System

Abstract

An object of a system according to an embodiment is to accurately grasp an abnormality in an emotion or a health condition from a dog's voice.SOLUTION: A system includes a voice collection unit, an analysis unit, an emotion determination unit, an abnormality detection unit, and a notification unit. The sound collection unit collects a dog's voice. The analysis unit analyzes the voice collected by the sound collection unit. The emotion determiner determines an emotion of the dog based on the pattern of the voice analyzed by the analyzer. The abnormality detector detects an abnormality in the health condition of the dog based on the pattern of the voice analyzed by the analyzer. The notification part notifies the owner of the results determined by the emotion determination part and the abnormality detection part.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 grasp a dog's emotions or abnormalities in health from its voice.

[0005] The system according to the embodiment aims to accurately grasp emotions and abnormalities in health from the dog's voice. [Means for solving the problem]

[0006] The system according to the embodiment includes a sound collection unit, an analysis unit, an emotion determination unit, an abnormality detection unit, and a notification unit. The sound collection unit collects the dog's voice. The analysis unit analyzes the voice collected by the sound collection unit. The emotion determination unit determines the dog's emotion based on the voice pattern analyzed by the analysis unit. The abnormality detection unit detects abnormalities in the dog's health based on the voice pattern analyzed by the analysis unit. The notification unit notifies the owner of the results determined by the emotion determination unit and the abnormality detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately detect emotions and abnormalities in health from the dog's voice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dog voice diagnostic system according to an embodiment of the present invention is a system that collects dog voices, analyzes them using a generation AI, and detects abnormalities in emotions and health conditions. As a result, the dog voice diagnostic system can detect abnormalities in emotions and health conditions from the dog's voice and notify the owner.

[0029] A dog voice diagnostic system according to an embodiment includes a sound collection unit, an analysis unit, an emotion determination unit, an abnormality detection unit, and a notification unit. The sound collection unit collects the dog's voice. For example, it collects the dog's barks and cries using a microphone. The sound collection unit can also record the dog's voice using a smartphone app. The sound collection unit can also continuously monitor the dog's voice using a wearable device. For example, a microphone can be built into a collar to collect the dog's voice 24 hours a day. The analysis unit analyzes the collected voice. For example, it can analyze the voice waveform and extract voice patterns and characteristics. The analysis unit can also perform frequency analysis to analyze the pitch and duration of the voice. The analysis unit can also use an emotion estimation function to extract and focus on parts of the collected voice that express particularly strong emotions. For example, it can identify parts that show strong emotions such as excitement or fear and focus on analyzing those parts. The emotion determination unit determines the dog's emotion based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether the dog is excited, afraid, or relaxed. The generation AI receives input from the voice pattern analysis results, which the generation AI uses to determine the dog's emotions. The anomaly detection unit detects abnormalities in the dog's health based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether the dog has an abnormality in its health, such as a cough or a hoarse voice. The generation AI receives input from the voice pattern analysis results, which the generation AI uses to detect abnormalities in the dog's health based on the results. The notification unit notifies the owner of the results determined by the emotion determination unit and the anomaly detection unit. For example, the notification unit may send a notification via a smartphone app. The notification unit may also provide appropriate countermeasures and advice. For example, the notification unit may provide advice such as, "Your dog seems stressed. Please create a relaxing environment." The generation AI receives input from the emotion and health assessment results, which the generation AI uses to generate notification content and advice based on the results. As a result, the dog voice diagnosis system according to the embodiment can detect abnormalities in the dog's emotions and health from the dog's voice and notify the owner.For example, owners can detect stress or illness in their dogs early and take appropriate measures. Also, if there are any health issues, owners can consult a veterinarian early on. This will help maintain the health and happiness of their dogs.

[0030] The audio collection unit can incorporate filtering technology that automatically removes environmental sounds and background noise. For example, when collecting a dog's voice, the audio collection unit analyzes the surrounding environmental sounds and background noise in real time and removes them using noise-canceling technology. For example, wind noise and the sounds of other animals can be filtered out to clearly collect only the dog's voice. This allows for the removal of environmental sounds and background noise, resulting in more accurate audio data.

[0031] The audio collection unit can simultaneously collect dog behavior data and integrate it with the audio data for analysis. For example, when collecting the dog's voice, the audio collection unit simultaneously records the dog's movements and posture using a camera or sensor. For example, it collects data on the dog's body movements and ear position when it barks, and integrates and analyzes it with the audio data. In this way, by integrating and analyzing the dog's behavior data and audio data, it becomes possible to more accurately determine the dog's emotions and health condition.

[0032] The voice collection unit can perform constant monitoring using a wearable device. For example, the voice collection unit can attach a wearable device to a dog and collect voices 24 hours a day. For example, a microphone can be built into the collar to constantly monitor the dog's voice. In this way, the wearable device can be used to constantly monitor the dog's voice.

[0033] The analysis unit can use voice analysis models specialized for different dog breeds or age groups. For example, the analysis unit develops voice analysis models specialized for different dog breeds and analyzes the vocal characteristics of each dog breed. For example, it uses models optimized for specific dog breeds, such as Shiba Inu or Golden Retriever. By using voice analysis models specialized for different dog breeds or age groups, more accurate analysis is possible.

[0034] The emotion determination unit can compare with past voice data and track changes in emotions over time. For example, the emotion determination unit stores past voice data of a dog in a database and compares it with newly collected voice data. For example, it analyzes past data and current data in time series to track changes in emotions. In this way, by comparing with past voice data, it is possible to track changes in emotions over time.

[0035] The emotion determination unit can perform more accurate emotion determination by combining biological data such as the dog's body temperature and heart rate. The emotion determination unit, for example, monitors the dog's body temperature and heart rate in real time and integrates this with voice data to determine the dog's emotion. For example, an increase in heart rate is combined with a change in voice tone to determine the dog's state of excitement. This allows for more accurate emotion determination by combining biological data such as the dog's body temperature and heart rate.

[0036] The anomaly detection unit can compare the data with past health data and detect abnormalities early. For example, the anomaly detection unit stores the dog's past health data in a database and compares it with newly collected voice data. For example, it analyzes past data and current data in chronological order to detect abnormalities in the health condition early. This makes it possible to detect abnormalities early by comparing with past health data.

[0037] The anomaly detection unit can perform a comprehensive health assessment by combining lifestyle data such as the dog's diet and amount of exercise. The anomaly detection unit, for example, records the dog's diet and amount of exercise and integrates this with audio data to evaluate the dog's health. For example, the anomaly detection unit collects data on the dog's diet and exercise frequency to perform a comprehensive health assessment. This makes it possible to perform a comprehensive health assessment by combining lifestyle data such as the dog's diet and amount of exercise.

[0038] The notification unit can customize the notification content based on the dog's individual personality and past behavioral patterns. For example, the notification unit stores the dog's individual personality and past behavioral patterns in a database and customizes the notification content. For example, the notification content is adjusted taking into account situations in which a particular dog is likely to feel stressed. In this way, customizing the notification content based on the dog's individual personality and past behavioral patterns makes it possible to provide more appropriate advice.

[0039] The notification unit can add audio or video messages to the notification content to make it easier to understand intuitively. For example, the notification unit can add audio messages to notifications to the owner to intuitively convey the dog's condition. For example, a notification can be sent that explains the dog's health condition or emotions through audio. By adding audio or video messages to the notification content, the owner can understand it more intuitively.

[0040] The notification unit can make it possible for the content of the notification sent to the owner to be shared with other family members and pet sitters. For example, the notification unit can create a system that stores the content of the notification sent to the owner in the cloud and allows other family members and pet sitters to access it. For example, it can make it possible for all family members to share the dog's status. This allows multiple people to know the dog's status by sharing the content of the notification with other family members and pet sitters.

[0041] The notification unit can automatically translate the notification content into different languages, making it possible to accommodate international users. For example, the notification unit can build a system that automatically translates the notification content to the pet owner and sends notifications in different languages. For example, it can support multiple languages ​​such as English, French, and Chinese. This allows the notification content to be automatically translated into different languages, making it possible to accommodate international users.

[0042] The anomaly detection unit can share the abnormality detection results of the dog's health condition with a veterinarian so that professional advice can be received. For example, the anomaly detection unit builds a system that stores the abnormality detection results of the dog's health condition in the cloud and makes them accessible to veterinarians. For example, the veterinarian checks the abnormality detection results and provides professional advice. In this way, by sharing the abnormality detection results of the health condition with a veterinarian, professional advice can be received.

[0043] The anomaly detection unit can expand the model so that the results of detecting abnormalities in dog health conditions can be applied to other pets. For example, the anomaly detection unit expands the model for detecting abnormalities in dog health conditions so that it can be applied to other pets (e.g., cats and birds). For example, a model can be developed to detect abnormalities in cat coughs and bird calls. This makes it possible to apply the results of detecting abnormalities in health conditions to other pets, which will be useful for managing the health of other pets.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The dog voice diagnosis system may further include a behavior collection unit that collects behavioral data of the dog. The behavior collection unit may use, for example, a wearable device equipped with an acceleration sensor and a gyroscope for detecting the dog's movements. This allows data on the dog's body movements and posture when it barks to be collected and integrated with audio data for analysis. For example, by detecting the dog's movements of lifting its front paws or wagging its tail when it barks and analyzing this behavioral data in combination with audio data, it is possible to more accurately determine the dog's emotions and health condition. The behavior collection unit may also monitor the dog's sleep patterns and activity level and integrate this data with audio data for analysis. For example, if a dog frequently wakes up and barks at night, the behavioral data can be used to identify the cause.

[0046] The dog voice diagnostic system may further include a meal collection unit that collects the dog's dietary data. The meal collection unit may use, for example, a smart feeder that records the contents and amount of food the dog eats. This allows data on what and how much the dog eats to be collected and integrated with audio data for analysis. For example, if a dog's barking frequency increases after eating a particular meal, the impact of that meal on the dog's health and emotions can be evaluated. The meal collection unit may also monitor the dog's water intake and integrate this data with audio data for analysis. For example, if a dog is not consuming enough water, the impact can be detected from the audio data.

[0047] The dog voice diagnosis system can further include a body temperature collection unit that monitors the dog's body temperature. The body temperature collection unit uses, for example, a non-contact thermometer to measure the dog's body temperature in real time. This allows the dog's body temperature data to be integrated with the voice data for analysis. For example, if the dog's body temperature rises, the effects can be detected from the voice data, allowing for early detection of abnormalities in health. The body temperature collection unit can also record fluctuations in the dog's body temperature over time and integrate these data with the voice data for analysis. For example, if the dog's body temperature fluctuates suddenly, the body temperature data can be used to identify the cause.

[0048] The dog voice diagnosis system may further include a heart rate collection unit that monitors the dog's heart rate. The heart rate collection unit may use, for example, a wearable device for measuring the dog's heart rate in real time. This allows the dog's heart rate data to be integrated with the voice data for analysis. For example, if the dog's heart rate increases, the impact can be detected from the voice data, allowing for early detection of abnormalities in health. The heart rate collection unit may also record fluctuations in the dog's heart rate over time and integrate these data with the voice data for analysis. For example, if the dog's heart rate fluctuates suddenly, the heart rate data can be used to identify the cause.

[0049] The dog voice diagnosis system may further include an activity collection unit that monitors the dog's activity level. The activity collection unit may use, for example, a fitness tracker that records the dog's steps and amount of exercise. This allows the dog's activity level data to be integrated with the voice data for analysis. For example, if the dog's activity level decreases, the impact can be detected from the voice data, allowing for early detection of abnormalities in health. The activity collection unit may also record the dog's activity patterns in chronological order and integrate this data with the voice data for analysis. For example, if the dog's activity level suddenly fluctuates, the activity data can be used to identify the cause.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The sound collection unit collects the dog's voice. For example, a microphone can be used to collect the dog's barks and cries. The sound collection unit can also record the dog's voice using a smartphone app. Furthermore, the sound collection unit can also constantly monitor the dog's voice using a wearable device. For example, a microphone can be built into the dog's collar, and the dog's voice can be collected 24 hours a day. Step 2: The analysis unit analyzes the collected voices. For example, it analyzes the voice waveform and extracts voice patterns and characteristics. The analysis unit can also perform frequency analysis to analyze the pitch and duration of the voice. Furthermore, the analysis unit can use an emotion estimation function to extract parts of the collected voices that express particularly strong emotions and focus on analyzing them. For example, it can identify parts that express strong emotions such as excitement or fear and focus on analyzing those parts. Step 3: The emotion determination unit determines the dog's emotion based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether the dog is excited, afraid, or relaxed. The input to the generation AI is the voice pattern analysis result, and the generation AI determines the emotion based on that result. Step 4: The anomaly detection unit detects abnormalities in the dog's health based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether there are any abnormalities in the dog's health, such as if the dog is coughing or has a hoarse voice. The input to the generation AI is the voice pattern analysis result, and the generation AI detects abnormalities in the dog's health based on the results. Step 5: The notification unit notifies the owner of the results determined by the emotion determination unit and anomaly detection unit. For example, the notification unit sends a notification via a smartphone app. It also provides appropriate measures and advice. For example, it may provide advice such as, "Your dog seems to be feeling stressed. Please create a relaxing environment." The input to the generation AI is the emotion and health status determination results, and the generation AI generates the notification content and advice based on those results.

[0052] (Example 2) The dog voice diagnostic system according to an embodiment of the present invention is a system that collects dog voices, analyzes them using a generation AI, and detects abnormalities in emotions and health conditions. As a result, the dog voice diagnostic system can detect abnormalities in emotions and health conditions from the dog's voice and notify the owner.

[0053] A dog voice diagnostic system according to an embodiment includes a sound collection unit, an analysis unit, an emotion determination unit, an abnormality detection unit, and a notification unit. The sound collection unit collects the dog's voice. For example, it collects the dog's barks and cries using a microphone. The sound collection unit can also record the dog's voice using a smartphone app. The sound collection unit can also continuously monitor the dog's voice using a wearable device. For example, a microphone can be built into a collar to collect the dog's voice 24 hours a day. The analysis unit analyzes the collected voice. For example, it can analyze the voice waveform and extract voice patterns and characteristics. The analysis unit can also perform frequency analysis to analyze the pitch and duration of the voice. The analysis unit can also use an emotion estimation function to extract and focus on parts of the collected voice that express particularly strong emotions. For example, it can identify parts that show strong emotions such as excitement or fear and focus on analyzing those parts. The emotion determination unit determines the dog's emotion based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether the dog is excited, afraid, or relaxed. The generation AI receives input from the voice pattern analysis results, which the generation AI uses to determine the dog's emotions. The anomaly detection unit detects abnormalities in the dog's health based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether the dog has an abnormality in its health, such as a cough or a hoarse voice. The generation AI receives input from the voice pattern analysis results, which the generation AI uses to detect abnormalities in the dog's health based on the results. The notification unit notifies the owner of the results determined by the emotion determination unit and the anomaly detection unit. For example, the notification unit may send a notification via a smartphone app. The notification unit may also provide appropriate countermeasures and advice. For example, the notification unit may provide advice such as, "Your dog seems stressed. Please create a relaxing environment." The generation AI receives input from the emotion and health assessment results, which the generation AI uses to generate notification content and advice based on the results. As a result, the dog voice diagnosis system according to the embodiment can detect abnormalities in the dog's emotions and health from the dog's voice and notify the owner.For example, owners can detect stress or illness in their dogs early and take appropriate measures. Also, if there are any health issues, owners can consult a veterinarian early on. This will help maintain the health and happiness of their dogs.

[0054] The audio collection unit can incorporate filtering technology that automatically removes environmental sounds and background noise. For example, when collecting a dog's voice, the audio collection unit analyzes the surrounding environmental sounds and background noise in real time and removes them using noise-canceling technology. For example, wind noise and the sounds of other animals can be filtered out to clearly collect only the dog's voice. This allows for the removal of environmental sounds and background noise, resulting in more accurate audio data.

[0055] The audio collection unit can simultaneously collect dog behavior data and integrate it with the audio data for analysis. For example, when collecting the dog's voice, the audio collection unit simultaneously records the dog's movements and posture using a camera or sensor. For example, it collects data on the dog's body movements and ear position when it barks, and integrates and analyzes it with the audio data. In this way, by integrating and analyzing the dog's behavior data and audio data, it becomes possible to more accurately determine the dog's emotions and health condition.

[0056] The analysis unit can use the emotion estimation function to extract and prioritize parts of collected voices that express particularly strong emotions. For example, the analysis unit applies an emotion estimation algorithm to collected data on dog voices to automatically extract parts that express strong emotions. For example, it identifies parts that express strong emotions, such as excitement or fear, and prioritizes analysis of those parts. This allows for more accurate emotion determination by focusing on analysis of parts that express strong emotions.

[0057] The voice collection unit can perform constant monitoring using a wearable device. For example, the voice collection unit can attach a wearable device to a dog and collect voices 24 hours a day. For example, a microphone can be built into the collar to constantly monitor the dog's voice. In this way, the wearable device can be used to constantly monitor the dog's voice.

[0058] The analysis unit can use voice analysis models specialized for different dog breeds or age groups. For example, the analysis unit develops voice analysis models specialized for different dog breeds and analyzes the vocal characteristics of each dog breed. For example, it uses models optimized for specific dog breeds, such as Shiba Inu or Golden Retriever. By using voice analysis models specialized for different dog breeds or age groups, more accurate analysis is possible.

[0059] The emotion determination unit can compare with past voice data and track changes in emotions over time. For example, the emotion determination unit stores past voice data of a dog in a database and compares it with newly collected voice data. For example, it analyzes past data and current data in time series to track changes in emotions. In this way, by comparing with past voice data, it is possible to track changes in emotions over time.

[0060] The emotion determination unit can perform more accurate emotion determination by combining biological data such as the dog's body temperature and heart rate. The emotion determination unit, for example, monitors the dog's body temperature and heart rate in real time and integrates this with voice data to determine the dog's emotion. For example, an increase in heart rate is combined with a change in voice tone to determine the dog's state of excitement. This allows for more accurate emotion determination by combining biological data such as the dog's body temperature and heart rate.

[0061] The emotion determination unit can use the emotion estimation function to determine the dog's emotion in real time and immediately notify the owner. The emotion determination unit, for example, uses an emotion estimation algorithm to analyze data on the dog's voice in real time and determine the emotion. For example, if the dog is excited, the owner is immediately notified. This allows the dog's emotion to be determined in real time and the owner to be immediately notified, enabling a prompt response.

[0062] The anomaly detection unit can compare the data with past health data and detect abnormalities early. For example, the anomaly detection unit stores the dog's past health data in a database and compares it with newly collected voice data. For example, it analyzes past data and current data in chronological order to detect abnormalities in the health condition early. This makes it possible to detect abnormalities early by comparing with past health data.

[0063] The anomaly detection unit can perform a comprehensive health assessment by combining lifestyle data such as the dog's diet and amount of exercise. The anomaly detection unit, for example, records the dog's diet and amount of exercise and integrates this with audio data to evaluate the dog's health. For example, the anomaly detection unit collects data on the dog's diet and exercise frequency to perform a comprehensive health assessment. This makes it possible to perform a comprehensive health assessment by combining lifestyle data such as the dog's diet and amount of exercise.

[0064] The anomaly detection unit uses the emotion estimation function to analyze the impact of an abnormality in the dog's health condition on the emotions, enabling early detection of abnormalities. The anomaly detection unit, for example, uses an emotion estimation algorithm to analyze data on the dog's voice and evaluate the impact of an abnormality in the health condition on the emotions. For example, it analyzes the changes that a deterioration in the health condition brings about. In this way, by using the emotion estimation function, it is possible to analyze the impact of an abnormality in the health condition on the emotions and detect abnormalities early.

[0065] The notification unit can customize the notification content based on the dog's individual personality and past behavioral patterns. For example, the notification unit stores the dog's individual personality and past behavioral patterns in a database and customizes the notification content. For example, the notification content is adjusted taking into account situations in which a particular dog is likely to feel stressed. In this way, customizing the notification content based on the dog's individual personality and past behavioral patterns makes it possible to provide more appropriate advice.

[0066] The notification unit can add audio or video messages to the notification content to make it easier to understand intuitively. For example, the notification unit can add audio messages to notifications to the owner to intuitively convey the dog's condition. For example, a notification can be sent that explains the dog's health condition or emotions through audio. By adding audio or video messages to the notification content, the owner can understand it more intuitively.

[0067] The notification unit can use the emotion estimation function to adjust the notification content received by the owner according to the owner's emotional state. For example, the notification unit uses an emotion estimation algorithm to analyze the owner's emotional state and adjust the notification content. For example, if the owner is feeling stressed, the notification unit can send relaxing notification content. This allows the owner to receive more appropriate advice by adjusting the notification content according to the owner's emotional state.

[0068] The notification unit can make it possible for the content of the notification sent to the owner to be shared with other family members and pet sitters. For example, the notification unit can create a system that stores the content of the notification sent to the owner in the cloud and allows other family members and pet sitters to access it. For example, it can make it possible for all family members to share the dog's status. This allows multiple people to know the dog's status by sharing the content of the notification with other family members and pet sitters.

[0069] The notification unit can automatically translate the notification content into different languages, making it possible to accommodate international users. For example, the notification unit can build a system that automatically translates the notification content to the pet owner and sends notifications in different languages. For example, it can support multiple languages ​​such as English, French, and Chinese. This allows the notification content to be automatically translated into different languages, making it possible to accommodate international users.

[0070] The anomaly detection unit can share the abnormality detection results of the dog's health condition with a veterinarian so that professional advice can be received. For example, the anomaly detection unit builds a system that stores the abnormality detection results of the dog's health condition in the cloud and makes them accessible to veterinarians. For example, the veterinarian checks the abnormality detection results and provides professional advice. In this way, by sharing the abnormality detection results of the health condition with a veterinarian, professional advice can be received.

[0071] The anomaly detection unit can expand the model so that the results of detecting abnormalities in dog health conditions can be applied to other pets. For example, the anomaly detection unit expands the model for detecting abnormalities in dog health conditions so that it can be applied to other pets (e.g., cats and birds). For example, a model can be developed to detect abnormalities in cat coughs and bird calls. This makes it possible to apply the results of detecting abnormalities in health conditions to other pets, which will be useful for managing the health of other pets.

[0072] The anomaly detection unit uses the emotion estimation function to analyze the impact of an abnormality in the dog's health condition on the emotions, enabling early detection of abnormalities. The anomaly detection unit, for example, uses an emotion estimation algorithm to analyze data on the dog's voice and evaluate the impact of an abnormality in the health condition on the emotions. For example, it analyzes the changes that a deterioration in the health condition brings about. In this way, by using the emotion estimation function, it is possible to analyze the impact of an abnormality in the health condition on the emotions and detect abnormalities early.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The dog voice diagnosis system may further include a behavior collection unit that collects behavioral data of the dog. The behavior collection unit may use, for example, a wearable device equipped with an acceleration sensor and a gyroscope for detecting the dog's movements. This allows data on the dog's body movements and posture when it barks to be collected and integrated with audio data for analysis. For example, by detecting the dog's movements of lifting its front paws or wagging its tail when it barks and analyzing this behavioral data in combination with audio data, it is possible to more accurately determine the dog's emotions and health condition. The behavior collection unit may also monitor the dog's sleep patterns and activity level and integrate this data with audio data for analysis. For example, if a dog frequently wakes up and barks at night, the behavioral data can be used to identify the cause.

[0075] The dog voice diagnostic system may further include a meal collection unit that collects the dog's dietary data. The meal collection unit may use, for example, a smart feeder that records the contents and amount of food the dog eats. This allows data on what and how much the dog eats to be collected and integrated with audio data for analysis. For example, if a dog's barking frequency increases after eating a particular meal, the impact of that meal on the dog's health and emotions can be evaluated. The meal collection unit may also monitor the dog's water intake and integrate this data with audio data for analysis. For example, if a dog is not consuming enough water, the impact can be detected from the audio data.

[0076] The dog voice diagnosis system can further include a body temperature collection unit that monitors the dog's body temperature. The body temperature collection unit uses, for example, a non-contact thermometer to measure the dog's body temperature in real time. This allows the dog's body temperature data to be integrated with the voice data for analysis. For example, if the dog's body temperature rises, the effects can be detected from the voice data, allowing for early detection of abnormalities in health. The body temperature collection unit can also record fluctuations in the dog's body temperature over time and integrate these data with the voice data for analysis. For example, if the dog's body temperature fluctuates suddenly, the body temperature data can be used to identify the cause.

[0077] The dog voice diagnosis system may further include a heart rate collection unit that monitors the dog's heart rate. The heart rate collection unit may use, for example, a wearable device for measuring the dog's heart rate in real time. This allows the dog's heart rate data to be integrated with the voice data for analysis. For example, if the dog's heart rate increases, the impact can be detected from the voice data, allowing for early detection of abnormalities in health. The heart rate collection unit may also record fluctuations in the dog's heart rate over time and integrate these data with the voice data for analysis. For example, if the dog's heart rate fluctuates suddenly, the heart rate data can be used to identify the cause.

[0078] The dog voice diagnosis system may further include an activity collection unit that monitors the dog's activity level. The activity collection unit may use, for example, a fitness tracker that records the dog's steps and amount of exercise. This allows the dog's activity level data to be integrated with the voice data for analysis. For example, if the dog's activity level decreases, the impact can be detected from the voice data, allowing for early detection of abnormalities in health. The activity collection unit may also record the dog's activity patterns in chronological order and integrate this data with the voice data for analysis. For example, if the dog's activity level suddenly fluctuates, the activity data can be used to identify the cause.

[0079] The dog voice diagnosis system may further include an advice providing unit that estimates the dog's emotions and provides the owner with appropriate advice based on the estimated emotions. For example, if the dog is feeling stressed, the advice providing unit may advise the owner to create a relaxing environment. For example, the advice providing unit may provide specific advice such as, "The dog seems to be feeling stressed. Let the dog rest in a quiet place." In addition, if the dog is excited, the advice providing unit may advise the owner to take appropriate measures. For example, the advice providing unit may provide advice such as, "The dog is excited. Let the dog play with a toy."

[0080] The dog voice diagnosis system may further include a notification unit that estimates the dog's emotions and notifies the owner based on the estimated emotions. For example, the notification unit may immediately notify the owner if the dog is feeling scared. For example, it may send a notification such as, "The dog is feeling scared. Please move the dog to a safe place." The notification unit may also notify the owner if the dog is relaxed. For example, it may send a notification such as, "The dog is relaxed. Please continue to maintain this environment."

[0081] The dog voice diagnosis system may further include a training suggestion unit that estimates the dog's emotions and suggests an appropriate training method to the owner based on the estimated emotions. For example, if the dog is excited, the training suggestion unit may suggest an appropriate training method to the owner. For example, it may suggest a specific training method such as, "Your dog is excited. Try the following training to calm him down." In addition, if the dog is feeling scared, the training suggestion unit may suggest a training method to the owner to alleviate the fear. For example, it may suggest, "Your dog is feeling scared. Try the following training to alleviate the fear."

[0082] The dog voice diagnosis system may further include a care suggestion unit that estimates the dog's emotions and suggests an appropriate care method to the owner based on the estimated emotions. For example, if the dog is feeling stressed, the care suggestion unit may suggest an appropriate care method to the owner. For example, it may suggest a specific care method such as, "Your dog is feeling stressed. Please try the following care method." Furthermore, if the dog is relaxed, the care suggestion unit may also suggest a care method to maintain that state. For example, it may suggest, "Your dog is relaxed. Please continue the following care method."

[0083] The dog voice diagnosis system may further include a game suggestion unit that estimates the dog's emotions and suggests appropriate ways to play to the owner based on the estimated emotions. For example, if the dog is excited, the game suggestion unit may suggest appropriate ways to play to the owner. For example, it may suggest a specific game such as, "Your dog is excited. Try the following game." In addition, if the dog is relaxed, the game suggestion unit may suggest a game to maintain that state. For example, it may suggest, "Your dog is relaxed. Continue the following game."

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The sound collection unit collects the dog's voice. For example, a microphone can be used to collect the dog's barks and cries. The sound collection unit can also record the dog's voice using a smartphone app. Furthermore, the sound collection unit can also constantly monitor the dog's voice using a wearable device. For example, a microphone can be built into the dog's collar, and the dog's voice can be collected 24 hours a day. Step 2: The analysis unit analyzes the collected voices. For example, it analyzes the voice waveform and extracts voice patterns and characteristics. The analysis unit can also perform frequency analysis to analyze the pitch and duration of the voice. Furthermore, the analysis unit can use an emotion estimation function to extract parts of the collected voices that express particularly strong emotions and focus on analyzing them. For example, it can identify parts that express strong emotions such as excitement or fear and focus on analyzing those parts. Step 3: The emotion determination unit determines the dog's emotion based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether the dog is excited, afraid, or relaxed. The input to the generation AI is the voice pattern analysis result, and the generation AI determines the emotion based on that result. Step 4: The anomaly detection unit detects abnormalities in the dog's health based on the voice patterns analyzed by the analysis unit. For example, the generation AI uses a pre-fine-tuned model to determine whether there are any abnormalities in the dog's health, such as if the dog is coughing or has a hoarse voice. The input to the generation AI is the voice pattern analysis result, and the generation AI detects abnormalities in the dog's health based on the results. Step 5: The notification unit notifies the owner of the results determined by the emotion determination unit and anomaly detection unit. For example, the notification unit sends a notification via a smartphone app. It also provides appropriate measures and advice. For example, it may provide advice such as, "Your dog seems to be feeling stressed. Please create a relaxing environment." The input to the generation AI is the emotion and health status determination results, and the generation AI generates the notification content and advice based on those results.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 sound collection unit that collects dog voices; an analysis unit that analyzes the voice collected by the voice collection unit; an emotion determination unit that determines the emotion of the dog based on the voice pattern analyzed by the analysis unit; an abnormality detection unit that detects abnormalities in the dog's health condition based on the voice pattern analyzed by the analysis unit; a notification unit that notifies the owner of the results determined by the emotion determination unit and the abnormality detection unit. A system characterized by:

2. The sound collection unit Introducing filtering technology that automatically removes environmental and background noise 2. The system of claim 1.

3. The analysis unit Uses specialized voice analysis models for different dog breeds and age groups 2. The system of claim 1.

4. The abnormality detection unit Compare with past health data to detect abnormalities early 2. The system of claim 1.

5. The notification unit Customize notifications based on your dog's individual personality and past behavior patterns 2. The system of claim 1.

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