System

Abstract

An object of a system according to an embodiment is to improve communication with a pet by analyzing an emotion of the pet and providing the analyzed emotion to an owner.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection part collects data on the behavior, expression and voice of the pet. The analyzer analyzes the data collected by the collector, and determines the emotion of the pet. The providing unit provides the user with the emotion data determined by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately understand a pet's emotions and desires, posing challenges for communication with owners.

[0005] The system according to the embodiment aims to improve communication with pets by analyzing pet emotions and providing the information to owners. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data on the behavior, facial expression, and voice of the pet. The analysis unit analyzes the data collected by the collection unit and determines the pet's emotion. The provision unit provides the emotion data determined by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can improve communication with pets by analyzing pet emotions and providing the emotions to owners. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A pet emotion decoding system according to an embodiment of the present invention collects data such as a pet's behavior, facial expressions, and voice, analyzes it using AI to determine the pet's emotions, and provides the user with that information. The pet emotion decoding system collects data such as a pet's behavior, facial expressions, and voice, and analyzes it using AI to determine the pet's emotions, thereby providing the user with information about the pet's emotions. For example, the pet emotion decoding system collects detailed data such as a pet's behavior, facial expressions, and voice. Then, the pet emotion decoding system analyzes the collected data using AI to determine the pet's emotions. For example, the pet emotion decoding system determines whether the pet is happy, sad, excited, etc. The analyzed emotional data is then provided to the user. For example, an app can display a message such as "Your pet is happy right now," allowing the user to understand the pet's emotions. This makes it easier for the user to understand the pet's emotions and enriches communication with the pet. By understanding the pet's emotions and desires, the pet emotion decoding system can deepen the relationship between the owner and the pet and help them live a better life. For example, by responding appropriately when the pet is feeling stressed, the pet's health can be maintained. You can also spend fun time with your pet by playing with it when it is happy.

[0029] A pet emotion decoding system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data on the behavior, facial expressions, and voice of a pet. Examples of pet behavior include, but are not limited to, walking, eating, and playing. For example, the collection unit captures the behavior of the pet with a camera and saves the video data. The collection unit can also capture the facial expressions of the pet with a camera and save the video data. The collection unit can also record the voice of the pet with a microphone and save the audio data. For example, the collection unit can record the cries and barks of the pet and save the audio data. The analysis unit analyzes the data collected by the collection unit to determine the emotions of the pet. Examples of emotions include, but are not limited to, joy, anger, and sadness. For example, the analysis unit uses AI to analyze the collected data and determine the emotions of the pet. For example, the analysis unit can analyze the behavioral data of the pet and determine whether the pet is happy. The analysis unit can also analyze the facial expression data of the pet and determine whether the pet is angry. The analysis unit can also analyze the pet's voice data and determine whether the pet is sad. The providing unit provides the emotion data determined by the analysis unit to the user. The providing unit displays the emotion data to the user, for example, through an app. For example, the providing unit causes the app to display a message such as "Your pet is happy right now." The providing unit can also notify the user of the emotion data by voice. For example, the providing unit uses a voice assistant to play a message such as "Your pet is sad right now." In this way, the pet emotion decoding system according to the embodiment can decode the pet's emotions and provide the user with the emotion data, thereby enriching communication with the pet and helping the user build a deep bond with the pet.

[0030] The providing unit can notify the user of the pet's desires. For example, the providing unit notifies the user of the pet's desires. Desires include, but are not limited to, eating, walking, and playing. For example, the providing unit notifies the user of the desires through an app. For example, the providing unit displays a message such as "Your pet wants to eat now" through the app. The providing unit can also notify the user of the desires by voice. For example, the providing unit plays a message such as "Your pet wants to go for a walk now" using a voice assistant. This notifies the user of the pet's desires, thereby improving the pet's satisfaction and deepening the bond with its owner. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the pet's desire data into a generating AI and cause the generating AI to notify the desires.

[0031] The collection unit can estimate the pet's emotions and adjust the timing of data collection based on the estimated pet's emotions. For example, the collection unit collects data when the pet is relaxed to avoid stress. For example, the collection unit collects data when the pet is relaxed and prioritizes the pet's natural behavior. The collection unit can also collect data when the pet is excited to capture emotional peaks. For example, the collection unit collects data when the pet is excited and records emotional changes in detail. The collection unit can also refrain from collecting data when the pet is sleeping and prioritize natural behavior. For example, the collection unit refrains from collecting data when the pet is sleeping and prioritizes the pet's rest. This allows for more accurate data collection by adjusting the timing of data collection based on the pet's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotional data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0032] The collection unit can analyze the pet's past behavioral history and select an appropriate data collection method. For example, if the pet was active during a specific time period in the past, the collection unit collects data during that time period. For example, if the pet was active during a specific time period in the past, the collection unit collects data during that time period to understand the pet's behavioral patterns. Furthermore, if the pet often plays in a specific location, the collection unit can prioritize data collection at that location. For example, if the pet often plays in a specific location, the collection unit prioritizes data collection at that location and records the pet's behavior in detail. Furthermore, if the pet repeatedly performs a specific behavior, the collection unit can collect data during that behavior. For example, if the pet repeatedly performs a specific behavior, the collection unit collects data during that behavior and records changes in the behavior. This allows the pet's past behavioral history to be analyzed to select an optimal data collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the pet's past behavioral data into the generation AI and cause the generation AI to select a data collection method.

[0033] When collecting data, the collection unit can filter the data based on the pet's current health condition and activity level. For example, the collection unit collects data when the pet is healthy and active and excludes abnormal data. For example, the collection unit collects data when the pet is healthy and active and records the pet's normal behavior. The collection unit can also refrain from collecting data when the pet is tired and prioritize the pet's health condition. For example, the collection unit refrains from collecting data when the pet is tired and prioritizes the pet's rest. The collection unit can also collect data when the pet is sick and monitor changes in the pet's health condition. For example, the collection unit collects data when the pet is sick and records changes in the pet's health condition in detail. This allows for more accurate data to be collected by filtering the data based on the pet's health condition and activity level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the pet's health data to the generation AI and have the generation AI perform data filtering.

[0034] When collecting data, the collection unit can select an appropriate collection means depending on the type and age of the pet. For example, the collection unit uses an active data collection means for young pets. For example, the collection unit uses an active data collection means for young pets and records the pet's active behavior. The collection unit can also use a less burdensome data collection means for older pets. For example, the collection unit uses a less burdensome data collection means for older pets and prioritizes the pet's health. The collection unit can also use a data collection means suited to the characteristics of a specific type of pet. For example, the collection unit uses a data collection means suited to the characteristics of a specific type of pet and records the pet's behavior in detail. This allows more appropriate data to be collected by selecting the optimal collection means depending on the type and age of the pet. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input pet type and age data into the generation AI and have the generation AI select the collection means.

[0035] The collection unit can estimate the pet's emotions and determine the priority of data to be collected based on the estimated pet's emotions. For example, the collection unit prioritizes collecting data related to play when the pet is happy. For example, when the pet is happy, the collection unit prioritizes collecting data related to play and records the pet's enjoyment. The collection unit can also prioritize collecting data related to the environment when the pet is anxious. For example, when the pet is anxious, the collection unit prioritizes collecting data related to the environment and records the pet's sense of security. The collection unit can also prioritize collecting data related to food when the pet is hungry. For example, when the pet is hungry, the collection unit prioritizes collecting data related to food and records the pet's satisfaction. In this way, by determining the priority of data to be collected based on the pet's emotions, more important data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the pet's emotional data to the generation AI and cause the generation AI to determine the priority of the data.

[0036] When collecting data, the collection unit can prioritize collection of highly relevant data based on the pet's living environment information. For example, the collection unit collects data related to indoor temperature and humidity when the pet is indoors. For example, the collection unit collects data related to indoor temperature and humidity when the pet is indoors and records the pet's comfort. The collection unit can also collect data related to weather and temperature when the pet is outdoors. For example, the collection unit collects data related to weather and temperature when the pet is outdoors and records the pet's activities. The collection unit can also collect environmental data of a room when the pet is in a specific room. For example, the collection unit collects environmental data of the room when the pet is in a specific room and records the pet's behavior in detail. This allows for the collection of more useful data by prioritized collection of highly relevant data based on the pet's living environment information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the pet's living environment data to the generation AI and cause the generation AI to collect highly relevant data.

[0037] The collection unit can analyze the social media activities of the pet owner during data collection and collect related data. For example, when the owner posts a photo of the pet on social media, the collection unit collects behavioral data of the pet at that time. For example, when the owner posts a photo of the pet on social media, the collection unit collects behavioral data of the pet at that time and records the pet's behavior in detail. The collection unit can also collect data related to the content of a post made by the owner about the pet. For example, when the owner posts a post about the pet, the collection unit collects data related to the content and records the pet's behavior. The collection unit can also collect health data of the pet at that time when the owner shares information about the pet's health. For example, when the owner shares information about the pet's health, the collection unit collects health data of the pet at that time and records the pet's health condition. In this way, related data can be collected by analyzing the social media activities of the pet owner. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the owner's social media data into the generation AI and cause the generation AI to collect related data.

[0038] The collection unit can customize the collection method by reflecting the pet's past feedback when collecting data. For example, the collection unit avoids collection methods that the pet disliked in the past and tries other methods. For example, the collection unit avoids collection methods that the pet disliked in the past to reduce the pet's stress. The collection unit can also prioritize collection methods that the pet preferred in the past. For example, the collection unit prioritizes the pet's comfort by using collection methods that the pet preferred in the past. The collection unit can also fine-tune the collection method based on the pet's past reactions. For example, the collection unit fine-tunes the collection method based on the pet's past reactions and records the pet's behavior in detail. In this way, the collection method can be customized by reflecting the pet's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the pet's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0039] The analysis unit can estimate the pet's emotions and adjust the way the analysis is presented based on the estimated pet's emotions. For example, if the pet is relaxed, the analysis unit displays the analysis results in a calm expression. For example, if the pet is relaxed, the analysis unit displays the analysis results in a calm expression to reflect the pet's emotions. Furthermore, if the pet is excited, the analysis unit can display the analysis results in a lively expression. For example, if the pet is excited, the analysis unit displays the analysis results in a lively expression to reflect the pet's emotions. Furthermore, if the pet is feeling anxious, the analysis unit can display the analysis results in an expression that gives a sense of security. For example, if the pet is feeling anxious, the analysis unit displays the analysis results in an expression that gives a sense of security to reflect the pet's emotions. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the pet's emotion data into the generation AI and cause the generation AI to adjust the way the analysis is presented.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's behavioral data. For example, the analysis unit performs a detailed analysis on important behavioral data. For example, the analysis unit performs a detailed analysis on important behavioral data and records the pet's behavior in detail. The analysis unit can also perform a simplified analysis on general behavioral data. For example, the analysis unit performs a simplified analysis on general behavioral data and records the pet's behavior. The analysis unit can also perform a focused analysis on specific behavioral data. For example, the analysis unit performs a focused analysis on specific behavioral data and records the pet's behavior in detail. In this way, by adjusting the level of detail of the analysis based on the importance of the pet's behavioral data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's behavioral data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. For example, the analysis unit applies an algorithm that analyzes active behavior to young pets. For example, the analysis unit applies an algorithm that analyzes active behavior to young pets and records the pet's active behavior. The analysis unit can also apply an analysis algorithm that emphasizes health status to older pets. For example, the analysis unit applies an analysis algorithm that emphasizes health status to older pets and records the pet's health status. The analysis unit can also apply an analysis algorithm that suits the characteristics of a specific type of pet. For example, the analysis unit applies an analysis algorithm that suits the characteristics of a specific type of pet and records the pet's behavior in detail. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the type and age of the pet. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet. The analysis unit, for example, corrects the current analysis result based on the past analysis result. For example, the analysis unit corrects the current analysis result based on the past analysis result and records the pet's behavior in detail. The analysis unit can also detect abnormal data by comparing it with past analysis results. For example, the analysis unit detects abnormal data by comparing it with past analysis results and records the pet's behavior in detail. The analysis unit can also learn from past analysis results and improve the analysis algorithm. For example, the analysis unit learns from past analysis results, improves the analysis algorithm, and records the pet's behavior in detail. In this way, the accuracy of the analysis is improved by referring to the pet's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated pet's emotions. For example, when the pet is relaxed, the analysis unit performs a detailed analysis. For example, when the pet is relaxed, the analysis unit performs a detailed analysis and records the pet's behavior in detail. The analysis unit can also perform a short analysis when the pet is excited. For example, when the pet is excited, the analysis unit performs a short analysis and records the pet's behavior. The analysis unit can also perform a quick analysis when the pet is anxious. For example, when the pet is anxious, the analysis unit performs a quick analysis and records the pet's behavior. This allows for adjusting the length of the analysis based on the pet's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on when the pet behavioral data was collected. The analysis unit, for example, prioritizes analysis of the most recent behavioral data. For example, the analysis unit prioritizes analysis of the most recent behavioral data and records the pet's behavior in detail. The analysis unit can also prioritize analysis of data collected during a specific time period. For example, the analysis unit prioritizes analysis of data collected during a specific time period and records the pet's behavior. The analysis unit can also prioritize analysis of abnormal data compared with past data. For example, the analysis unit prioritizes analysis of abnormal data compared with past data and records the pet's behavior in detail. In this way, by determining the analysis priority based on when the pet behavioral data was collected, more important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's behavioral data to a generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the pet's behavioral data. The analysis unit, for example, prioritizes analysis of important behavioral data. For example, the analysis unit prioritizes analysis of important behavioral data and records the pet's behavior in detail. The analysis unit can also analyze highly related behavioral data collectively. For example, the analysis unit analyzes highly related behavioral data collectively and records the pet's behavior in detail. The analysis unit can also sequentially analyze related data based on specific behavioral data. For example, the analysis unit sequentially analyzes related data based on specific behavioral data and records the pet's behavior in detail. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the pet's behavioral data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's behavioral data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the pet's health condition. For example, the analysis unit provides analysis results that use a lot of technical terms to healthy pets. For example, the analysis unit provides analysis results that use a lot of technical terms to healthy pets and records the pet's behavior in detail. The analysis unit can also provide concise and easy-to-understand analysis results to pets in poor health. For example, the analysis unit provides concise and easy-to-understand analysis results to pets in poor health and records the pet's behavior. The analysis unit can also adjust the frequency of use of technical terms according to the pet's health condition. For example, the analysis unit adjusts the frequency of use of technical terms according to the pet's health condition and records the pet's behavior in detail. In this way, by adjusting the use of technical terms in the analysis according to the pet's health condition, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data to the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The providing unit can estimate the pet's emotions and adjust the way the information is presented based on the estimated pet's emotions. For example, if the pet is relaxed, the providing unit provides the information using calm expressions. For example, if the pet is relaxed, the providing unit provides the information using calm expressions to reflect the pet's emotions. Furthermore, if the pet is excited, the providing unit can provide the information using lively expressions. For example, if the pet is excited, the providing unit provides the information using lively expressions to reflect the pet's emotions. Furthermore, if the pet is feeling anxious, the providing unit can provide the information using expressions that give a sense of security. For example, if the pet is feeling anxious, the providing unit provides the information using expressions that give a sense of security to reflect the pet's emotions. In this way, by adjusting the way the information is presented based on the pet's emotions, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the way the information is presented.

[0048] The providing unit can adjust the level of detail of the provided information based on the importance of the pet's emotional data when providing the information. The providing unit, for example, provides detailed information for important emotional data. For example, the providing unit provides detailed information for important emotional data and records the pet's emotions in detail. The providing unit can also provide simplified information for general emotional data. For example, the providing unit provides simplified information for general emotional data and records the pet's emotions. The providing unit can also provide focused information for specific emotional data. For example, the providing unit provides focused information for specific emotional data and records the pet's emotions in detail. In this way, by adjusting the level of detail of the provided information based on the importance of the pet's emotional data, more important information can be provided in detail. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the level of detail of the provided information.

[0049] The providing unit can apply different providing algorithms depending on the type and age of the pet when providing the information. For example, the providing unit applies an algorithm that provides active information to young pets. For example, the providing unit applies an algorithm that provides active information to young pets and records the pet's active behavior. The providing unit can also apply an algorithm that provides information that emphasizes the health status to older pets. For example, the providing unit applies an algorithm that provides information that emphasizes the health status to older pets and records the pet's health status. The providing unit can also apply an algorithm that provides information tailored to the characteristics of a specific type of pet. For example, the providing unit applies an algorithm that provides information tailored to the characteristics of a specific type of pet and records the pet's behavior in detail. This allows more appropriate information to be provided by applying different providing algorithms depending on the type and age of the pet. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the providing algorithm.

[0050] The providing unit can improve the accuracy of the data provided by referring to the pet's past provision results when providing the data. The providing unit, for example, corrects the current provision result based on the past provision results. For example, the providing unit corrects the current provision result based on the past provision results and records the pet's emotions in detail. The providing unit can also detect abnormal data by comparing the data with the past provision results. For example, the providing unit detects abnormal data by comparing the data with the past provision results and records the pet's emotions in detail. The providing unit can also learn from the past provision results and improve the providing algorithm. For example, the providing unit learns from the past provision results, improves the providing algorithm, and records the pet's emotions in detail. This improves the accuracy of the data provided by referring to the pet's past provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's past provision data into the generation AI and cause the generation AI to improve the accuracy of the data provided.

[0051] The providing unit can estimate the pet's emotions and adjust the length of the information to be provided based on the estimated pet's emotions. For example, when the pet is relaxed, the providing unit provides detailed information. For example, when the pet is relaxed, the providing unit provides detailed information and records the pet's emotions in detail. Furthermore, when the pet is excited, the providing unit can provide short, concise information. For example, when the pet is excited, the providing unit provides short, concise information and records the pet's emotions. Furthermore, when the pet is anxious, the providing unit can quickly provide information. For example, when the pet is anxious, the providing unit quickly provides information and records the pet's emotions. In this way, by adjusting the length of the information to be provided based on the pet's emotions, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the length of the information.

[0052] The providing unit can determine the priority of providing the pet's emotional data based on when the data was collected. The providing unit, for example, prioritizes providing the most recent emotional data. For example, the providing unit prioritizes providing the most recent emotional data and records the pet's emotions in detail. The providing unit can also prioritize providing emotional data collected during a specific time period. For example, the providing unit prioritizes providing emotional data collected during a specific time period and records the pet's emotions. The providing unit can also prioritize providing abnormal emotional data compared to past data. For example, the providing unit prioritizes providing abnormal emotional data compared to past data and records the pet's emotions in detail. In this way, by determining the priority of providing the pet's emotional data based on when the data was collected, more important information can be provided preferentially. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's emotional data to the generation AI and have the generation AI determine the priority of providing the data.

[0053] The providing unit can adjust the order of providing the data based on the relevance of the pet's emotional data when providing the data. The providing unit, for example, prioritizes providing important emotional data. For example, the providing unit prioritizes providing important emotional data and records the pet's emotions in detail. The providing unit can also provide highly related emotional data collectively. For example, the providing unit collectively provides highly related emotional data and records the pet's emotions in detail. The providing unit can also sequentially provide related data based on specific emotional data. For example, the providing unit sequentially provides related data based on specific emotional data and records the pet's emotions in detail. This allows information to be provided more efficiently by adjusting the order of providing based on the relevance of the pet's emotional data. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the order of providing.

[0054] The providing unit can adjust the use of technical terms provided in accordance with the pet's health condition at the time of providing the information. For example, the providing unit provides information that uses a lot of technical terms to a healthy pet. For example, the providing unit provides information that uses a lot of technical terms to a healthy pet and records the pet's emotions in detail. The providing unit can also provide concise and easy-to-understand information to a pet in poor health. For example, the providing unit provides concise and easy-to-understand information to a pet in poor health and records the pet's emotions. The providing unit can also adjust the frequency of use of technical terms in accordance with the pet's health condition. For example, the providing unit adjusts the frequency of use of technical terms in accordance with the pet's health condition and records the pet's emotions in detail. In this way, by adjusting the use of technical terms provided in accordance with the pet's health condition, more understandable information can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the pet's health data into a generation AI and cause the generation AI to adjust the use of technical terms.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can analyze the pet's past behavioral history and select an appropriate data collection method. For example, if the pet was active during a specific time period in the past, data can be collected during that time period. Also, if the pet often plays in a specific place, data collection in that place can be prioritized. Furthermore, if the pet repeatedly performs a specific behavior, data can be collected during that behavior. In this way, the optimal data collection method can be selected by analyzing the pet's past behavioral history.

[0057] When collecting data, the collection unit can filter the data based on the pet's current health condition and activity level. For example, data can be collected when the pet is healthy and active, and abnormal data can be excluded. Data collection can also be refrained from when the pet is tired, prioritizing the pet's health condition. Furthermore, data can be collected when the pet is sick to monitor changes in the pet's health condition. This allows for more accurate data collection by filtering data based on the pet's health condition and activity level.

[0058] When collecting data, the collection unit can select an appropriate collection method depending on the type and age of the pet. For example, an active data collection method can be used for young pets. Alternatively, a less burdensome data collection method can be used for older pets. Furthermore, a data collection method suited to the characteristics of a particular type of pet can be used. This allows more appropriate data to be collected by selecting the optimal collection method depending on the type and age of the pet.

[0059] When collecting data, the collection unit can prioritize collection of highly relevant data based on the pet's living environment information. For example, when the pet is indoors, data on indoor temperature and humidity can be collected. When the pet is outdoors, data on weather and temperature can also be collected. Furthermore, when the pet is in a specific room, environmental data for that room can be collected. In this way, by preferentially collecting highly relevant data based on the pet's living environment information, more useful data can be collected.

[0060] When collecting data, the collection unit can analyze the social media activities of pet owners and collect related data. For example, when a pet owner posts a photo of their pet on social media, the collection unit can collect data on the pet's behavior at that time. Also, when a pet owner posts something about their pet, the collection unit can collect data related to the content of that post. Furthermore, when a pet owner shares information about their pet's health, the collection unit can collect data on the pet's health at that time. In this way, by analyzing the social media activities of pet owners, related data can be collected.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects data on the pet's behavior, facial expressions, and voice. Pet behavior includes, for example, walking, eating, playing, etc. The collection unit captures the pet's behavior with a camera and saves it as video data. The collection unit can also capture the pet's facial expressions with a camera and save them as image data. Furthermore, the pet's voice can be recorded with a microphone and saved as voice data. For example, the pet's cries and barks can be recorded and saved as voice data. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the pet's emotions. Emotions include, for example, joy, anger, and sadness. The analysis unit uses AI to analyze the collected data and determine the pet's emotions. For example, it can analyze the pet's behavior data and determine whether the pet is happy. It can also analyze the pet's facial expression data and determine whether the pet is angry. It can also analyze the pet's voice data and determine whether the pet is sad. Step 3: The providing unit provides the emotion data determined by the analyzing unit to the user. The providing unit displays the emotion data to the user through an app. For example, the app displays a message such as "Your pet is happy right now." The providing unit can also notify the user of the emotion data by voice. For example, the app may use a voice assistant to play a message such as "Your pet is sad right now."

[0063] (Example 2) A pet emotion decoding system according to an embodiment of the present invention collects data such as a pet's behavior, facial expressions, and voice, analyzes it using AI to determine the pet's emotions, and provides the user with that information. The pet emotion decoding system collects data such as a pet's behavior, facial expressions, and voice, and analyzes it using AI to determine the pet's emotions, thereby providing the user with information about the pet's emotions. For example, the pet emotion decoding system collects detailed data such as a pet's behavior, facial expressions, and voice. Then, the pet emotion decoding system analyzes the collected data using AI to determine the pet's emotions. For example, the pet emotion decoding system determines whether the pet is happy, sad, excited, etc. The analyzed emotional data is then provided to the user. For example, an app can display a message such as "Your pet is happy right now," allowing the user to understand the pet's emotions. This makes it easier for the user to understand the pet's emotions and enriches communication with the pet. By understanding the pet's emotions and desires, the pet emotion decoding system can deepen the relationship between the owner and the pet and help them live a better life. For example, by responding appropriately when the pet is feeling stressed, the pet's health can be maintained. You can also spend fun time with your pet by playing with it when it is happy.

[0064] A pet emotion decoding system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data on the behavior, facial expressions, and voice of a pet. Examples of pet behavior include, but are not limited to, walking, eating, and playing. For example, the collection unit captures the behavior of the pet with a camera and saves the video data. The collection unit can also capture the facial expressions of the pet with a camera and save the video data. The collection unit can also record the voice of the pet with a microphone and save the audio data. For example, the collection unit can record the cries and barks of the pet and save the audio data. The analysis unit analyzes the data collected by the collection unit to determine the emotions of the pet. Examples of emotions include, but are not limited to, joy, anger, and sadness. For example, the analysis unit uses AI to analyze the collected data and determine the emotions of the pet. For example, the analysis unit can analyze the behavioral data of the pet and determine whether the pet is happy. The analysis unit can also analyze the facial expression data of the pet and determine whether the pet is angry. The analysis unit can also analyze the pet's voice data and determine whether the pet is sad. The providing unit provides the emotion data determined by the analysis unit to the user. The providing unit displays the emotion data to the user, for example, through an app. For example, the providing unit causes the app to display a message such as "Your pet is happy right now." The providing unit can also notify the user of the emotion data by voice. For example, the providing unit uses a voice assistant to play a message such as "Your pet is sad right now." In this way, the pet emotion decoding system according to the embodiment can decode the pet's emotions and provide the user with the emotion data, thereby enriching communication with the pet and helping the user build a deep bond with the pet.

[0065] The providing unit can notify the user of the pet's desires. For example, the providing unit notifies the user of the pet's desires. Desires include, but are not limited to, eating, walking, and playing. For example, the providing unit notifies the user of the desires through an app. For example, the providing unit displays a message such as "Your pet wants to eat now" through the app. The providing unit can also notify the user of the desires by voice. For example, the providing unit plays a message such as "Your pet wants to go for a walk now" using a voice assistant. This notifies the user of the pet's desires, thereby improving the pet's satisfaction and deepening the bond with its owner. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the pet's desire data into a generating AI and cause the generating AI to notify the desires.

[0066] The collection unit can estimate the pet's emotions and adjust the timing of data collection based on the estimated pet's emotions. For example, the collection unit collects data when the pet is relaxed to avoid stress. For example, the collection unit collects data when the pet is relaxed and prioritizes the pet's natural behavior. The collection unit can also collect data when the pet is excited to capture emotional peaks. For example, the collection unit collects data when the pet is excited and records emotional changes in detail. The collection unit can also refrain from collecting data when the pet is sleeping and prioritize natural behavior. For example, the collection unit refrains from collecting data when the pet is sleeping and prioritizes the pet's rest. This allows for more accurate data collection by adjusting the timing of data collection based on the pet's emotions. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotional data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0067] The collection unit can analyze the pet's past behavioral history and select an appropriate data collection method. For example, if the pet was active during a specific time period in the past, the collection unit collects data during that time period. For example, if the pet was active during a specific time period in the past, the collection unit collects data during that time period to understand the pet's behavioral patterns. Furthermore, if the pet often plays in a specific location, the collection unit can prioritize data collection at that location. For example, if the pet often plays in a specific location, the collection unit prioritizes data collection at that location and records the pet's behavior in detail. Furthermore, if the pet repeatedly performs a specific behavior, the collection unit can collect data during that behavior. For example, if the pet repeatedly performs a specific behavior, the collection unit collects data during that behavior and records changes in the behavior. This allows the pet's past behavioral history to be analyzed to select an optimal data collection method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the pet's past behavioral data into the generation AI and cause the generation AI to select a data collection method.

[0068] When collecting data, the collection unit can filter the data based on the pet's current health condition and activity level. For example, the collection unit collects data when the pet is healthy and active and excludes abnormal data. For example, the collection unit collects data when the pet is healthy and active and records the pet's normal behavior. The collection unit can also refrain from collecting data when the pet is tired and prioritize the pet's health condition. For example, the collection unit refrains from collecting data when the pet is tired and prioritizes the pet's rest. The collection unit can also collect data when the pet is sick and monitor changes in the pet's health condition. For example, the collection unit collects data when the pet is sick and records changes in the pet's health condition in detail. This allows for more accurate data to be collected by filtering the data based on the pet's health condition and activity level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the pet's health data to the generation AI and have the generation AI perform data filtering.

[0069] When collecting data, the collection unit can select an appropriate collection means depending on the type and age of the pet. For example, the collection unit uses an active data collection means for young pets. For example, the collection unit uses an active data collection means for young pets and records the pet's active behavior. The collection unit can also use a less burdensome data collection means for older pets. For example, the collection unit uses a less burdensome data collection means for older pets and prioritizes the pet's health. The collection unit can also use a data collection means suited to the characteristics of a specific type of pet. For example, the collection unit uses a data collection means suited to the characteristics of a specific type of pet and records the pet's behavior in detail. This allows more appropriate data to be collected by selecting the optimal collection means depending on the type and age of the pet. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input pet type and age data into the generation AI and have the generation AI select the collection means.

[0070] The collection unit can estimate the pet's emotions and determine the priority of data to be collected based on the estimated pet's emotions. For example, the collection unit prioritizes collecting data related to play when the pet is happy. For example, when the pet is happy, the collection unit prioritizes collecting data related to play and records the pet's enjoyment. The collection unit can also prioritize collecting data related to the environment when the pet is anxious. For example, when the pet is anxious, the collection unit prioritizes collecting data related to the environment and records the pet's sense of security. The collection unit can also prioritize collecting data related to food when the pet is hungry. For example, when the pet is hungry, the collection unit prioritizes collecting data related to food and records the pet's satisfaction. In this way, by determining the priority of data to be collected based on the pet's emotions, more important data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the pet's emotional data to the generation AI and cause the generation AI to determine the priority of the data.

[0071] When collecting data, the collection unit can prioritize collection of highly relevant data based on the pet's living environment information. For example, the collection unit collects data related to indoor temperature and humidity when the pet is indoors. For example, the collection unit collects data related to indoor temperature and humidity when the pet is indoors and records the pet's comfort. The collection unit can also collect data related to weather and temperature when the pet is outdoors. For example, the collection unit collects data related to weather and temperature when the pet is outdoors and records the pet's activities. The collection unit can also collect environmental data of a room when the pet is in a specific room. For example, the collection unit collects environmental data of the room when the pet is in a specific room and records the pet's behavior in detail. This allows for the collection of more useful data by prioritized collection of highly relevant data based on the pet's living environment information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the pet's living environment data to the generation AI and cause the generation AI to collect highly relevant data.

[0072] The collection unit can analyze the social media activities of the pet owner during data collection and collect related data. For example, when the owner posts a photo of the pet on social media, the collection unit collects behavioral data of the pet at that time. For example, when the owner posts a photo of the pet on social media, the collection unit collects behavioral data of the pet at that time and records the pet's behavior in detail. The collection unit can also collect data related to the content of a post made by the owner about the pet. For example, when the owner posts a post about the pet, the collection unit collects data related to the content and records the pet's behavior. The collection unit can also collect health data of the pet at that time when the owner shares information about the pet's health. For example, when the owner shares information about the pet's health, the collection unit collects health data of the pet at that time and records the pet's health condition. In this way, related data can be collected by analyzing the social media activities of the pet owner. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the owner's social media data into the generation AI and cause the generation AI to collect related data.

[0073] The collection unit can customize the collection method by reflecting the pet's past feedback when collecting data. For example, the collection unit avoids collection methods that the pet disliked in the past and tries other methods. For example, the collection unit avoids collection methods that the pet disliked in the past to reduce the pet's stress. The collection unit can also prioritize collection methods that the pet preferred in the past. For example, the collection unit prioritizes the pet's comfort by using collection methods that the pet preferred in the past. The collection unit can also fine-tune the collection method based on the pet's past reactions. For example, the collection unit fine-tunes the collection method based on the pet's past reactions and records the pet's behavior in detail. In this way, the collection method can be customized by reflecting the pet's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the pet's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0074] The analysis unit can estimate the pet's emotions and adjust the way the analysis is presented based on the estimated pet's emotions. For example, if the pet is relaxed, the analysis unit displays the analysis results in a calm expression. For example, if the pet is relaxed, the analysis unit displays the analysis results in a calm expression to reflect the pet's emotions. Furthermore, if the pet is excited, the analysis unit can display the analysis results in a lively expression. For example, if the pet is excited, the analysis unit displays the analysis results in a lively expression to reflect the pet's emotions. Furthermore, if the pet is feeling anxious, the analysis unit can display the analysis results in an expression that gives a sense of security. For example, if the pet is feeling anxious, the analysis unit displays the analysis results in an expression that gives a sense of security to reflect the pet's emotions. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the pet's emotion data into the generation AI and cause the generation AI to adjust the way the analysis is presented.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's behavioral data. For example, the analysis unit performs a detailed analysis on important behavioral data. For example, the analysis unit performs a detailed analysis on important behavioral data and records the pet's behavior in detail. The analysis unit can also perform a simplified analysis on general behavioral data. For example, the analysis unit performs a simplified analysis on general behavioral data and records the pet's behavior. The analysis unit can also perform a focused analysis on specific behavioral data. For example, the analysis unit performs a focused analysis on specific behavioral data and records the pet's behavior in detail. In this way, by adjusting the level of detail of the analysis based on the importance of the pet's behavioral data, more important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's behavioral data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the type and age of the pet. For example, the analysis unit applies an algorithm that analyzes active behavior to young pets. For example, the analysis unit applies an algorithm that analyzes active behavior to young pets and records the pet's active behavior. The analysis unit can also apply an analysis algorithm that emphasizes health status to older pets. For example, the analysis unit applies an analysis algorithm that emphasizes health status to older pets and records the pet's health status. The analysis unit can also apply an analysis algorithm that suits the characteristics of a specific type of pet. For example, the analysis unit applies an analysis algorithm that suits the characteristics of a specific type of pet and records the pet's behavior in detail. This allows for more appropriate analysis results to be provided by applying different analysis algorithms depending on the type and age of the pet. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the type and age of the pet into the generation AI and cause the generation AI to apply the analysis algorithm.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet. The analysis unit, for example, corrects the current analysis result based on the past analysis result. For example, the analysis unit corrects the current analysis result based on the past analysis result and records the pet's behavior in detail. The analysis unit can also detect abnormal data by comparing it with past analysis results. For example, the analysis unit detects abnormal data by comparing it with past analysis results and records the pet's behavior in detail. The analysis unit can also learn from past analysis results and improve the analysis algorithm. For example, the analysis unit learns from past analysis results, improves the analysis algorithm, and records the pet's behavior in detail. In this way, the accuracy of the analysis is improved by referring to the pet's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated pet's emotions. For example, when the pet is relaxed, the analysis unit performs a detailed analysis. For example, when the pet is relaxed, the analysis unit performs a detailed analysis and records the pet's behavior in detail. The analysis unit can also perform a short analysis when the pet is excited. For example, when the pet is excited, the analysis unit performs a short analysis and records the pet's behavior. The analysis unit can also perform a quick analysis when the pet is anxious. For example, when the pet is anxious, the analysis unit performs a quick analysis and records the pet's behavior. This allows for adjusting the length of the analysis based on the pet's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without AI. For example, the analysis unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0079] During analysis, the analysis unit can determine the analysis priority based on when the pet behavioral data was collected. The analysis unit, for example, prioritizes analysis of the most recent behavioral data. For example, the analysis unit prioritizes analysis of the most recent behavioral data and records the pet's behavior in detail. The analysis unit can also prioritize analysis of data collected during a specific time period. For example, the analysis unit prioritizes analysis of data collected during a specific time period and records the pet's behavior. The analysis unit can also prioritize analysis of abnormal data compared with past data. For example, the analysis unit prioritizes analysis of abnormal data compared with past data and records the pet's behavior in detail. In this way, by determining the analysis priority based on when the pet behavioral data was collected, more important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the pet's behavioral data to a generation AI and have the generation AI determine the analysis priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the pet's behavioral data. The analysis unit, for example, prioritizes analysis of important behavioral data. For example, the analysis unit prioritizes analysis of important behavioral data and records the pet's behavior in detail. The analysis unit can also analyze highly related behavioral data collectively. For example, the analysis unit analyzes highly related behavioral data collectively and records the pet's behavior in detail. The analysis unit can also sequentially analyze related data based on specific behavioral data. For example, the analysis unit sequentially analyzes related data based on specific behavioral data and records the pet's behavior in detail. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the pet's behavioral data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's behavioral data to the generation AI and cause the generation AI to adjust the order of analysis.

[0081] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the pet's health condition. For example, the analysis unit provides analysis results that use a lot of technical terms to healthy pets. For example, the analysis unit provides analysis results that use a lot of technical terms to healthy pets and records the pet's behavior in detail. The analysis unit can also provide concise and easy-to-understand analysis results to pets in poor health. For example, the analysis unit provides concise and easy-to-understand analysis results to pets in poor health and records the pet's behavior. The analysis unit can also adjust the frequency of use of technical terms according to the pet's health condition. For example, the analysis unit adjusts the frequency of use of technical terms according to the pet's health condition and records the pet's behavior in detail. In this way, by adjusting the use of technical terms in the analysis according to the pet's health condition, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the pet's health data to the generation AI and cause the generation AI to adjust the use of technical terms.

[0082] The providing unit can estimate the pet's emotions and adjust the way the information is presented based on the estimated pet's emotions. For example, if the pet is relaxed, the providing unit provides the information using calm expressions. For example, if the pet is relaxed, the providing unit provides the information using calm expressions to reflect the pet's emotions. Furthermore, if the pet is excited, the providing unit can provide the information using lively expressions. For example, if the pet is excited, the providing unit provides the information using lively expressions to reflect the pet's emotions. Furthermore, if the pet is feeling anxious, the providing unit can provide the information using expressions that give a sense of security. For example, if the pet is feeling anxious, the providing unit provides the information using expressions that give a sense of security to reflect the pet's emotions. In this way, by adjusting the way the information is presented based on the pet's emotions, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the way the information is presented.

[0083] The providing unit can adjust the level of detail of the provided information based on the importance of the pet's emotional data when providing the information. The providing unit, for example, provides detailed information for important emotional data. For example, the providing unit provides detailed information for important emotional data and records the pet's emotions in detail. The providing unit can also provide simplified information for general emotional data. For example, the providing unit provides simplified information for general emotional data and records the pet's emotions. The providing unit can also provide focused information for specific emotional data. For example, the providing unit provides focused information for specific emotional data and records the pet's emotions in detail. In this way, by adjusting the level of detail of the provided information based on the importance of the pet's emotional data, more important information can be provided in detail. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the level of detail of the provided information.

[0084] The providing unit can apply different providing algorithms depending on the type and age of the pet when providing the information. For example, the providing unit applies an algorithm that provides active information to young pets. For example, the providing unit applies an algorithm that provides active information to young pets and records the pet's active behavior. The providing unit can also apply an algorithm that provides information that emphasizes the health status to older pets. For example, the providing unit applies an algorithm that provides information that emphasizes the health status to older pets and records the pet's health status. The providing unit can also apply an algorithm that provides information tailored to the characteristics of a specific type of pet. For example, the providing unit applies an algorithm that provides information tailored to the characteristics of a specific type of pet and records the pet's behavior in detail. This allows more appropriate information to be provided by applying different providing algorithms depending on the type and age of the pet. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the type and age of the pet to the generation AI and cause the generation AI to apply the providing algorithm.

[0085] The providing unit can improve the accuracy of the data provided by referring to the pet's past provision results when providing the data. The providing unit, for example, corrects the current provision result based on the past provision results. For example, the providing unit corrects the current provision result based on the past provision results and records the pet's emotions in detail. The providing unit can also detect abnormal data by comparing the data with the past provision results. For example, the providing unit detects abnormal data by comparing the data with the past provision results and records the pet's emotions in detail. The providing unit can also learn from the past provision results and improve the providing algorithm. For example, the providing unit learns from the past provision results, improves the providing algorithm, and records the pet's emotions in detail. This improves the accuracy of the data provided by referring to the pet's past provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's past provision data into the generation AI and cause the generation AI to improve the accuracy of the data provided.

[0086] The providing unit can estimate the pet's emotions and adjust the length of the information to be provided based on the estimated pet's emotions. For example, when the pet is relaxed, the providing unit provides detailed information. For example, when the pet is relaxed, the providing unit provides detailed information and records the pet's emotions in detail. Furthermore, when the pet is excited, the providing unit can provide short, concise information. For example, when the pet is excited, the providing unit provides short, concise information and records the pet's emotions. Furthermore, when the pet is anxious, the providing unit can quickly provide information. For example, when the pet is anxious, the providing unit quickly provides information and records the pet's emotions. In this way, by adjusting the length of the information to be provided based on the pet's emotions, more appropriate information can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the pet's emotion data to the generation AI and cause the generation AI to adjust the length of the information.

[0087] The providing unit can determine the priority of providing the pet's emotional data based on when the data was collected. The providing unit, for example, prioritizes providing the most recent emotional data. For example, the providing unit prioritizes providing the most recent emotional data and records the pet's emotions in detail. The providing unit can also prioritize providing emotional data collected during a specific time period. For example, the providing unit prioritizes providing emotional data collected during a specific time period and records the pet's emotions. The providing unit can also prioritize providing abnormal emotional data compared to past data. For example, the providing unit prioritizes providing abnormal emotional data compared to past data and records the pet's emotions in detail. In this way, by determining the priority of providing the pet's emotional data based on when the data was collected, more important information can be provided preferentially. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the pet's emotional data to the generation AI and have the generation AI determine the priority of providing the data.

[0088] The providing unit can adjust the order of providing the data based on the relevance of the pet's emotional data when providing the data. The providing unit, for example, prioritizes providing important emotional data. For example, the providing unit prioritizes providing important emotional data and records the pet's emotions in detail. The providing unit can also provide highly related emotional data collectively. For example, the providing unit collectively provides highly related emotional data and records the pet's emotions in detail. The providing unit can also sequentially provide related data based on specific emotional data. For example, the providing unit sequentially provides related data based on specific emotional data and records the pet's emotions in detail. This allows information to be provided more efficiently by adjusting the order of providing based on the relevance of the pet's emotional data. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the pet's emotional data to the generation AI and cause the generation AI to adjust the order of providing.

[0089] The providing unit can adjust the use of technical terms provided in accordance with the pet's health condition at the time of providing the information. For example, the providing unit provides information that uses a lot of technical terms to a healthy pet. For example, the providing unit provides information that uses a lot of technical terms to a healthy pet and records the pet's emotions in detail. The providing unit can also provide concise and easy-to-understand information to a pet in poor health. For example, the providing unit provides concise and easy-to-understand information to a pet in poor health and records the pet's emotions. The providing unit can also adjust the frequency of use of technical terms in accordance with the pet's health condition. For example, the providing unit adjusts the frequency of use of technical terms in accordance with the pet's health condition and records the pet's emotions in detail. In this way, by adjusting the use of technical terms provided in accordance with the pet's health condition, more understandable information can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the pet's health data into a generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data on the pet's behavior, facial expressions, and voice using the camera 42 and microphone 38B of the smart device 14. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data on the pet's behavior, facial expressions, and voice. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze the collected data using AI to determine the pet's emotions. For example, the provision unit can be realized by the control unit 46A of the smart device 14 and provide the analyzed emotion data to the user via an app. Furthermore, the provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the emotion data using a voice assistant. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data on the pet's behavior, facial expressions, and voice using the camera 42 and microphone 238 of the smart glasses 214. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data on the pet's behavior, facial expressions, and voice. The analysis unit can also be realized by the specific processing unit 290 of the data processing device 12 and analyze the collected data using AI to determine the pet's emotions. The provision unit can also be realized by the control unit 46A of the smart glasses 214 and provide the analyzed emotion data to the user via an app. The provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the emotion data using a voice assistant. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data on the pet's behavior, facial expressions, and voice using the camera 42 and microphone 238 of the headset-type terminal 314. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data on the pet's behavior, facial expressions, and voice. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected data using AI to determine the pet's emotions. The provision unit can be realized, for example, by the control unit 46A of the headset-type terminal 314 and provide the analyzed emotion data to the user via an app. The provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the emotion data using a voice assistant. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data on the pet's behavior, facial expressions, and voice using the camera 42 and microphone 238 of the robot 414. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12 and collect data on the pet's behavior, facial expressions, and voice. The analysis unit can be realized, for example, by the specific processing unit 290 of the data processing device 12 and analyze the collected data using AI to determine the pet's emotions. The provision unit can be realized, for example, by the control unit 46A of the robot 414 and provide the analyzed emotion data to the user via an app. The provision unit can also be realized by the specific processing unit 290 of the data processing device 12 and notify the user of the emotion data using a voice assistant.

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

[0091] The analysis unit can estimate the pet's emotions and determine the analysis priority based on the estimated pet's emotions. For example, when the pet is happy, data related to play can be analyzed preferentially and the pet's enjoyment can be recorded. When the pet is feeling anxious, data related to the environment can be analyzed preferentially and the pet's sense of security can be recorded. Furthermore, when the pet is hungry, data related to food can be analyzed preferentially and the pet's satisfaction can be recorded. In this way, by determining the analysis priority based on the pet's emotions, more important data can be analyzed preferentially.

[0092] The providing unit can estimate the pet's emotions and adjust the way in which the information is presented based on the estimated pet's emotions. For example, if the pet is relaxed, the information can be presented in a calm manner to reflect the pet's emotions. If the pet is excited, the information can be presented in a lively manner. Furthermore, if the pet is anxious, the information can be presented in a reassuring manner. In this way, by adjusting the way in which the information is presented based on the pet's emotions, more appropriate information can be provided.

[0093] The collection unit can estimate the pet's emotions and adjust the timing of data collection based on the estimated pet's emotions. For example, data can be collected when the pet is relaxed to avoid stress. Data can also be collected when the pet is excited to capture the peak of the pet's emotions. Furthermore, data collection can be refrained from when the pet is sleeping, prioritizing natural behavior. This allows for more accurate data collection by adjusting the timing of data collection based on the pet's emotions.

[0094] The analysis unit can estimate the pet's emotions and adjust the way the analysis is presented based on the estimated pet's emotions. For example, if the pet is relaxed, the analysis results can be displayed in calm expressions. If the pet is excited, the analysis results can be displayed in lively expressions. Furthermore, if the pet is feeling anxious, the analysis results can be displayed in expressions that give a sense of security. In this way, by adjusting the way the analysis is presented based on the pet's emotions, more appropriate analysis results can be provided.

[0095] The providing unit can estimate the pet's emotions and adjust the length of the information to be provided based on the estimated pet's emotions. For example, if the pet is relaxed, detailed information can be provided. If the pet is excited, short, concise information can be provided. Furthermore, if the pet is anxious, information can be provided quickly. In this way, by adjusting the length of the information to be provided based on the pet's emotions, more appropriate information can be provided.

[0096] The collection unit can analyze the pet's past behavioral history and select an appropriate data collection method. For example, if the pet was active during a specific time period in the past, data can be collected during that time period. Also, if the pet often plays in a specific place, data collection in that place can be prioritized. Furthermore, if the pet repeatedly performs a specific behavior, data can be collected during that behavior. In this way, the optimal data collection method can be selected by analyzing the pet's past behavioral history.

[0097] When collecting data, the collection unit can filter the data based on the pet's current health condition and activity level. For example, data can be collected when the pet is healthy and active, and abnormal data can be excluded. Data collection can also be refrained from when the pet is tired, prioritizing the pet's health condition. Furthermore, data can be collected when the pet is sick to monitor changes in the pet's health condition. This allows for more accurate data collection by filtering data based on the pet's health condition and activity level.

[0098] When collecting data, the collection unit can select an appropriate collection method depending on the type and age of the pet. For example, an active data collection method can be used for young pets. Alternatively, a less burdensome data collection method can be used for older pets. Furthermore, a data collection method suited to the characteristics of a particular type of pet can be used. This allows more appropriate data to be collected by selecting the optimal collection method depending on the type and age of the pet.

[0099] When collecting data, the collection unit can prioritize collection of highly relevant data based on the pet's living environment information. For example, when the pet is indoors, data on indoor temperature and humidity can be collected. When the pet is outdoors, data on weather and temperature can also be collected. Furthermore, when the pet is in a specific room, environmental data for that room can be collected. In this way, by preferentially collecting highly relevant data based on the pet's living environment information, more useful data can be collected.

[0100] When collecting data, the collection unit can analyze the social media activities of pet owners and collect related data. For example, when a pet owner posts a photo of their pet on social media, the collection unit can collect data on the pet's behavior at that time. Also, when a pet owner posts something about their pet, the collection unit can collect data related to the content of that post. Furthermore, when a pet owner shares information about their pet's health, the collection unit can collect data on the pet's health at that time. In this way, by analyzing the social media activities of pet owners, related data can be collected.

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

[0102] Step 1: The collection unit collects data on the pet's behavior, facial expressions, and voice. Pet behavior includes, for example, walking, eating, playing, etc. The collection unit captures the pet's behavior with a camera and saves it as video data. The collection unit can also capture the pet's facial expressions with a camera and save them as image data. Furthermore, the pet's voice can be recorded with a microphone and saved as voice data. For example, the pet's cries and barks can be recorded and saved as voice data. Step 2: The analysis unit analyzes the data collected by the collection unit and determines the pet's emotions. Emotions include, for example, joy, anger, and sadness. The analysis unit uses AI to analyze the collected data and determine the pet's emotions. For example, it can analyze the pet's behavior data and determine whether the pet is happy. It can also analyze the pet's facial expression data and determine whether the pet is angry. It can also analyze the pet's voice data and determine whether the pet is sad. Step 3: The providing unit provides the emotion data determined by the analyzing unit to the user. The providing unit displays the emotion data to the user through an app. For example, the app displays a message such as "Your pet is happy right now." The providing unit can also notify the user of the emotion data by voice. For example, the app may use a voice assistant to play a message such as "Your pet is sad right now."

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0108] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0124] 5, the data processing system 310 includes the 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0140] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[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] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data on pet behavior, facial expressions, and voices; an analysis unit that analyzes the data collected by the collection unit and determines the emotion of the pet; a providing unit that provides a user with emotion data determined by the analysis unit; Equipped with A system characterized by:

2. The providing unit Notifying users of their pet's needs 2. The system of claim 1.

3. The collecting unit Estimate the pet's emotions and adjust the timing of data collection based on the estimated pet's emotions 2. The system of claim 1.

4. The collecting unit Analyze your pet's past behavioral history and select the appropriate data collection method 2. The system of claim 1.

5. The collecting unit Filter data collection based on your pet's current health and activity level 2. The system of claim 1.

6. The collecting unit When collecting data, choose the appropriate collection method depending on the type and age of the pet.

2. The system of claim 1.

7. The collecting unit Estimate the pet's emotions and prioritize the data to collect based on the estimated emotions.

2. The system of claim 1.

8. The collecting unit When collecting data, prioritize collection of relevant data based on pet living environment information.

2. The system of claim 1.

9. The collecting unit During data collection, we analyze the social media activity of pet owners and collect relevant data.

2. The system of claim 1.

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