System

Abstract

An object of a system according to an embodiment is to accurately estimate the age of a pet and obtain information for providing appropriate care.SOLUTION: A system includes a photograph analysis unit, a question analysis unit, and an age estimation unit. The photograph analysis unit analyzes the photograph of the pet. The question analysis unit analyzes an answer to a simple question. The age estimation part integrates the analysis results of the photograph analysis part and the question analysis part to estimate the age of the pet.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately estimate a pet's age, leaving the information needed to provide appropriate care.

[0005] The system according to the embodiment aims to accurately estimate the age of a pet and obtain information for providing appropriate care. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo analysis unit, a question analysis unit, and an age estimation unit. The photo analysis unit analyzes photos of the pet. The question analysis unit analyzes answers to simple questions. The age estimation unit integrates the analysis results of the photo analysis unit and the question analysis unit to estimate the age of the pet. [Effects of the Invention]

[0007] The system according to the embodiment can accurately estimate the age of a pet and obtain information for providing appropriate care. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The age determination system according to an embodiment of the present invention automatically analyzes a photograph of a pet, and a generation AI analyzes the answers to simple questions to estimate the pet's age. This allows the age determination system to accurately estimate the pet's age and provide appropriate care.

[0029] The age determination system according to the embodiment includes a photo analysis unit, a question analysis unit, and an age estimation unit. The photo analysis unit analyzes photos of pets. For example, a user uploads a photo capturing the pet's facial and body features, and the generation AI analyzes the photo to extract age-related features. The photo analysis unit can analyze, for example, facial wrinkles, fur condition, and body size of dogs and cats. The question analysis unit analyzes answers to simple questions. For example, a user inputs answers to questions about the pet's weight and activity level, and the generation AI analyzes the answers to obtain additional information necessary for age estimation. The question analysis unit can analyze questions such as, "How much does your pet weigh?" and "How active is your pet?" The age estimation unit integrates the analysis results of the photo analysis unit and the question analysis unit to estimate the pet's age. For example, the generation AI comprehensively assesses information such as the depth of facial wrinkles, fur condition, weight, and activity level, and then executes an age estimation algorithm to output a final age. In addition, the age estimation unit can estimate the age based on the generation AI's analysis of the photo and the answers to the questions. This allows the age determination system according to the embodiment to accurately estimate the age of the pet and provide appropriate care. For example, it can provide advice on diet and exercise according to the pet's age.

[0030] The photo analysis unit can simultaneously analyze not only a pet's age from a photo, but also its health condition and signs of certain illnesses. For example, the photo analysis unit uploads a photo of the pet, and the generative AI analyzes the photo to simultaneously detect not only the pet's age, but also its health condition and signs of certain illnesses. For example, it analyzes the eye condition, skin color, and coat quality of dogs and cats to assess their health condition. The photo analysis unit can also evaluate the pet's health condition based on its weight and activity level, for example. This allows the pet's health condition and signs of illness to be simultaneously analyzed.

[0031] The photo analysis unit can analyze videos of your pet and estimate its age from its movements and behavior patterns. For example, the photo analysis unit uploads a video of your pet, and the generation AI analyzes the video to estimate its age from its movements and behavior patterns. For example, it analyzes how a dog runs or how high a cat jumps. The photo analysis unit can also estimate your pet's age from, for example, how it plays and eats. This makes it possible to estimate your pet's age from its movements and behavior patterns.

[0032] The photo analysis unit can extend the generative AI's algorithm to handle age estimation for different types of pets. For example, the photo analysis unit can extend the generative AI's algorithm to handle age estimation for birds and reptiles in addition to dogs and cats. For example, it analyzes the condition of birds' feathers and the quality of reptiles' skin. The photo analysis unit can also handle age estimation for fish and small animals, for example. This makes it possible to handle age estimation for different types of pets.

[0033] The question analysis unit can perform more accurate age estimation based on the answers to the questions by taking into account the pet's living environment and the owner's lifestyle. The question analysis unit can improve the accuracy of age estimation by taking into account the pet's living environment and the owner's lifestyle based on the answers to the questions, for example. For example, it can take into account whether the pet is kept indoors or outdoors. The question analysis unit can also take into account, for example, the owner's work hours and the amount of time spent with the pet. In this way, by taking into account the pet's living environment and the owner's lifestyle, more accurate age estimation is possible.

[0034] The question analysis unit can also refer to the pet's past medical records and health checkup results when analyzing the answer to the question. For example, the question analysis unit can refer to the pet's past medical records and health checkup results when analyzing the answer to the question to improve the accuracy of age estimation. For example, the question analysis unit can take into account past medical history and vaccination records. The question analysis unit can also estimate the age based on the pet's health checkup results, for example. In this way, by referring to the pet's past medical records and health checkup results, the accuracy of age estimation can be improved.

[0035] The question analysis unit can diversify the format of questions and accept answers via voice input or chat format. The question analysis unit, for example, diversifies the format of questions and builds a system that accepts answers via voice input or chat format. For example, it allows owners to answer questions via voice. The question analysis unit can also accept answers to questions using, for example, a chatbot. This diversifies the format of questions, making it easier for owners to answer questions.

[0036] The question analysis unit can expand the content of the question to include not only the pet's age but also questions about its health condition and behavioral characteristics. For example, the question analysis unit can expand the content of the question to include not only the pet's age but also questions about its health condition and behavioral characteristics. For example, the question analysis unit can ask about the pet's appetite and sleep patterns. The question analysis unit can also ask about the pet's exercise level and behavioral patterns, for example. By expanding the content of the question, more information can be collected and the accuracy of age estimation can be improved.

[0037] The age estimation unit can perform more accurate estimation by incorporating the genetic information and breed characteristics of the pet into the age estimation algorithm. The age estimation unit can perform more accurate estimation by, for example, incorporating the genetic information and breed characteristics of the pet into the age estimation algorithm. For example, the genetic characteristics of a specific dog breed or cat breed are taken into consideration. The age estimation unit can also perform age estimation based on the genetic information of the pet, for example. In this way, the accuracy of age estimation is improved by taking the genetic information and breed characteristics into consideration.

[0038] The age estimation unit can take into account the pet's past growth data and weight changes when estimating the age. The age estimation unit, for example, takes into account the pet's past growth data and weight changes when estimating the age. For example, it analyzes weight gain during growth and weight loss due to aging. The age estimation unit can also estimate the age based on the pet's growth data, for example. In this way, by taking into account the pet's past growth data and weight changes, the accuracy of age estimation is improved.

[0039] The age estimation unit can link the age estimation result with a health management app or fitness tracker for the pet. For example, the age estimation unit links the age estimation result with a health management app or fitness tracker for the pet. For example, it can provide advice on the amount of exercise and diet according to the pet's age. The age estimation unit can also synchronize the pet's health data with a health management app, for example. In this way, linking the age estimation result with a health management app or fitness tracker makes it easier to manage the pet's health.

[0040] The age estimation unit can utilize the age estimation result to propose insurance plans and medical services for pets. The age estimation unit, for example, utilizes the age estimation result to propose insurance plans and medical services for pets. For example, it reviews insurance plans and proposes medical services according to the pet's age. The age estimation unit can also propose appropriate medical services based on the pet's health condition, for example. In this way, by utilizing the age estimation result to propose insurance plans and medical services, pet health management is improved.

[0041] The continuous age verification unit can monitor the behavioral data and activity level of the pet during continuous age verification and reflect the data in age estimation. The continuous age verification unit can, for example, monitor the behavioral data and activity level of the pet during continuous age verification and reflect the data in age estimation. For example, the number of steps taken by the pet and the amount of exercise time are analyzed. The continuous age verification unit can also monitor, for example, the sleeping patterns and frequency of meals of the pet. In this way, by monitoring the behavioral data and activity level of the pet, the accuracy of age estimation is improved.

[0042] The continuous age verification unit can make suggestions for improving the pet's health condition and lifestyle habits based on the results of the continuous age verification. The continuous age verification unit can make suggestions for improving the pet's health condition and lifestyle habits based on the results of the continuous age verification. For example, it can suggest reviewing the pet's diet or adjusting the amount of exercise. The continuous age verification unit can also make suggestions for improvement based on the pet's living environment and the owner's lifestyle, for example. This makes it possible to make suggestions for improving the pet's health condition and lifestyle habits based on the results of the continuous age verification.

[0043] The continuous age verification unit can store the results of the continuous age verification as a pet growth record or album and provide it to the owner. The continuous age verification unit, for example, stores the results of the continuous age verification as a pet growth record or album and provides it to the owner. For example, the pet's growth process is recorded using photographs or data. The continuous age verification unit can also provide it as, for example, a digital album or a printed album. In this way, the results of the continuous age verification can be stored as a growth record or album and provided to the owner.

[0044] The continuous age verification unit can utilize the results of the continuous age verification in a training program or behavior modification plan for the pet. The continuous age verification unit, for example, utilizes the results of the continuous age verification in a training program or behavior modification plan for the pet. For example, it can provide a training menu appropriate for the pet's age. The continuous age verification unit can also provide a modification plan for the pet's problem behavior. In this way, the results of the continuous age verification can be utilized in a training program or behavior modification plan.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The age determination system can further include a pet behavior analysis unit. The behavior analysis unit analyzes the pet's behavioral patterns to help estimate the pet's age. For example, it analyzes the pet's play style, eating habits, and sleep patterns. The behavior analysis unit can also monitor the pet's exercise level and activity time and reflect this in age estimation. This can improve the accuracy of age estimation based on the pet's behavioral data.

[0047] The age determination system can further include a pet health monitoring unit. The health monitoring unit continuously monitors the pet's health condition to aid in age estimation. For example, it monitors the pet's body temperature, heart rate, and respiratory rate. The health monitoring unit can also record the pet's appetite and water intake and reflect these in age estimation. This can improve the accuracy of age estimation based on the pet's health data.

[0048] The age determination system can further include a genetic information analysis unit for the pet. The genetic information analysis unit analyzes the genetic information of the pet to help estimate the age. For example, the genetic characteristics of a specific breed of dog or cat are taken into consideration. The genetic information analysis unit can also estimate the age based on the genetic information of the pet. By taking the genetic information into consideration, the accuracy of age estimation can be improved.

[0049] The age determination system may further include a pet growth recorder. The growth recorder records the pet's growth process and uses this information to estimate the pet's age. For example, it records changes in the pet's weight and body length. The growth recorder may also estimate the pet's age based on the pet's growth data. This allows for improved accuracy in age estimation based on the pet's growth data.

[0050] The age determination system may further include a pet living environment analysis unit. The living environment analysis unit analyzes the pet's living environment to aid in age estimation. For example, the living environment analysis unit may consider whether the pet is kept indoors or outdoors. The living environment analysis unit may also estimate the age based on the pet's living environment. This allows for improved accuracy in age estimation by taking the pet's living environment into consideration.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The photo analysis unit analyzes a photo of your pet. For example, you can upload a photo that captures your pet's facial and body features, and the generative AI will analyze the photo to extract age-related features. The photo analysis unit can also analyze facial wrinkles, fur condition, and body size of dogs and cats. Step 2: The question analyzer analyzes answers to simple questions. For example, answers to questions about a pet's weight or activity level can be entered, and the generation AI analyzes the answers to obtain additional information needed for age estimation. The question analyzer can also analyze questions such as "How much does your pet weigh?" and "How active is your pet?" Step 3: The age estimation unit estimates the pet's age by integrating the analysis results of the photo analysis unit and the question analysis unit. For example, the generation AI comprehensively assesses information such as the depth of facial wrinkles, the condition of the coat, weight, and activity level, and then runs an age estimation algorithm to output the final age. The age estimation unit can also estimate the pet's age based on the photo analysis results and the answers to the questions.

[0053] (Example 2) The age determination system according to an embodiment of the present invention automatically analyzes a photograph of a pet, and a generation AI analyzes the answers to simple questions to estimate the pet's age. This allows the age determination system to accurately estimate the pet's age and provide appropriate care.

[0054] The age determination system according to the embodiment includes a photo analysis unit, a question analysis unit, and an age estimation unit. The photo analysis unit analyzes photos of pets. For example, a user uploads a photo capturing the pet's facial and body features, and the generation AI analyzes the photo to extract age-related features. The photo analysis unit can analyze, for example, facial wrinkles, fur condition, and body size of dogs and cats. The question analysis unit analyzes answers to simple questions. For example, a user inputs answers to questions about the pet's weight and activity level, and the generation AI analyzes the answers to obtain additional information necessary for age estimation. The question analysis unit can analyze questions such as, "How much does your pet weigh?" and "How active is your pet?" The age estimation unit integrates the analysis results of the photo analysis unit and the question analysis unit to estimate the pet's age. For example, the generation AI comprehensively assesses information such as the depth of facial wrinkles, fur condition, weight, and activity level, and then executes an age estimation algorithm to output a final age. In addition, the age estimation unit can estimate the age based on the generation AI's analysis of the photo and the answers to the questions. This allows the age determination system according to the embodiment to accurately estimate the age of the pet and provide appropriate care. For example, it can provide advice on diet and exercise according to the pet's age.

[0055] The photo analysis unit can simultaneously analyze not only a pet's age from a photo, but also its health condition and signs of certain illnesses. For example, the photo analysis unit uploads a photo of the pet, and the generative AI analyzes the photo to simultaneously detect not only the pet's age, but also its health condition and signs of certain illnesses. For example, it analyzes the eye condition, skin color, and coat quality of dogs and cats to assess their health condition. The photo analysis unit can also evaluate the pet's health condition based on its weight and activity level, for example. This allows the pet's health condition and signs of illness to be simultaneously analyzed.

[0056] The photo analysis unit can analyze a pet's facial expression and posture to estimate its stress level and emotional state. For example, the photo analysis unit uploads a photo of the pet, and the generation AI analyzes the photo to estimate the pet's stress level and emotional state from its facial expression and posture. For example, it analyzes the position of the ears, the degree of eye opening, and the level of tension in the body. The photo analysis unit can also estimate the pet's emotional state from its behavioral patterns and body movements, for example. This makes it possible to estimate the pet's stress level and emotional state.

[0057] The photo analysis unit can estimate the owner's emotions from photos of their pet and adjust the age estimation result based on the emotions. For example, the photo analysis unit uploads a photo of the pet, and the generation AI analyzes the photo to estimate the owner's emotions. For example, if the owner is smiling and photographed with their pet, a positive emotion is estimated. The photo analysis unit can also estimate emotions from the owner's facial expression and posture, for example. This allows the age estimation result to be adjusted based on the owner's emotions.

[0058] The photo analysis unit can analyze videos of your pet and estimate its age from its movements and behavior patterns. For example, the photo analysis unit uploads a video of your pet, and the generation AI analyzes the video to estimate its age from its movements and behavior patterns. For example, it analyzes how a dog runs or how high a cat jumps. The photo analysis unit can also estimate your pet's age from, for example, how it plays and eats. This makes it possible to estimate your pet's age from its movements and behavior patterns.

[0059] The photo analysis unit can extend the generative AI's algorithm to handle age estimation for different types of pets. For example, the photo analysis unit can extend the generative AI's algorithm to handle age estimation for birds and reptiles in addition to dogs and cats. For example, it analyzes the condition of birds' feathers and the quality of reptiles' skin. The photo analysis unit can also handle age estimation for fish and small animals, for example. This makes it possible to handle age estimation for different types of pets.

[0060] The photo analysis unit can analyze the owner's emotions in real time when uploading a photo of their pet and provide positive feedback. For example, when uploading a photo of their pet, the photo analysis unit uses a generative AI to analyze the owner's emotions in real time and provide positive feedback. For example, if the owner uploads a photo with a smile, an encouraging message is displayed. The photo analysis unit can also analyze the owner's emotions from their facial expressions and voice, for example, and provide positive feedback. This makes it possible to provide positive feedback based on the owner's emotions.

[0061] The question analysis unit can perform more accurate age estimation based on the answers to the questions by taking into account the pet's living environment and the owner's lifestyle. The question analysis unit can improve the accuracy of age estimation by taking into account the pet's living environment and the owner's lifestyle based on the answers to the questions, for example. For example, it can take into account whether the pet is kept indoors or outdoors. The question analysis unit can also take into account, for example, the owner's work hours and the amount of time spent with the pet. In this way, by taking into account the pet's living environment and the owner's lifestyle, more accurate age estimation is possible.

[0062] The question analysis unit can also refer to the pet's past medical records and health checkup results when analyzing the answer to the question. For example, the question analysis unit can refer to the pet's past medical records and health checkup results when analyzing the answer to the question to improve the accuracy of age estimation. For example, the question analysis unit can take into account past medical history and vaccination records. The question analysis unit can also estimate the age based on the pet's health checkup results, for example. In this way, by referring to the pet's past medical records and health checkup results, the accuracy of age estimation can be improved.

[0063] The question analysis unit can use the emotion estimation function to analyze the emotion of the owner when answering a question and adjust the age estimation result based on that emotion. The question analysis unit, for example, analyzes the emotion of the owner when answering a question and adjusts the age estimation result based on that emotion. For example, if the owner is feeling anxious, the age estimation result is presented carefully. The question analysis unit can also analyze the emotion from the owner's facial expression or voice, for example, and adjust the age estimation result. This allows the age estimation result to be adjusted based on the owner's emotion.

[0064] The question analysis unit can diversify the format of questions and accept answers via voice input or chat format. The question analysis unit, for example, diversifies the format of questions and builds a system that accepts answers via voice input or chat format. For example, it allows owners to answer questions via voice. The question analysis unit can also accept answers to questions using, for example, a chatbot. This diversifies the format of questions, making it easier for owners to answer questions.

[0065] The question analysis unit can expand the content of the question to include not only the pet's age but also questions about its health condition and behavioral characteristics. For example, the question analysis unit can expand the content of the question to include not only the pet's age but also questions about its health condition and behavioral characteristics. For example, the question analysis unit can ask about the pet's appetite and sleep patterns. The question analysis unit can also ask about the pet's exercise level and behavioral patterns, for example. By expanding the content of the question, more information can be collected and the accuracy of age estimation can be improved.

[0066] The question analysis unit can use the emotion estimation function to analyze the emotion of the owner when answering a question in real time and provide positive feedback. The question analysis unit, for example, analyzes the emotion of the owner when answering a question in real time and builds a system that provides positive feedback. For example, if the owner is feeling anxious, an encouraging message is displayed. The question analysis unit can also analyze the emotion from the owner's facial expression or voice, for example, and provide positive feedback. This makes it possible to provide positive feedback based on the owner's emotion.

[0067] The age estimation unit can perform more accurate estimation by incorporating the genetic information and breed characteristics of the pet into the age estimation algorithm. The age estimation unit can perform more accurate estimation by, for example, incorporating the genetic information and breed characteristics of the pet into the age estimation algorithm. For example, the genetic characteristics of a specific dog breed or cat breed are taken into consideration. The age estimation unit can also perform age estimation based on the genetic information of the pet, for example. In this way, the accuracy of age estimation is improved by taking the genetic information and breed characteristics into consideration.

[0068] The age estimation unit can take into account the pet's past growth data and weight changes when estimating the age. The age estimation unit, for example, takes into account the pet's past growth data and weight changes when estimating the age. For example, it analyzes weight gain during growth and weight loss due to aging. The age estimation unit can also estimate the age based on the pet's growth data, for example. In this way, by taking into account the pet's past growth data and weight changes, the accuracy of age estimation is improved.

[0069] The age estimation unit can use the emotion estimation function to analyze the owner's emotional reaction to the age estimation result and adjust the result based on that reaction. For example, the age estimation unit analyzes the owner's emotional reaction to the age estimation result and adjusts the result based on that reaction. For example, if the owner is feeling surprised or anxious, the result is presented carefully. The age estimation unit can also analyze the owner's emotion from, for example, facial expressions or voice and adjust the result. This allows the age estimation result to be adjusted based on the owner's emotional reaction.

[0070] The age estimation unit can link the age estimation result with a health management app or fitness tracker for the pet. For example, the age estimation unit links the age estimation result with a health management app or fitness tracker for the pet. For example, it can provide advice on the amount of exercise and diet according to the pet's age. The age estimation unit can also synchronize the pet's health data with a health management app, for example. In this way, linking the age estimation result with a health management app or fitness tracker makes it easier to manage the pet's health.

[0071] The age estimation unit can utilize the age estimation result to propose insurance plans and medical services for pets. The age estimation unit, for example, utilizes the age estimation result to propose insurance plans and medical services for pets. For example, it reviews insurance plans and proposes medical services according to the pet's age. The age estimation unit can also propose appropriate medical services based on the pet's health condition, for example. In this way, by utilizing the age estimation result to propose insurance plans and medical services, pet health management is improved.

[0072] The age estimation unit can use the emotion estimation function to analyze the owner's emotional reaction to the age estimation result in real time and provide positive feedback. The age estimation unit, for example, builds a system that analyzes the owner's emotional reaction to the age estimation result in real time and provides positive feedback. For example, if the owner is feeling anxious, an encouraging message is displayed. The age estimation unit can also analyze the owner's emotions from, for example, facial expressions and voice and provide positive feedback. This makes it possible to provide positive feedback based on the owner's emotional reaction.

[0073] The continuous age verification unit can monitor the behavioral data and activity level of the pet during continuous age verification and reflect the data in age estimation. The continuous age verification unit can, for example, monitor the behavioral data and activity level of the pet during continuous age verification and reflect the data in age estimation. For example, the number of steps taken by the pet and the amount of exercise time are analyzed. The continuous age verification unit can also monitor, for example, the sleeping patterns and frequency of meals of the pet. In this way, by monitoring the behavioral data and activity level of the pet, the accuracy of age estimation is improved.

[0074] The continuous age verification unit can make suggestions for improving the pet's health condition and lifestyle habits based on the results of the continuous age verification. The continuous age verification unit can make suggestions for improving the pet's health condition and lifestyle habits based on the results of the continuous age verification. For example, it can suggest reviewing the pet's diet or adjusting the amount of exercise. The continuous age verification unit can also make suggestions for improvement based on the pet's living environment and the owner's lifestyle, for example. This makes it possible to make suggestions for improving the pet's health condition and lifestyle habits based on the results of the continuous age verification.

[0075] The continuous age verification unit can use the emotion estimation function to analyze the owner's emotional response to the result of the continuous age verification and adjust the care suggestions based on the response. For example, the continuous age verification unit can analyze the owner's emotional response to the result of the continuous age verification and adjust the care suggestions based on the response. For example, if the owner is feeling anxious, the continuous age verification unit can make suggestions that will give the owner a sense of security. The continuous age verification unit can also analyze the owner's emotions from, for example, facial expressions and voice and adjust the care suggestions. This allows the care suggestions to be adjusted based on the owner's emotional response.

[0076] The continuous age verification unit can store the results of the continuous age verification as a pet growth record or album and provide it to the owner. The continuous age verification unit, for example, stores the results of the continuous age verification as a pet growth record or album and provides it to the owner. For example, the pet's growth process is recorded using photographs or data. The continuous age verification unit can also provide it as, for example, a digital album or a printed album. In this way, the results of the continuous age verification can be stored as a growth record or album and provided to the owner.

[0077] The continuous age verification unit can utilize the results of the continuous age verification in a training program or behavior modification plan for the pet. The continuous age verification unit, for example, utilizes the results of the continuous age verification in a training program or behavior modification plan for the pet. For example, it can provide a training menu appropriate for the pet's age. The continuous age verification unit can also provide a modification plan for the pet's problem behavior. In this way, the results of the continuous age verification can be utilized in a training program or behavior modification plan.

[0078] The continuous age verification unit can use the emotion estimation function to analyze the owner's emotional response to the result of the continuous age verification in real time and provide positive feedback. The continuous age verification unit, for example, builds a system that analyzes the owner's emotional response to the result of the continuous age verification in real time and provides positive feedback. For example, if the owner is feeling anxious, an encouraging message is displayed. The continuous age verification unit can also analyze the owner's emotions from, for example, facial expressions and voice and provide positive feedback. This makes it possible to provide positive feedback based on the owner's emotional response.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The age determination system can further include a pet behavior analysis unit. The behavior analysis unit analyzes the pet's behavioral patterns to help estimate the pet's age. For example, it analyzes the pet's play style, eating habits, and sleep patterns. The behavior analysis unit can also monitor the pet's exercise level and activity time and reflect this in age estimation. This can improve the accuracy of age estimation based on the pet's behavioral data.

[0081] The age determination system can further include a pet health monitoring unit. The health monitoring unit continuously monitors the pet's health condition to aid in age estimation. For example, it monitors the pet's body temperature, heart rate, and respiratory rate. The health monitoring unit can also record the pet's appetite and water intake and reflect these in age estimation. This can improve the accuracy of age estimation based on the pet's health data.

[0082] The age determination system can further include a genetic information analysis unit for the pet. The genetic information analysis unit analyzes the genetic information of the pet to help estimate the age. For example, the genetic characteristics of a specific breed of dog or cat are taken into consideration. The genetic information analysis unit can also estimate the age based on the genetic information of the pet. By taking the genetic information into consideration, the accuracy of age estimation can be improved.

[0083] The age determination system may further include a pet growth recorder. The growth recorder records the pet's growth process and uses this information to estimate the pet's age. For example, it records changes in the pet's weight and body length. The growth recorder may also estimate the pet's age based on the pet's growth data. This allows for improved accuracy in age estimation based on the pet's growth data.

[0084] The age determination system may further include a pet living environment analysis unit. The living environment analysis unit analyzes the pet's living environment to aid in age estimation. For example, the living environment analysis unit may consider whether the pet is kept indoors or outdoors. The living environment analysis unit may also estimate the age based on the pet's living environment. This allows for improved accuracy in age estimation by taking the pet's living environment into consideration.

[0085] The age determination system may further include a pet emotion analysis unit. The emotion analysis unit analyzes the emotional state of the pet to help estimate the age. For example, the emotion analysis unit estimates the emotional state from the pet's facial expressions and behavior. The emotion analysis unit may also estimate the age based on the emotional state of the pet. This allows for improved accuracy in age estimation by taking the emotional state of the pet into consideration.

[0086] The age determination system can further include an owner emotion analysis unit. The owner emotion analysis unit analyzes the owner's emotional state and uses it to estimate the age. For example, the emotional state is estimated from the owner's facial expression or voice. The age estimation result can also be adjusted based on the owner's emotional state. This allows the accuracy of age estimation to be improved by taking the owner's emotional state into consideration.

[0087] The age determination system may further include a pet stress level analysis unit. The stress level analysis unit analyzes the pet's stress level to help estimate the pet's age. For example, the stress level may be estimated from the pet's behavior or physiological responses. The stress level analysis unit may also estimate the pet's age based on the pet's stress level. This allows for improved accuracy in age estimation by taking the pet's stress level into consideration.

[0088] The age determination system may further include a pet emotion feedback unit. The emotion feedback unit analyzes the emotional state of the pet and provides positive feedback. For example, if the pet is relaxed, a praising message is displayed. The emotion feedback unit may also adjust the feedback based on the emotional state of the pet. This allows the positive feedback to be provided taking the emotional state of the pet into consideration.

[0089] The age determination system may further include an owner's emotional feedback unit. The emotional feedback unit analyzes the owner's emotional state and provides positive feedback. For example, if the owner feels anxious, an encouraging message is displayed. The emotional feedback unit may also adjust the feedback based on the owner's emotional state. This allows the positive feedback to be provided taking the owner's emotional state into consideration.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The photo analysis unit analyzes a photo of your pet. For example, you can upload a photo that captures your pet's facial and body features, and the generative AI will analyze the photo to extract age-related features. The photo analysis unit can also analyze facial wrinkles, fur condition, and body size of dogs and cats. Step 2: The question analyzer analyzes answers to simple questions. For example, answers to questions about a pet's weight or activity level can be entered, and the generation AI analyzes the answers to obtain additional information needed for age estimation. The question analyzer can also analyze questions such as "How much does your pet weigh?" and "How active is your pet?" Step 3: The age estimation unit estimates the pet's age by integrating the analysis results of the photo analysis unit and the question analysis unit. For example, the generation AI comprehensively assesses information such as the depth of facial wrinkles, the condition of the coat, weight, and activity level, and then runs an age estimation algorithm to output the final age. The age estimation unit can also estimate the pet's age based on the photo analysis results and the answers to the questions.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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 photo analysis section that analyzes photos of pets, a question analysis unit that analyzes answers to simple questions; an age estimation unit that estimates the age of the pet by integrating the analysis results of the photo analysis unit and the question analysis unit; A system characterized by:

2. The photo analysis unit Analyzing videos of pets and estimating their age from their movements and behavior patterns 2. The system of claim 1.

3. The question analysis unit Based on the answers to the questions, the living environment of the pet and the lifestyle of the owner are also taken into consideration to perform a more accurate age estimation.

2. The system of claim 1.

4. The age estimation unit By incorporating the pet's genetic information and breed characteristics into the age estimation algorithm, more accurate estimations can be made.

2. The system of claim 1.

5. The photo analysis unit Analyze your pet's facial expressions and posture to estimate its stress level and emotional state 2. The system of claim 1.

Images