 42 and microphone 238 of the headset terminal 314. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the pet owner's emotions by analyzing the information from the acquisition unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing device 12 and provides appropriate advice to the pet owner. The learning unit is implemented by the identification processing unit 290 of the data processing device 12 and learns the pet owner's emotions and the pet's behavior patterns and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] For example, the acquisition unit can acquire the pet owner's facial expressions and voice tone using the camera 42 and microphone 238 of the robot 414. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the pet owner's emotions by analyzing the information from the acquisition unit. The provision unit is implemented by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing device 12 and provides appropriate advice to the pet owner. The learning unit is implemented by the identification processing unit 290 of the data processing device 12 and learns the pet owner's emotions and the pet's behavior patterns and provides appropriate advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of pet owners, An emotion estimation unit estimates the emotions of the pet owner based on the emotion estimation information acquired by the acquisition unit, The system includes a provisioning unit that provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. A system characterized by the following features. (Note 2) The system includes a learning unit that learns the relationship between the pet owner's emotions and their behavior towards their pet. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the learning results from the learning unit, the unit provides advice to improve the pet's behavior in accordance with the emotions estimated by the emotion estimation unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned learning unit, This system learns the relationship between the emotions of the pet owner and the behavior of multiple pets. The system described in Appendix 2, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on the learning results from the learning unit, the emotion estimation unit provides advice to improve the behavior of the multiple pets in accordance with the emotions estimated by the emotion estimation unit. The system described in Appendix 2, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the pet owner's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, We analyze the pet owner's past emotional history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring information for emotion estimation, filtering is performed based on the pet owner's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the pet owner's emotions and prioritizes the emotion estimation information to be acquired based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring information for emotion estimation, the system prioritizes acquiring highly relevant information by considering the pet owner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring information for sentiment estimation, the social media activity of pet owners is analyzed to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The emotion estimation unit, It estimates the pet owner's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The emotion estimation unit, When estimating emotions, we refer to the pet owner's past emotional data to improve estimation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion estimation unit, When estimating emotions, the estimation method is customized based on the pet owner's living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion estimation unit, It estimates the pet owner's emotions and adjusts how the emotion estimation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion estimation unit, When estimating emotions, consider the pet owner's geographical location to improve estimation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 17) The emotion estimation unit, Analyzing pet owners' social media activity improves the accuracy of emotion estimation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the pet owner's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, the system selects the most appropriate advice by referring to the pet owner's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, we customize the content based on the pet owner's living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the pet owner's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we take the pet owner's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we analyze the pet owner's social media activity and adjust the advice accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The system estimates the emotions of pet owners and selects training data based on these estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the learning algorithm is optimized by referencing past emotional data of pet owners. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned learning unit, It estimates the pet owner's emotions and adjusts the learning frequency based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the training data is weighted based on the pet owner's lifestyle and areas of interest. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0175] 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. An acquisition unit that acquires emotion estimation information, which is information used to estimate the emotions of pet owners, An emotion estimation unit estimates the emotions of the pet owner based on the emotion estimation information acquired by the acquisition unit, The system includes a provisioning unit that provides advice to the pet owner based on the emotions estimated by the emotion estimation unit. A system characterized by the following features.

2. The system includes a learning unit that learns the relationship between the pet owner's emotions and their behavior towards their pet. The system according to feature 1.

3. The aforementioned supply unit is, Based on the learning results from the learning unit, the unit provides advice to improve the pet's behavior in accordance with the emotions estimated by the emotion estimation unit. The system according to feature 2.

4. The aforementioned learning unit, This system learns the relationship between the emotions of the pet owner and the behavior of multiple pets. The system according to feature 2.

5. The aforementioned supply unit is, Based on the learning results from the learning unit, the emotion estimation unit provides advice to improve the behavior of the multiple pets in accordance with the emotions estimated by the emotion estimation unit. The system according to feature 2.

6. The acquisition unit is, The system estimates the pet owner's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, We analyze the pet owner's past emotional history and select the optimal acquisition method. The system according to feature 1.

8. The acquisition unit is, When acquiring information for emotion estimation, filtering is performed based on the pet owner's current living situation and areas of interest. The system according to feature 1.

9. The acquisition unit is, The system estimates the pet owner's emotions and prioritizes the emotion estimation information to be acquired based on the estimated emotions. The system according to feature 1.

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