system

Abstract

To enable real-time monitoring of a cat's health status and emotional state.SOLUTION: A system according to an embodiment includes a sensor unit, an analysis unit, a transmission unit, and a display unit. The sensor unit collects the cat's body temperature, heart rate, and movements. The analysis unit interprets the data collected by the sensor unit to determine the cat's physical condition. The transmission unit transmits the analysis result obtained by the analysis unit to a smartphone. The display unit displays the analysis result transmitted by the transmission unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to grasp a cat's physical condition and mood in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to grasp the physical condition and mood of a cat in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, a transmission unit, and a display unit. The sensor unit collects the cat's body temperature, heart rate, and movements. The analysis unit analyzes the data collected by the sensor unit and determines the cat's physical condition. The transmission unit transmits the analysis results obtained by the analysis unit to a smartphone. The display unit displays the analysis results transmitted by the transmission unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the physical condition and mood of a cat in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system for determining a cat's physical condition and mood according to an embodiment of the present invention is a collar that uses AI to determine the cat's physical condition and mood and transmits the information to a smartphone. The system includes a sensor unit that collects the cat's body temperature, heart rate, and movements; an analysis unit that analyzes the data collected by the sensor unit and determines the cat's physical condition and mood; a transmission unit that transmits the analysis results obtained by the analysis unit to a smartphone; and a display unit that displays the analysis results transmitted by the transmission unit. For example, the sensor unit includes a body temperature sensor, a heart rate sensor, and an acceleration sensor to collect data such as the cat's body temperature, heart rate, and movements. The analysis unit analyzes the collected data using a machine learning algorithm to determine the cat's physical condition and mood. For example, a high body temperature may indicate a fever, and a fast heart rate may indicate agitation. The transmission unit transmits the analysis results to a smartphone using Bluetooth (registered trademark), allowing the owner to check the cat's condition in real time. The display unit displays the analysis results transmitted to the smartphone, making it easier for the owner to understand the cat's physical condition and mood. Additionally, the cat language translation option allows users to analyze a cat's meows and display their meaning in text. For example, messages such as "I'm hungry" or "I want to play" are displayed. This system makes it easier for owners to understand their cat's physical condition and mood, allowing them to provide appropriate care. This allows the system to determine a cat's physical condition and mood in real time and send it to a smartphone.

[0029] A system for determining a cat's physical condition and mood according to an embodiment includes a sensor unit, an analysis unit, a transmission unit, and a display unit. The sensor unit collects the cat's body temperature, heart rate, and movement. For example, the sensor unit includes a body temperature sensor, a heart rate sensor, and an acceleration sensor, and collects data such as the cat's body temperature, heart rate, and movement. The body temperature sensor is placed, for example, on a part worn around the cat's neck to measure the cat's body temperature. The heart rate sensor is placed, for example, on the cat's chest to measure the heart rate. The acceleration sensor is placed, for example, inside the cat's collar to detect the cat's movement. The analysis unit analyzes the collected data and determines the cat's physical condition and mood. For example, the analysis unit analyzes the data using a machine learning algorithm. The machine learning algorithm may use techniques such as deep learning and support vector machines. The analysis unit determines that a high body temperature indicates a possible fever, and that a fast heart rate indicates a possible agitation. The transmission unit transmits the analysis results to a smartphone. For example, the transmission unit transmits the data using Bluetooth. Bluetooth may use versions such as Bluetooth 4.0 or Bluetooth Low Energy. The transmitter transmits the analysis results to a smartphone in real time, allowing the owner to instantly check the cat's condition. The display displays the analysis results transmitted to the smartphone. For example, the display may have a function to display data in real time. The display may also have a function to store and analyze past data, enabling long-term health management of the cat. As a result, the system for determining a cat's physical condition and mood according to the embodiment can determine the cat's physical condition and mood in real time and transmit the data to a smartphone.

[0030] The sensor unit collects the cat's body temperature, heart rate, and movement. For example, the sensor unit may incorporate a body temperature sensor, a heart rate sensor, and an acceleration sensor to collect data such as the cat's body temperature, heart rate, and movement. The body temperature sensor is placed, for example, on a part worn around the cat's neck to measure the cat's body temperature. The body temperature sensor can measure the cat's body temperature non-contact using infrared technology, allowing accurate body temperature data to be obtained without stressing the cat. The heart rate sensor is placed, for example, on the cat's chest to measure the heart rate. The heart rate sensor uses photoplethysmography (PPG) to detect changes in blood flow through the skin and measure the heart rate. The acceleration sensor is placed, for example, inside the cat's collar to detect the cat's movement. The acceleration sensor uses a triaxial accelerometer to record the direction and intensity of the cat's movement in detail. This allows the sensor unit to collect data on the cat's body temperature, heart rate, and movement with high precision, providing basic data for accurately understanding the cat's physical condition and mood. Furthermore, the sensor unit is equipped with a memory that temporarily stores collected data, preventing data loss. The sensor unit is designed to be flexible enough to follow the cat's movements, so data collection is not hindered even if the cat moves around freely. This allows the sensor unit to continuously collect data without interfering with the cat's natural behavior.

[0031] The analysis unit analyzes the collected data to determine the cat's physical condition and mood. For example, the analysis unit uses a machine learning algorithm to analyze the data. Machine learning algorithms can use technologies such as deep learning and support vector machines. Deep learning uses multilayer neural networks to automatically extract data features and perform advanced pattern recognition. Support vector machines demonstrate excellent performance in data classification and regression analysis, allowing for highly accurate determination of a cat's physical condition and mood. The analysis unit determines that a high body temperature indicates a possible fever, and a fast heart rate indicates a possible agitation. Furthermore, the analysis unit can analyze the cat's movement patterns and detect abnormal movements or changes in behavior. For example, if a cat is moving less than usual, it can be determined to be a sign of poor health or stress. By comparing the data with past data, the analysis unit can monitor changes in the cat's physical condition and mood over the long term and issue an early warning if an abnormality occurs. This allows the analysis unit to quickly and accurately analyze the collected data and determine the cat's physical condition and mood in real time. Furthermore, the analysis unit has a function to visualize the results of data analysis, allowing owners to intuitively understand them. This allows the analysis unit to support cat health management and provide owners with information to take appropriate action.

[0032] The transmitter transmits the analysis results to a smartphone. For example, the transmitter transmits data using Bluetooth. Bluetooth versions, such as Bluetooth 4.0 and Bluetooth Low Energy, can be used. Bluetooth Low Energy allows data transmission with low power consumption, extending the battery life of the cat collar. The transmitter transmits the analysis results to the smartphone in real time, allowing owners to instantly check their cat's condition. The transmitter has an error-checking function to ensure stable communication and prevent data transmission interruptions. Furthermore, the transmitter has a data encryption function to ensure the security of transmitted data. This allows the transmitter to safely and securely transmit the analysis results to the smartphone, allowing owners to monitor their cat's physical condition and mood in real time. The transmitter works in conjunction with a smartphone app and can receive data even when the app is running in the background. This allows owners to constantly monitor their cat's condition and respond immediately if an abnormality occurs. The transmitter also has a function to adjust the data transmission frequency, allowing owners to set the optimal transmission frequency based on their cat's activity level and remaining battery power. This allows the transmitter to transmit data efficiently, improving the performance of the entire system.

[0033] The display unit displays the analysis results sent to the smartphone. For example, the display unit has the ability to display data in real time. The display unit also has the ability to store and analyze past data, enabling long-term cat health management. The display unit visualizes the analysis results in graphs and charts, allowing owners to intuitively understand their cat's condition. For example, it can display fluctuations in body temperature and heart rate as a line graph and the cat's activity level as a bar graph. The display unit also has the ability to display alerts to alert owners if abnormalities are detected. For example, if the body temperature exceeds a certain range or the heart rate fluctuates rapidly, an alert will be displayed, prompting owners to take early action. The display unit has a data storage function, allowing owners to refer to past data to monitor changes in their cat's physical condition and mood over the long term. This allows owners to comprehensively understand their cat's health and take appropriate measures. Furthermore, the display unit has a data sharing function, allowing data to be shared with veterinarians and other owners. This allows owners to manage their cat's health while consulting with expert advice. The display unit has an intuitive and easy-to-use user interface, allowing owners to operate it easily. This allows owners to understand their cat's physical condition and mood in real time and provides them with information to take appropriate action.

[0034] The analysis unit can analyze the data using a machine learning algorithm. The analysis unit analyzes the data using a machine learning algorithm such as deep learning or a support vector machine. Deep learning analyzes data using a multi-layer neural network and can accurately determine a cat's physical condition and mood. Support vector machines are used for data classification and regression analysis and can accurately determine a cat's physical condition and mood. For example, when deep learning is used, the analysis unit uses a neural network model that receives data such as the cat's body temperature, heart rate, and movement as input and outputs the cat's physical condition and mood. When a support vector machine is used, the analysis unit uses a support vector machine model that receives data such as the cat's body temperature, heart rate, and movement as input and outputs the cat's physical condition and mood. In this way, the use of a machine learning algorithm improves the accuracy of data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The transmitter can transmit data using Bluetooth. The transmitter transmits data using versions such as Bluetooth 4.0 and Bluetooth Low Energy. Bluetooth 4.0 can transmit data with low power consumption, extending the battery life of the cat collar. Bluetooth Low Energy can transmit data with even lower power consumption, enabling longer data transmission periods. For example, the transmitter can use Bluetooth 4.0 to transmit data such as the cat's body temperature, heart rate, and movement to a smartphone. The transmitter can also use Bluetooth Low Energy to transmit analysis results to the smartphone in real time. This stabilizes data transmission using Bluetooth. Some or all of the above-described processing in the transmitter can be performed using, for example, AI, or without AI.

[0036] The display unit may have a function for displaying data in real time. The display unit displays the data in real time on, for example, a smartphone screen. For example, the display unit displays data such as a cat's body temperature, heart rate, and movement in real time, allowing the owner to instantly check the cat's condition. The display unit achieves real-time data display by setting a high data update frequency and minimizing delay time. For example, the display unit updates the data every second to instantly reflect changes in the cat's physical condition and mood. The display unit also devise a method for visually displaying the data so that the owner can intuitively understand the data. For example, the display unit can display the cat's body temperature in a graph and its heart rate in a chart. This allows the owner to instantly check the cat's condition by displaying the data in real time. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI.

[0037] The display unit may have a function for saving and analyzing past data. For example, the display unit may have a function for saving and analyzing past data. For example, the display unit may save data such as a cat's body temperature, heart rate, and movement for a certain period of time, and by analyzing the past data, long-term health management of the cat is possible. The display unit may set a storage period and automatically save data at regular intervals. For example, the display unit may save data once a week, retaining data from the past year. The display unit may also analyze the saved data to understand changes in the cat's physical condition and mood. For example, the display unit may display past data in graphs or charts, visually showing trends in the cat's physical condition and mood. This enables long-term health management of the cat by saving and analyzing past data. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI.

[0038] The analysis unit may include a cat language translation unit that analyzes cat meows and displays the analysis results in text. The analysis unit may include, for example, a cat language translation unit that analyzes cat meows and displays their meanings in text. The cat language translation unit may analyze cat meows using, for example, voice analysis technology and display their meanings in text. For example, the cat language translation unit may record a cat's meow and extract features of the meow using a voice analysis algorithm. The voice analysis algorithm may use technologies such as frequency analysis and volume analysis. The cat language translation unit may determine the meaning of the cat's meow based on the extracted features and display it in text. For example, the cat language translation unit may display messages such as "I'm hungry" or "I want to play." By analyzing a cat's meow and displaying its meaning in text, the owner can more easily understand their cat's requests. Some or all of the above-described processing in the cat language translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the cat language translation unit can input cat meow data into the generation AI and have the generation AI analyze the meaning of the meow.

[0039] The sensor unit can estimate the cat's emotions and adjust the sensitivity of the sensor based on the estimated emotions. For example, the sensor unit estimates the cat's emotions and adjusts the sensitivity of the sensor based on the estimated emotions. For example, if the cat is relaxed, the sensor unit can set the sensitivity of the sensor low to reduce the frequency of data collection. Furthermore, if the cat is excited, the sensor unit can set the sensitivity of the sensor high to increase the frequency of data collection. Furthermore, if the cat is stressed, the sensor unit can set the sensitivity of the sensor to a medium level to balance the data collection. This improves the accuracy of data collection by adjusting the sensitivity of the sensor based on the cat's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input cat emotion data into the generation AI, causing the generation AI to estimate the emotion and adjust the sensor sensitivity.

[0040] The sensor unit can analyze the cat's past physical condition data and select appropriate sensor placement. The sensor unit, for example, analyzes the cat's past physical condition data and selects optimal sensor placement. For example, the sensor unit can optimize the position of the body temperature sensor based on the cat's past body temperature data. The sensor unit can also optimize the position of the heart rate sensor based on the cat's past heart rate data. Furthermore, the sensor unit can optimize the position of the acceleration sensor based on the cat's past movement data. This improves the accuracy of data collection by optimizing sensor placement based on past physical condition data. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the cat's past physical condition data into the generation AI and cause the generation AI to select optimal sensor placement.

[0041] The sensor unit may have a function for automatically adjusting the data collection frequency according to the cat's activity level. The sensor unit automatically adjusts the data collection frequency according to the cat's activity level, for example. For example, the sensor unit sets the data collection frequency high when the cat is actively moving. Furthermore, the sensor unit can set the data collection frequency low when the cat is resting. Furthermore, the sensor unit can set the data collection frequency to medium when the cat is moderately active. This enables efficient data collection by adjusting the data collection frequency according to the cat's activity level. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI, for example. For example, the sensor unit may input the cat's activity level data into the generation AI and cause the generation AI to automatically adjust the data collection frequency.

[0042] The sensor unit can add sensors that measure the cat's environmental temperature and humidity to improve the accuracy of physical condition determination. The sensor unit can add sensors that measure the cat's environmental temperature and humidity to improve the accuracy of physical condition determination. For example, the sensor unit can add an environmental temperature sensor and compare it with the cat's body temperature data to determine the physical condition. The sensor unit can also add an environmental humidity sensor and compare it with the cat's breathing data to determine the physical condition. Furthermore, the sensor unit can combine environmental temperature and humidity data to more accurately determine the cat's physical condition. Thus, measuring the environmental temperature and humidity improves the accuracy of physical condition determination. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, the sensor unit can input environmental temperature and humidity data into the generation AI and cause the generation AI to improve the accuracy of physical condition determination.

[0043] The sensor unit can have a function to monitor the cat's diet and water intake. The sensor unit can have a function to monitor the cat's diet and water intake, for example. For example, the sensor unit can add a diet sensor to monitor the cat's diet. Also, the sensor unit can add a water intake sensor to monitor the cat's water intake. Furthermore, the sensor unit can combine data on diet and water intake to determine the cat's physical condition. This allows for more accurate health management of the cat by monitoring the diet and water intake. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input data on the cat's diet and water intake into the generation AI and have the generation AI perform physical condition determination.

[0044] The analysis unit can estimate the cat's emotions and adjust the analysis algorithm based on the estimated cat emotions. The analysis unit, for example, estimates the cat's emotions and adjusts the analysis algorithm based on the estimated cat emotions. For example, the analysis unit can set the analysis algorithm to be gentle when the cat is relaxed. Furthermore, the analysis unit can set the analysis algorithm to be fast when the cat is excited. Furthermore, the analysis unit can set the analysis algorithm to be moderate when the cat is stressed. This improves the analysis accuracy by adjusting the analysis algorithm based on the cat's emotions. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the cat's emotion data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the cat's past health data. The analysis unit can improve the accuracy of the analysis by referring to the cat's past health data, for example. For example, the analysis unit can refer to the cat's past body temperature data and compare it with current body temperature data for analysis. The analysis unit can also refer to the cat's past heart rate data and compare it with current heart rate data for analysis. Furthermore, the analysis unit can refer to the cat's past movement data and compare it with current movement data for analysis. In this way, by referring to the past health data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the cat's past health data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit may have a function to detect specific behavioral patterns of the cat and detect abnormalities early. The analysis unit may have a function to detect specific behavioral patterns of the cat and detect abnormalities early. For example, the analysis unit may detect an abnormality when the cat is moving abnormally. The analysis unit may also detect an abnormality when the cat's heart rate is abnormal. Furthermore, the analysis unit may detect an abnormality when the cat's body temperature is abnormal. By detecting specific behavioral patterns, abnormalities can be detected early. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input cat behavioral data into the generation AI and cause the generation AI to perform early detection of abnormalities.

[0047] The analysis unit can estimate the cat's emotions and adjust the display method of the analysis results based on the estimated cat emotions. The analysis unit, for example, estimates the cat's emotions and adjusts the display method of the analysis results based on the estimated cat emotions. For example, if the cat is relaxed, the analysis unit can display the analysis results simply. If the cat is excited, the analysis unit can display the analysis results in detail. If the cat is stressed, the analysis unit can display the analysis results moderately. This makes the analysis results easier to understand by adjusting the display method based on the cat's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the cat's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0048] The analysis unit can improve the accuracy of the analysis by incorporating feedback from the cat owner. The analysis unit can improve the accuracy of the analysis by incorporating feedback from the cat owner, for example. For example, the analysis unit reflects information about the cat's physical condition provided by the owner in the analysis. The analysis unit can also reflect information about the cat's behavior provided by the owner in the analysis. Furthermore, the analysis unit can reflect information about the cat's diet provided by the owner in the analysis. In this way, by incorporating feedback from the owner, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the owner's feedback data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0049] The analysis unit can analyze the frequency and volume of a cat's meows to help determine its physical condition and mood. The analysis unit can, for example, analyze the frequency and volume of a cat's meows to help determine its physical condition and mood. For example, the analysis unit can analyze the frequency of a cat's meows to determine its physical condition. The analysis unit can also analyze the volume of a cat's meows to determine its mood. Furthermore, the analysis unit can analyze the pattern of a cat's meows to determine its physical condition and mood. As a result, analyzing the frequency and volume of the meows can more accurately determine its physical condition and mood. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input cat meow data into a generation AI and have the generation AI determine its physical condition and mood.

[0050] The transmitting unit can estimate the cat's emotions and adjust the frequency of data transmission based on the estimated cat emotions. The transmitting unit, for example, estimates the cat's emotions and adjusts the frequency of data transmission based on the estimated cat emotions. For example, the transmitting unit sets the data transmission frequency low when the cat is relaxed. Furthermore, the transmitting unit can set the data transmission frequency high when the cat is excited. Furthermore, the transmitting unit can set the data transmission frequency to medium when the cat is stressed. This enables efficient data transmission by adjusting the data transmission frequency based on the cat's emotions. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the cat's emotion data to the generating AI and cause the generating AI to adjust the data transmission frequency.

[0051] The transmitting unit can determine the transmission priority based on the importance of the data. The transmitting unit determines the transmission priority based on, for example, the importance of the data. For example, if the body temperature data is abnormal, the transmitting unit transmits it with the highest priority. Also, if the heart rate data is abnormal, the transmitting unit can transmit it with priority. Furthermore, if the movement data is abnormal, the transmitting unit can transmit it with normal priority. In this way, by determining the transmission priority based on the importance of the data, important data is transmitted with priority. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the importance of the data to the generating AI and have the generating AI determine the transmission priority.

[0052] The transmitting unit can encrypt data to enhance security. The transmitting unit can, for example, encrypt data to enhance security. For example, the transmitting unit can encrypt and transmit body temperature data. The transmitting unit can also encrypt and transmit heart rate data. The transmitting unit can also encrypt and transmit movement data. In this way, data encryption enhances security. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input an encryption algorithm to a generating AI and have the generating AI encrypt the data.

[0053] The transmitting unit can estimate the cat's emotions and adjust the content of the transmission data based on the estimated cat emotions. The transmitting unit, for example, estimates the cat's emotions and adjusts the content of the transmission data based on the estimated cat emotions. For example, if the cat is relaxed, the transmitting unit transmits only basic data. Also, if the cat is excited, the transmitting unit can transmit detailed data. Furthermore, if the cat is stressed, the transmitting unit can transmit moderate data. In this way, by adjusting the content of the transmission data based on the cat's emotions, necessary information is appropriately transmitted. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the cat's emotion data into a generating AI and have the generating AI adjust the content of the transmission data.

[0054] The transmitting unit may have an option to transmit data using Wi-Fi or a mobile network. The transmitting unit may have an option to transmit data using, for example, Wi-Fi or a mobile network. For example, the transmitting unit may transmit data using Wi-Fi. The transmitting unit may also transmit data using a mobile network. Furthermore, the transmitting unit may transmit data using both Wi-Fi and a mobile network. This improves the flexibility of data transmission by using Wi-Fi and a mobile network. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit may input connection settings for Wi-Fi or a mobile network to the generating AI and cause the generating AI to execute option settings for data transmission.

[0055] The transmitting unit can compress data to improve the transmission speed. The transmitting unit can compress data to improve the transmission speed, for example. For example, the transmitting unit can compress and transmit body temperature data. The transmitting unit can also compress and transmit heart rate data. The transmitting unit can also compress and transmit movement data. This improves the transmission speed by compressing the data. Some or all of the above-described processing in the transmitting unit can be performed using AI, for example, or can be performed without using AI. For example, the transmitting unit can input a compression algorithm to the generating AI and have the generating AI compress the data.

[0056] The display unit can estimate the cat's emotions and customize the display content based on the estimated cat emotions. The display unit, for example, estimates the cat's emotions and customizes the display content based on the estimated cat emotions. For example, the display unit provides simple display content when the cat is relaxed. Furthermore, the display unit can provide detailed display content when the cat is excited. Furthermore, the display unit can provide moderate display content when the cat is stressed. By customizing the display content based on the cat's emotions, it is possible to provide information that is easier for the owner to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the cat's emotion data into the generation AI and have the generation AI customize the display content.

[0057] The display unit may have a function to visually display past data in graphs or charts. The display unit may have a function to visually display past data in graphs or charts, for example. For example, the display unit may display a cat's body temperature data in a graph. The display unit may also display a cat's heart rate data in a chart. The display unit may also display a cat's movement data in a graph. This makes it easier to understand the data by visually displaying past data. Some or all of the above-described processing in the display unit may be performed using, or without, AI, for example. For example, the display unit may input past data into a generation AI and cause the generation AI to generate graphs or charts.

[0058] The display unit can have an alert function according to the cat's health condition. The display unit has an alert function according to the cat's health condition, for example. For example, the display unit displays an alert if the cat's body temperature is abnormal. The display unit can also display an alert if the cat's heart rate is abnormal. Furthermore, the display unit can display an alert if the cat's movements are abnormal. This allows the owner to respond quickly by displaying an alert according to the cat's health condition. Some or all of the above-mentioned processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the cat's health data into the generation AI and cause the generation AI to display an alert.

[0059] The display unit can estimate the cat's emotions and adjust the display interface based on the estimated cat emotions. For example, the display unit can provide a calming interface when the cat is relaxed. Furthermore, the display unit can provide a brightly colored interface when the cat is excited. Furthermore, the display unit can provide a simple, highly visible interface when the cat is stressed. This allows the owner to more comfortably view information by adjusting the display interface based on the cat's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the cat's emotion data into the generation AI and have the generation AI adjust the display interface.

[0060] The display unit can customize the display method according to the notification settings of the owner's smartphone. The display unit customizes the display method according to, for example, the notification settings of the owner's smartphone. For example, the display unit notifies the owner by vibration when the owner's smartphone is in silent mode. Furthermore, the display unit can notify the owner by voice when the owner's smartphone is in normal mode. Furthermore, the display unit can suppress notifications when the owner's smartphone is in do-not-disturb mode. In this way, by customizing the display method according to the notification settings of the owner's smartphone, notifications are appropriately performed. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input notification setting data of the smartphone to a generation AI and cause the generation AI to customize the display method.

[0061] The display unit can have a function to simultaneously display data for multiple cats. The display unit can, for example, simultaneously display data for multiple cats. For example, the display unit can simultaneously display body temperature data for multiple cats. The display unit can also simultaneously display heart rate data for multiple cats. Furthermore, the display unit can simultaneously display movement data for multiple cats. By simultaneously displaying data for multiple cats, the owner can check the condition of multiple cats at once. Some or all of the above-described processing in the display unit can be performed, for example, using AI, or can be performed without using AI. For example, the display unit can input data for multiple cats into the generation AI and have the generation AI execute the simultaneous display settings.

[0062] The cat language translation unit can estimate the cat's emotions and adjust the way the translation result is expressed based on the estimated cat emotions. The cat language translation unit, for example, estimates the cat's emotions and adjusts the way the translation result is expressed based on the estimated cat emotions. For example, the cat language translation unit can display a simple translation result if the cat is relaxed. Furthermore, the cat language translation unit can display a detailed translation result if the cat is excited. Furthermore, the cat language translation unit can display a medium translation result if the cat is stressed. This allows the way the translation result is expressed to be adjusted based on the cat's emotions, thereby providing a translation result that is easier for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, AI, or without AI. For example, the cat language translation unit can input cat emotion data into the generation AI and have the generation AI adjust the way the translation results are expressed.

[0063] The cat language translation unit can improve translation accuracy by referring to past cat meow data. The cat language translation unit improves translation accuracy by referring to, for example, past cat meow data. For example, the cat language translation unit refers to past cat meow data and compares it with the current meow to perform translation. The cat language translation unit can also refer to past cat meow patterns and compare them with the current meow to perform translation. Furthermore, the cat language translation unit can refer to the frequency of past cat meows and compare them with the current meow to perform translation. In this way, by referring to past meow data, translation accuracy is improved. Some or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cat language translation unit can input past cat meow data into a generation AI and cause the generation AI to improve translation accuracy.

[0064] The cat language translation unit may have a function to detect specific cat meow patterns and display the meaning in more detail. The cat language translation unit may have a function to detect specific cat meow patterns and display the meaning in more detail. For example, the cat language translation unit may detect specific cat meow patterns and display "I'm hungry." The cat language translation unit may also detect specific cat meow patterns and display "I want to play." The cat language translation unit may also detect specific cat meow patterns and display "I'm sleepy." In this way, by detecting specific meow patterns, the meaning can be displayed in more detail. Some or all of the above-described processing in the cat language translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cat language translation unit may input cat meow data into a generation AI and cause the generation AI to detect specific meow patterns and display the meaning.

[0065] The cat language translation unit can estimate the cat's emotions and adjust the display method of the translation result based on the estimated cat emotions. The cat language translation unit, for example, estimates the cat's emotions and adjusts the display method of the translation result based on the estimated cat emotions. For example, the cat language translation unit can provide a simple display method when the cat is relaxed. Furthermore, the cat language translation unit can provide a detailed display method when the cat is excited. Furthermore, the cat language translation unit can provide a medium display method when the cat is stressed. This allows the display method of the translation result to be adjusted based on the cat's emotions, thereby providing a translation result that is easier for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the cat language translation unit may be performed using AI, for example, or without AI. For example, the cat language translation unit can input cat emotion data into the generation AI and have the generation AI adjust the display method of the translation result.

[0066] The cat language translation unit can analyze the volume and frequency of a cat's meow to improve translation accuracy. The cat language translation unit can analyze, for example, the volume and frequency of a cat's meow to improve translation accuracy. For example, the cat language translation unit can analyze the volume of a cat's meow to improve translation accuracy. The cat language translation unit can also analyze the frequency of a cat's meow to improve translation accuracy. Furthermore, the cat language translation unit can analyze the pattern of a cat's meow to improve translation accuracy. In this way, by analyzing the volume and frequency of the meow, translation accuracy is improved. Some or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cat language translation unit can input cat's meow data into a generation AI and have the generation AI analyze the volume and frequency.

[0067] The cat language translation unit can have a function to display the translation result in multiple languages. The cat language translation unit can, for example, have a function to display the translation result in multiple languages. For example, the cat language translation unit displays the translation result in English. The cat language translation unit can also display the translation result in Japanese. The cat language translation unit can also display the translation result in Chinese. This makes it possible to accommodate owners who speak different languages ​​by displaying the translation result in multiple languages. Some or all of the above-mentioned processing in the cat language translation unit may be performed using AI, for example, or may be performed without using AI. For example, the cat language translation unit can input the translation result to a generation AI and have the generation AI display the result in multiple languages.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The system for determining a cat's physical condition and mood can further be equipped with the function of learning the cat's behavioral patterns and detecting abnormal behavior. For example, the analysis unit can detect abnormal behavior if the cat is moving in a way that is different from normal. The analysis unit can also detect abnormal behavior if the cat's food or water intake is different from normal. Furthermore, the analysis unit can detect abnormal behavior if the cat's meowing pattern is different from normal. This allows the system to learn the cat's behavioral patterns and detect abnormal behavior early, allowing the owner to take prompt action.

[0070] The system for determining a cat's physical condition and mood can further include a function for monitoring the cat's sleep pattern and evaluating the quality of sleep. For example, the sensor unit monitors the cat's movements and heart rate and analyzes the sleep pattern. The analysis unit can evaluate the quality of sleep based on the cat's body temperature data. Furthermore, the analysis unit can analyze the cat's meow data and evaluate the quality of sleep. In this way, monitoring the cat's sleep pattern and evaluating the quality of sleep can help owners manage their cat's health.

[0071] The system for determining a cat's physical condition and mood can further include a function for monitoring the cat's amount of exercise and providing advice for maintaining an appropriate amount of exercise. For example, the sensor unit monitors the cat's movements and measures the amount of exercise. The analysis unit can analyze the data on the cat's amount of exercise and evaluate the appropriate amount of exercise. Furthermore, the analysis unit can evaluate the amount of exercise based on the cat's weight data. This makes it possible to monitor the cat's amount of exercise and provide advice for maintaining an appropriate amount of exercise, thereby making cat health management more effective.

[0072] The system for determining a cat's physical condition and mood can further include a function for monitoring the cat's eating pattern and evaluating the quality of the diet. For example, the sensor unit monitors the amount of food the cat eats and analyzes the eating pattern. The analysis unit can also evaluate the quality of the diet based on the cat's weight data. The analysis unit can also evaluate the quality of the diet based on the cat's body temperature data. This allows owners to monitor the cat's eating pattern and evaluate the quality of the diet, which can be useful for managing the health of their cats.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The sensor unit collects the cat's body temperature, heart rate, and movements. For example, the sensor unit may have a built-in body temperature sensor, heart rate sensor, and acceleration sensor, and collect data such as the cat's body temperature, heart rate, and movements. The body temperature sensor is, for example, placed on a part worn around the cat's neck to measure the cat's body temperature. The heart rate sensor is, for example, placed on the cat's chest to measure the heart rate. The acceleration sensor is, for example, placed inside the collar to detect the cat's movements. Step 2: The analysis unit analyzes the collected data and determines the cat's physical condition and mood. For example, the analysis unit analyzes the data using a machine learning algorithm. The machine learning algorithm can use technologies such as deep learning and support vector machines. The analysis unit determines that a high body temperature may indicate a fever, and that a fast heart rate may indicate excitement. Step 3: The transmitter transmits the analysis results to a smartphone. For example, the transmitter transmits data using Bluetooth. Bluetooth versions such as Bluetooth 4.0 and Bluetooth Low Energy can be used. The transmitter transmits the analysis results to the smartphone in real time, allowing the owner to instantly check the cat's condition. Step 4: The display unit displays the analysis results sent to the smartphone. For example, the display unit has the function of displaying data in real time. The display unit also has the function of saving and analyzing past data, allowing for long-term health management of cats.

[0075] (Example 2) A system for determining a cat's physical condition and mood according to an embodiment of the present invention is a collar that uses AI to determine the cat's physical condition and mood and transmits the information to a smartphone. The system includes a sensor unit that collects the cat's body temperature, heart rate, and movement; an analysis unit that analyzes the data collected by the sensor unit and determines the cat's physical condition and mood; a transmission unit that transmits the analysis results obtained by the analysis unit to a smartphone; and a display unit that displays the analysis results transmitted by the transmission unit. For example, the sensor unit includes a body temperature sensor, a heart rate sensor, and an acceleration sensor to collect data such as the cat's body temperature, heart rate, and movement. The analysis unit analyzes the collected data using a machine learning algorithm to determine the cat's physical condition and mood. For example, a high body temperature may indicate a fever, and a fast heart rate may indicate agitation. The transmission unit transmits the analysis results to a smartphone using Bluetooth, allowing the owner to check the cat's condition in real time. The display unit displays the analysis results transmitted to the smartphone, making it easier for the owner to understand the cat's physical condition and mood. Additionally, the cat language translation option allows users to analyze a cat's meows and display their meaning in text. For example, messages such as "I'm hungry" or "I want to play" are displayed. This system makes it easier for owners to understand their cat's physical condition and mood, allowing them to provide appropriate care. This allows the system to determine a cat's physical condition and mood in real time and send it to a smartphone.

[0076] A system for determining a cat's physical condition and mood according to an embodiment includes a sensor unit, an analysis unit, a transmission unit, and a display unit. The sensor unit collects the cat's body temperature, heart rate, and movement. For example, the sensor unit includes a body temperature sensor, a heart rate sensor, and an acceleration sensor, and collects data such as the cat's body temperature, heart rate, and movement. The body temperature sensor is placed, for example, on a part worn around the cat's neck to measure the cat's body temperature. The heart rate sensor is placed, for example, on the cat's chest to measure the heart rate. The acceleration sensor is placed, for example, inside the cat's collar to detect the cat's movement. The analysis unit analyzes the collected data and determines the cat's physical condition and mood. For example, the analysis unit analyzes the data using a machine learning algorithm. The machine learning algorithm may use techniques such as deep learning and support vector machines. The analysis unit determines that a high body temperature indicates a possible fever, and that a fast heart rate indicates a possible agitation. The transmission unit transmits the analysis results to a smartphone. For example, the transmission unit transmits the data using Bluetooth. Bluetooth may use versions such as Bluetooth 4.0 or Bluetooth Low Energy. The transmitter transmits the analysis results to a smartphone in real time, allowing the owner to instantly check the cat's condition. The display displays the analysis results transmitted to the smartphone. For example, the display may have a function to display data in real time. The display may also have a function to store and analyze past data, enabling long-term health management of the cat. As a result, the system for determining a cat's physical condition and mood according to the embodiment can determine the cat's physical condition and mood in real time and transmit the data to a smartphone.

[0077] The sensor unit collects the cat's body temperature, heart rate, and movement. For example, the sensor unit may incorporate a body temperature sensor, a heart rate sensor, and an acceleration sensor to collect data such as the cat's body temperature, heart rate, and movement. The body temperature sensor is placed, for example, on a part worn around the cat's neck to measure the cat's body temperature. The body temperature sensor can measure the cat's body temperature non-contact using infrared technology, allowing accurate body temperature data to be obtained without stressing the cat. The heart rate sensor is placed, for example, on the cat's chest to measure the heart rate. The heart rate sensor uses photoplethysmography (PPG) to detect changes in blood flow through the skin and measure the heart rate. The acceleration sensor is placed, for example, inside the cat's collar to detect the cat's movement. The acceleration sensor uses a triaxial accelerometer to record the direction and intensity of the cat's movement in detail. This allows the sensor unit to collect data on the cat's body temperature, heart rate, and movement with high precision, providing basic data for accurately understanding the cat's physical condition and mood. Furthermore, the sensor unit is equipped with a memory that temporarily stores collected data, preventing data loss. The sensor unit is designed to be flexible enough to follow the cat's movements, so data collection is not hindered even if the cat moves around freely. This allows the sensor unit to continuously collect data without interfering with the cat's natural behavior.

[0078] The analysis unit analyzes the collected data to determine the cat's physical condition and mood. For example, the analysis unit uses a machine learning algorithm to analyze the data. Machine learning algorithms can use technologies such as deep learning and support vector machines. Deep learning uses multilayer neural networks to automatically extract data features and perform advanced pattern recognition. Support vector machines demonstrate excellent performance in data classification and regression analysis, allowing for highly accurate determination of a cat's physical condition and mood. The analysis unit determines that a high body temperature indicates a possible fever, and a fast heart rate indicates a possible agitation. Furthermore, the analysis unit can analyze the cat's movement patterns and detect abnormal movements or changes in behavior. For example, if a cat is moving less than usual, it can be determined to be a sign of poor health or stress. By comparing the data with past data, the analysis unit can monitor changes in the cat's physical condition and mood over the long term and issue an early warning if an abnormality occurs. This allows the analysis unit to quickly and accurately analyze the collected data and determine the cat's physical condition and mood in real time. Furthermore, the analysis unit has a function to visualize the results of data analysis, allowing owners to intuitively understand them. This allows the analysis unit to support cat health management and provide owners with information to take appropriate action.

[0079] The transmitter transmits the analysis results to a smartphone. For example, the transmitter transmits data using Bluetooth. Bluetooth versions, such as Bluetooth 4.0 and Bluetooth Low Energy, can be used. Bluetooth Low Energy allows data transmission with low power consumption, extending the battery life of the cat collar. The transmitter transmits the analysis results to the smartphone in real time, allowing owners to instantly check their cat's condition. The transmitter has an error-checking function to ensure stable communication and prevent data transmission interruptions. Furthermore, the transmitter has a data encryption function to ensure the security of transmitted data. This allows the transmitter to safely and securely transmit the analysis results to the smartphone, allowing owners to monitor their cat's physical condition and mood in real time. The transmitter works in conjunction with a smartphone app and can receive data even when the app is running in the background. This allows owners to constantly monitor their cat's condition and respond immediately if an abnormality occurs. The transmitter also has a function to adjust the data transmission frequency, allowing owners to set the optimal transmission frequency based on their cat's activity level and remaining battery power. This allows the transmitter to transmit data efficiently, improving the performance of the entire system.

[0080] The display unit displays the analysis results sent to the smartphone. For example, the display unit has the ability to display data in real time. The display unit also has the ability to store and analyze past data, enabling long-term cat health management. The display unit visualizes the analysis results in graphs and charts, allowing owners to intuitively understand their cat's condition. For example, it can display fluctuations in body temperature and heart rate as a line graph and the cat's activity level as a bar graph. The display unit also has the ability to display alerts to alert owners if abnormalities are detected. For example, if the body temperature exceeds a certain range or the heart rate fluctuates rapidly, an alert will be displayed, prompting owners to take early action. The display unit has a data storage function, allowing owners to refer to past data to monitor changes in their cat's physical condition and mood over the long term. This allows owners to comprehensively understand their cat's health and take appropriate measures. Furthermore, the display unit has a data sharing function, allowing data to be shared with veterinarians and other owners. This allows owners to manage their cat's health while consulting with expert advice. The display unit has an intuitive and easy-to-use user interface, allowing owners to operate it easily. This allows owners to understand their cat's physical condition and mood in real time and provides them with information to take appropriate action.

[0081] The analysis unit can analyze the data using a machine learning algorithm. The analysis unit analyzes the data using a machine learning algorithm such as deep learning or a support vector machine. Deep learning analyzes data using a multi-layer neural network and can accurately determine a cat's physical condition and mood. Support vector machines are used for data classification and regression analysis and can accurately determine a cat's physical condition and mood. For example, when deep learning is used, the analysis unit uses a neural network model that receives data such as the cat's body temperature, heart rate, and movement as input and outputs the cat's physical condition and mood. When a support vector machine is used, the analysis unit uses a support vector machine model that receives data such as the cat's body temperature, heart rate, and movement as input and outputs the cat's physical condition and mood. In this way, the use of a machine learning algorithm improves the accuracy of data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The transmitter can transmit data using Bluetooth. The transmitter transmits data using versions such as Bluetooth 4.0 and Bluetooth Low Energy. Bluetooth 4.0 can transmit data with low power consumption, extending the battery life of the cat collar. Bluetooth Low Energy can transmit data with even lower power consumption, enabling longer data transmission periods. For example, the transmitter can use Bluetooth 4.0 to transmit data such as the cat's body temperature, heart rate, and movement to a smartphone. The transmitter can also use Bluetooth Low Energy to transmit analysis results to the smartphone in real time. This stabilizes data transmission using Bluetooth. Some or all of the above-described processing in the transmitter can be performed using, for example, AI, or without AI.

[0083] The display unit may have a function for displaying data in real time. The display unit displays the data in real time on, for example, a smartphone screen. For example, the display unit displays data such as a cat's body temperature, heart rate, and movement in real time, allowing the owner to instantly check the cat's condition. The display unit achieves real-time data display by setting a high data update frequency and minimizing delay time. For example, the display unit updates the data every second to instantly reflect changes in the cat's physical condition and mood. The display unit also devise a method for visually displaying the data so that the owner can intuitively understand the data. For example, the display unit can display the cat's body temperature in a graph and its heart rate in a chart. This allows the owner to instantly check the cat's condition by displaying the data in real time. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI.

[0084] The display unit may have a function for saving and analyzing past data. For example, the display unit may have a function for saving and analyzing past data. For example, the display unit may save data such as a cat's body temperature, heart rate, and movement for a certain period of time, and by analyzing the past data, long-term health management of the cat is possible. The display unit may set a storage period and automatically save data at regular intervals. For example, the display unit may save data once a week, retaining data from the past year. The display unit may also analyze the saved data to understand changes in the cat's physical condition and mood. For example, the display unit may display past data in graphs or charts, visually showing trends in the cat's physical condition and mood. This enables long-term health management of the cat by saving and analyzing past data. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI.

[0085] The analysis unit may include a cat language translation unit that analyzes cat meows and displays the analysis results in text. The analysis unit may include, for example, a cat language translation unit that analyzes cat meows and displays their meanings in text. The cat language translation unit may analyze cat meows using, for example, voice analysis technology and display their meanings in text. For example, the cat language translation unit may record a cat's meow and extract features of the meow using a voice analysis algorithm. The voice analysis algorithm may use technologies such as frequency analysis and volume analysis. The cat language translation unit may determine the meaning of the cat's meow based on the extracted features and display it in text. For example, the cat language translation unit may display messages such as "I'm hungry" or "I want to play." By analyzing a cat's meow and displaying its meaning in text, the owner can more easily understand their cat's requests. Some or all of the above-described processing in the cat language translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the cat language translation unit can input cat meow data into the generation AI and have the generation AI analyze the meaning of the meow.

[0086] The sensor unit can estimate the cat's emotions and adjust the sensitivity of the sensor based on the estimated emotions. For example, the sensor unit estimates the cat's emotions and adjusts the sensitivity of the sensor based on the estimated emotions. For example, if the cat is relaxed, the sensor unit can set the sensitivity of the sensor low to reduce the frequency of data collection. Furthermore, if the cat is excited, the sensor unit can set the sensitivity of the sensor high to increase the frequency of data collection. Furthermore, if the cat is stressed, the sensor unit can set the sensitivity of the sensor to a medium level to balance the data collection. This improves the accuracy of data collection by adjusting the sensitivity of the sensor based on the cat's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sensor unit may be performed using, for example, AI, or without AI. For example, the sensor unit can input cat emotion data into the generation AI, causing the generation AI to estimate the emotion and adjust the sensor sensitivity.

[0087] The sensor unit can analyze the cat's past physical condition data and select appropriate sensor placement. The sensor unit, for example, analyzes the cat's past physical condition data and selects optimal sensor placement. For example, the sensor unit can optimize the position of the body temperature sensor based on the cat's past body temperature data. The sensor unit can also optimize the position of the heart rate sensor based on the cat's past heart rate data. Furthermore, the sensor unit can optimize the position of the acceleration sensor based on the cat's past movement data. This improves the accuracy of data collection by optimizing sensor placement based on past physical condition data. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input the cat's past physical condition data into the generation AI and cause the generation AI to select optimal sensor placement.

[0088] The sensor unit may have a function for automatically adjusting the data collection frequency according to the cat's activity level. The sensor unit automatically adjusts the data collection frequency according to the cat's activity level, for example. For example, the sensor unit sets the data collection frequency high when the cat is actively moving. Furthermore, the sensor unit can set the data collection frequency low when the cat is resting. Furthermore, the sensor unit can set the data collection frequency to medium when the cat is moderately active. This enables efficient data collection by adjusting the data collection frequency according to the cat's activity level. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI, for example. For example, the sensor unit may input the cat's activity level data into the generation AI and cause the generation AI to automatically adjust the data collection frequency.

[0089] The sensor unit can add sensors that measure the cat's environmental temperature and humidity to improve the accuracy of physical condition determination. The sensor unit can add sensors that measure the cat's environmental temperature and humidity to improve the accuracy of physical condition determination. For example, the sensor unit can add an environmental temperature sensor and compare it with the cat's body temperature data to determine the physical condition. The sensor unit can also add an environmental humidity sensor and compare it with the cat's breathing data to determine the physical condition. Furthermore, the sensor unit can combine environmental temperature and humidity data to more accurately determine the cat's physical condition. Thus, measuring the environmental temperature and humidity improves the accuracy of physical condition determination. Some or all of the above-described processing in the sensor unit may be performed using, or without, AI. For example, the sensor unit can input environmental temperature and humidity data into the generation AI and cause the generation AI to improve the accuracy of physical condition determination.

[0090] The sensor unit can have a function to monitor the cat's diet and water intake. The sensor unit can have a function to monitor the cat's diet and water intake, for example. For example, the sensor unit can add a diet sensor to monitor the cat's diet. Also, the sensor unit can add a water intake sensor to monitor the cat's water intake. Furthermore, the sensor unit can combine data on diet and water intake to determine the cat's physical condition. This allows for more accurate health management of the cat by monitoring the diet and water intake. Some or all of the above-described processing in the sensor unit can be performed using, for example, AI, or without AI. For example, the sensor unit can input data on the cat's diet and water intake into the generation AI and have the generation AI perform physical condition determination.

[0091] The analysis unit can estimate the cat's emotions and adjust the analysis algorithm based on the estimated cat emotions. The analysis unit, for example, estimates the cat's emotions and adjusts the analysis algorithm based on the estimated cat emotions. For example, the analysis unit can set the analysis algorithm to be gentle when the cat is relaxed. Furthermore, the analysis unit can set the analysis algorithm to be fast when the cat is excited. Furthermore, the analysis unit can set the analysis algorithm to be moderate when the cat is stressed. This improves the analysis accuracy by adjusting the analysis algorithm based on the cat's emotions. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the cat's emotion data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0092] The analysis unit can improve the accuracy of the analysis by referring to the cat's past health data. The analysis unit can improve the accuracy of the analysis by referring to the cat's past health data, for example. For example, the analysis unit can refer to the cat's past body temperature data and compare it with current body temperature data for analysis. The analysis unit can also refer to the cat's past heart rate data and compare it with current heart rate data for analysis. Furthermore, the analysis unit can refer to the cat's past movement data and compare it with current movement data for analysis. In this way, by referring to the past health data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the cat's past health data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0093] The analysis unit may have a function to detect specific behavioral patterns of the cat and detect abnormalities early. The analysis unit may have a function to detect specific behavioral patterns of the cat and detect abnormalities early. For example, the analysis unit may detect an abnormality when the cat is moving abnormally. The analysis unit may also detect an abnormality when the cat's heart rate is abnormal. Furthermore, the analysis unit may detect an abnormality when the cat's body temperature is abnormal. By detecting specific behavioral patterns, abnormalities can be detected early. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input cat behavioral data into the generation AI and cause the generation AI to perform early detection of abnormalities.

[0094] The analysis unit can estimate the cat's emotions and adjust the display method of the analysis results based on the estimated cat emotions. The analysis unit, for example, estimates the cat's emotions and adjusts the display method of the analysis results based on the estimated cat emotions. For example, if the cat is relaxed, the analysis unit can display the analysis results simply. If the cat is excited, the analysis unit can display the analysis results in detail. If the cat is stressed, the analysis unit can display the analysis results moderately. This makes the analysis results easier to understand by adjusting the display method based on the cat's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the cat's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0095] The analysis unit can improve the accuracy of the analysis by incorporating feedback from the cat owner. The analysis unit can improve the accuracy of the analysis by incorporating feedback from the cat owner, for example. For example, the analysis unit reflects information about the cat's physical condition provided by the owner in the analysis. The analysis unit can also reflect information about the cat's behavior provided by the owner in the analysis. Furthermore, the analysis unit can reflect information about the cat's diet provided by the owner in the analysis. In this way, by incorporating feedback from the owner, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the owner's feedback data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] The analysis unit can analyze the frequency and volume of a cat's meows to help determine its physical condition and mood. The analysis unit can, for example, analyze the frequency and volume of a cat's meows to help determine its physical condition and mood. For example, the analysis unit can analyze the frequency of a cat's meows to determine its physical condition. The analysis unit can also analyze the volume of a cat's meows to determine its mood. Furthermore, the analysis unit can analyze the pattern of a cat's meows to determine its physical condition and mood. As a result, analyzing the frequency and volume of the meows can more accurately determine its physical condition and mood. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input cat meow data into a generation AI and have the generation AI determine its physical condition and mood.

[0097] The transmitting unit can estimate the cat's emotions and adjust the frequency of data transmission based on the estimated cat emotions. The transmitting unit, for example, estimates the cat's emotions and adjusts the frequency of data transmission based on the estimated cat emotions. For example, the transmitting unit sets the data transmission frequency low when the cat is relaxed. Furthermore, the transmitting unit can set the data transmission frequency high when the cat is excited. Furthermore, the transmitting unit can set the data transmission frequency to medium when the cat is stressed. This enables efficient data transmission by adjusting the data transmission frequency based on the cat's emotions. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the cat's emotion data to the generating AI and cause the generating AI to adjust the data transmission frequency.

[0098] The transmitting unit can determine the transmission priority based on the importance of the data. The transmitting unit determines the transmission priority based on, for example, the importance of the data. For example, if the body temperature data is abnormal, the transmitting unit transmits it with the highest priority. Also, if the heart rate data is abnormal, the transmitting unit can transmit it with priority. Furthermore, if the movement data is abnormal, the transmitting unit can transmit it with normal priority. In this way, by determining the transmission priority based on the importance of the data, important data is transmitted with priority. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the importance of the data to the generating AI and have the generating AI determine the transmission priority.

[0099] The transmitting unit can encrypt data to enhance security. The transmitting unit can, for example, encrypt data to enhance security. For example, the transmitting unit can encrypt and transmit body temperature data. The transmitting unit can also encrypt and transmit heart rate data. The transmitting unit can also encrypt and transmit movement data. In this way, data encryption enhances security. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input an encryption algorithm to a generating AI and have the generating AI encrypt the data.

[0100] The transmitting unit can estimate the cat's emotions and adjust the content of the transmission data based on the estimated cat emotions. The transmitting unit, for example, estimates the cat's emotions and adjusts the content of the transmission data based on the estimated cat emotions. For example, if the cat is relaxed, the transmitting unit transmits only basic data. Also, if the cat is excited, the transmitting unit can transmit detailed data. Furthermore, if the cat is stressed, the transmitting unit can transmit moderate data. In this way, by adjusting the content of the transmission data based on the cat's emotions, necessary information is appropriately transmitted. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit can input the cat's emotion data into a generating AI and have the generating AI adjust the content of the transmission data.

[0101] The transmitting unit may have an option to transmit data using Wi-Fi or a mobile network. The transmitting unit may have an option to transmit data using, for example, Wi-Fi or a mobile network. For example, the transmitting unit may transmit data using Wi-Fi. The transmitting unit may also transmit data using a mobile network. Furthermore, the transmitting unit may transmit data using both Wi-Fi and a mobile network. This improves the flexibility of data transmission by using Wi-Fi and a mobile network. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit may input connection settings for Wi-Fi or a mobile network to the generating AI and cause the generating AI to execute option settings for data transmission.

[0102] The transmitting unit can compress data to improve the transmission speed. The transmitting unit can compress data to improve the transmission speed, for example. For example, the transmitting unit can compress and transmit body temperature data. The transmitting unit can also compress and transmit heart rate data. The transmitting unit can also compress and transmit movement data. This improves the transmission speed by compressing the data. Some or all of the above-described processing in the transmitting unit can be performed using AI, for example, or can be performed without using AI. For example, the transmitting unit can input a compression algorithm to the generating AI and have the generating AI compress the data.

[0103] The display unit can estimate the cat's emotions and customize the display content based on the estimated cat emotions. The display unit, for example, estimates the cat's emotions and customizes the display content based on the estimated cat emotions. For example, the display unit provides simple display content when the cat is relaxed. Furthermore, the display unit can provide detailed display content when the cat is excited. Furthermore, the display unit can provide moderate display content when the cat is stressed. By customizing the display content based on the cat's emotions, it is possible to provide information that is easier for the owner to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the cat's emotion data into the generation AI and have the generation AI customize the display content.

[0104] The display unit may have a function to visually display past data in graphs or charts. The display unit may have a function to visually display past data in graphs or charts, for example. For example, the display unit may display a cat's body temperature data in a graph. The display unit may also display a cat's heart rate data in a chart. The display unit may also display a cat's movement data in a graph. This makes it easier to understand the data by visually displaying past data. Some or all of the above-described processing in the display unit may be performed using, or without, AI, for example. For example, the display unit may input past data into a generation AI and cause the generation AI to generate graphs or charts.

[0105] The display unit can have an alert function according to the cat's health condition. The display unit has an alert function according to the cat's health condition, for example. For example, the display unit displays an alert if the cat's body temperature is abnormal. The display unit can also display an alert if the cat's heart rate is abnormal. Furthermore, the display unit can display an alert if the cat's movements are abnormal. This allows the owner to respond quickly by displaying an alert according to the cat's health condition. Some or all of the above-mentioned processing in the display unit may be performed using AI, for example, or may be performed without using AI. For example, the display unit can input the cat's health data into the generation AI and cause the generation AI to display an alert.

[0106] The display unit can estimate the cat's emotions and adjust the display interface based on the estimated cat emotions. For example, the display unit can provide a calming interface when the cat is relaxed. Furthermore, the display unit can provide a brightly colored interface when the cat is excited. Furthermore, the display unit can provide a simple, highly visible interface when the cat is stressed. This allows the owner to more comfortably view information by adjusting the display interface based on the cat's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, AI, or without AI. For example, the display unit can input the cat's emotion data into the generation AI and have the generation AI adjust the display interface.

[0107] The display unit can customize the display method according to the notification settings of the owner's smartphone. The display unit customizes the display method according to, for example, the notification settings of the owner's smartphone. For example, the display unit notifies the owner by vibration when the owner's smartphone is in silent mode. Furthermore, the display unit can notify the owner by voice when the owner's smartphone is in normal mode. Furthermore, the display unit can suppress notifications when the owner's smartphone is in do-not-disturb mode. In this way, by customizing the display method according to the notification settings of the owner's smartphone, notifications are appropriately performed. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input notification setting data of the smartphone to a generation AI and cause the generation AI to customize the display method.

[0108] The display unit can have a function to simultaneously display data for multiple cats. The display unit can, for example, simultaneously display data for multiple cats. For example, the display unit can simultaneously display body temperature data for multiple cats. The display unit can also simultaneously display heart rate data for multiple cats. Furthermore, the display unit can simultaneously display movement data for multiple cats. By simultaneously displaying data for multiple cats, the owner can check the condition of multiple cats at once. Some or all of the above-described processing in the display unit can be performed, for example, using AI, or can be performed without using AI. For example, the display unit can input data for multiple cats into the generation AI and have the generation AI execute the simultaneous display settings.

[0109] The cat language translation unit can estimate the cat's emotions and adjust the way the translation result is expressed based on the estimated cat emotions. The cat language translation unit, for example, estimates the cat's emotions and adjusts the way the translation result is expressed based on the estimated cat emotions. For example, the cat language translation unit can display a simple translation result if the cat is relaxed. Furthermore, the cat language translation unit can display a detailed translation result if the cat is excited. Furthermore, the cat language translation unit can display a medium translation result if the cat is stressed. This allows the way the translation result is expressed to be adjusted based on the cat's emotions, thereby providing a translation result that is easier for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, AI, or without AI. For example, the cat language translation unit can input cat emotion data into the generation AI and have the generation AI adjust the way the translation results are expressed.

[0110] The cat language translation unit can improve translation accuracy by referring to past cat meow data. The cat language translation unit improves translation accuracy by referring to, for example, past cat meow data. For example, the cat language translation unit refers to past cat meow data and compares it with the current meow to perform translation. The cat language translation unit can also refer to past cat meow patterns and compare them with the current meow to perform translation. Furthermore, the cat language translation unit can refer to the frequency of past cat meows and compare them with the current meow to perform translation. In this way, by referring to past meow data, translation accuracy is improved. Some or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cat language translation unit can input past cat meow data into a generation AI and cause the generation AI to improve translation accuracy.

[0111] The cat language translation unit may have a function to detect specific cat meow patterns and display the meaning in more detail. The cat language translation unit may have a function to detect specific cat meow patterns and display the meaning in more detail. For example, the cat language translation unit may detect specific cat meow patterns and display "I'm hungry." The cat language translation unit may also detect specific cat meow patterns and display "I want to play." The cat language translation unit may also detect specific cat meow patterns and display "I'm sleepy." In this way, by detecting specific meow patterns, the meaning can be displayed in more detail. Some or all of the above-described processing in the cat language translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cat language translation unit may input cat meow data into a generation AI and cause the generation AI to detect specific meow patterns and display the meaning.

[0112] The cat language translation unit can estimate the cat's emotions and adjust the display method of the translation result based on the estimated cat emotions. The cat language translation unit, for example, estimates the cat's emotions and adjusts the display method of the translation result based on the estimated cat emotions. For example, the cat language translation unit can provide a simple display method when the cat is relaxed. Furthermore, the cat language translation unit can provide a detailed display method when the cat is excited. Furthermore, the cat language translation unit can provide a medium display method when the cat is stressed. This allows the display method of the translation result to be adjusted based on the cat's emotions, thereby providing a translation result that is easier for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the cat language translation unit may be performed using AI, for example, or without AI. For example, the cat language translation unit can input cat emotion data into the generation AI and have the generation AI adjust the display method of the translation result.

[0113] The cat language translation unit can analyze the volume and frequency of a cat's meow to improve translation accuracy. The cat language translation unit can analyze, for example, the volume and frequency of a cat's meow to improve translation accuracy. For example, the cat language translation unit can analyze the volume of a cat's meow to improve translation accuracy. The cat language translation unit can also analyze the frequency of a cat's meow to improve translation accuracy. Furthermore, the cat language translation unit can analyze the pattern of a cat's meow to improve translation accuracy. In this way, by analyzing the volume and frequency of the meow, translation accuracy is improved. Some or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the cat language translation unit can input cat's meow data into a generation AI and have the generation AI analyze the volume and frequency.

[0114] The cat language translation unit can have a function to display the translation result in multiple languages. The cat language translation unit can, for example, have a function to display the translation result in multiple languages. For example, the cat language translation unit displays the translation result in English. The cat language translation unit can also display the translation result in Japanese. The cat language translation unit can also display the translation result in Chinese. This makes it possible to accommodate owners who speak different languages ​​by displaying the translation result in multiple languages. Some or all of the above-mentioned processing in the cat language translation unit may be performed using AI, for example, or may be performed without using AI. For example, the cat language translation unit can input the translation result to a generation AI and have the generation AI display the result in multiple languages.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The system for determining a cat's physical condition and mood can further be equipped with the function of learning the cat's behavioral patterns and detecting abnormal behavior. For example, the analysis unit can detect abnormal behavior if the cat is moving in a way that is different from normal. The analysis unit can also detect abnormal behavior if the cat's food or water intake is different from normal. Furthermore, the analysis unit can detect abnormal behavior if the cat's meowing pattern is different from normal. This allows the system to learn the cat's behavioral patterns and detect abnormal behavior early, allowing the owner to take prompt action.

[0117] The system for determining a cat's physical condition and mood can further include a function for evaluating the cat's stress level and providing advice for stress reduction. For example, the analysis unit can evaluate the stress level based on the cat's heart rate and movement data. The analysis unit can also analyze the cat's meowing patterns to evaluate the stress level. Furthermore, the analysis unit can evaluate the stress level based on the cat's body temperature data. This allows for more effective cat health management by evaluating the cat's stress level and providing the owner with advice for stress reduction.

[0118] The system for determining a cat's physical condition and mood can further include a function for monitoring the cat's sleep pattern and evaluating the quality of sleep. For example, the sensor unit monitors the cat's movements and heart rate and analyzes the sleep pattern. The analysis unit can evaluate the quality of sleep based on the cat's body temperature data. Furthermore, the analysis unit can analyze the cat's meow data and evaluate the quality of sleep. In this way, monitoring the cat's sleep pattern and evaluating the quality of sleep can help owners manage their cat's health.

[0119] The system for determining a cat's physical condition and mood can further include a function for monitoring the cat's amount of exercise and providing advice for maintaining an appropriate amount of exercise. For example, the sensor unit monitors the cat's movements and measures the amount of exercise. The analysis unit can analyze the data on the cat's amount of exercise and evaluate the appropriate amount of exercise. Furthermore, the analysis unit can evaluate the amount of exercise based on the cat's weight data. This makes it possible to monitor the cat's amount of exercise and provide advice for maintaining an appropriate amount of exercise, thereby making cat health management more effective.

[0120] The system for determining a cat's physical condition and mood can further include a function for monitoring the cat's eating pattern and evaluating the quality of the diet. For example, the sensor unit monitors the amount of food the cat eats and analyzes the eating pattern. The analysis unit can also evaluate the quality of the diet based on the cat's weight data. The analysis unit can also evaluate the quality of the diet based on the cat's body temperature data. This allows owners to monitor the cat's eating pattern and evaluate the quality of the diet, which can be useful for managing the health of their cats.

[0121] The system for determining a cat's physical condition and mood can further include a function for estimating the cat's emotions and suggesting games that the cat prefers based on the estimated emotions. For example, the analysis unit can suggest quiet games if the cat is relaxed. Also, the analysis unit can suggest active games if the cat is excited. Furthermore, the analysis unit can suggest relaxing games if the cat is stressed. In this way, suggesting appropriate games based on the cat's emotions can help reduce stress and maintain the cat's health.

[0122] The system for determining a cat's physical condition and mood can further include a function for estimating the cat's emotions and suggesting the cat's preferred food based on the estimated emotions. For example, the analysis unit can suggest a regular meal if the cat is relaxed. Also, the analysis unit can suggest a special reward meal if the cat is excited. Furthermore, the analysis unit can suggest a relaxing meal if the cat is stressed. This makes cat health management more effective by suggesting an appropriate meal based on the cat's emotions.

[0123] The system for determining a cat's physical condition and mood can further include a function for estimating the cat's emotions and suggesting the cat's preferred environmental settings based on the estimated emotions. For example, the analysis unit can suggest a quiet environment if the cat is relaxed. Also, the analysis unit can suggest a play area if the cat is excited. Furthermore, the analysis unit can suggest a relaxing environment if the cat is stressed. In this way, suggesting appropriate environmental settings based on the cat's emotions can help reduce stress and maintain the cat's health.

[0124] The system for determining a cat's physical condition and mood can further include a function for estimating the cat's emotions and suggesting music that the cat likes based on the estimated emotions. For example, the analysis unit can suggest calm music if the cat is relaxed. Alternatively, the analysis unit can suggest lively music if the cat is excited. Furthermore, the analysis unit can suggest relaxing music if the cat is stressed. In this way, suggesting appropriate music based on the cat's emotions can help reduce stress and maintain the cat's health.

[0125] The system for determining a cat's physical condition and mood can further include a function for estimating the cat's emotions and suggesting a toy that the cat likes based on the estimated emotions. For example, the analysis unit can suggest a quiet toy if the cat is relaxed. Also, the analysis unit can suggest a moving toy if the cat is excited. Furthermore, the analysis unit can suggest a toy that will help the cat relax if the cat is stressed. In this way, suggesting an appropriate toy based on the cat's emotions can help reduce stress and maintain the cat's health.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The sensor unit collects the cat's body temperature, heart rate, and movements. For example, the sensor unit may have a built-in body temperature sensor, heart rate sensor, and acceleration sensor, and collect data such as the cat's body temperature, heart rate, and movements. The body temperature sensor is, for example, placed on a part worn around the cat's neck to measure the cat's body temperature. The heart rate sensor is, for example, placed on the cat's chest to measure the heart rate. The acceleration sensor is, for example, placed inside the collar to detect the cat's movements. Step 2: The analysis unit analyzes the collected data and determines the cat's physical condition and mood. For example, the analysis unit analyzes the data using a machine learning algorithm. The machine learning algorithm can use technologies such as deep learning and support vector machines. The analysis unit determines that a high body temperature may indicate a fever, and that a fast heart rate may indicate excitement. Step 3: The transmitter transmits the analysis results to a smartphone. For example, the transmitter transmits data using Bluetooth. Bluetooth versions such as Bluetooth 4.0 and Bluetooth Low Energy can be used. The transmitter transmits the analysis results to the smartphone in real time, allowing the owner to instantly check the cat's condition. Step 4: The display unit displays the analysis results sent to the smartphone. For example, the display unit has the function of displaying data in real time. The display unit also has the function of saving and analyzing past data, allowing for long-term health management of cats.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0130] 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.

[0131] For example, the sensor unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the body temperature sensor, heart rate sensor, and acceleration sensor collect the cat's body temperature, heart rate, and movement using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to determine the cat's physical condition and mood. The transmission unit transmits the analysis results to a smartphone via Bluetooth using, for example, the communication I / F 44 of the smart device 14. The display unit displays the analysis results on, for example, the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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.

[0147] For example, the sensor unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the body temperature sensor, heart rate sensor, and acceleration sensor collect the cat's body temperature, heart rate, and movements using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to determine the cat's physical condition and mood. The transmission unit transmits the analysis results to a smartphone via Bluetooth using, for example, the communication I / F 44 of the smart glasses 214. The display unit displays the analysis results on, for example, the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] 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.

[0160] 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.

[0161] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0162] 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.

[0163] For example, the sensor unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the body temperature sensor, heart rate sensor, and acceleration sensor collect the cat's body temperature, heart rate, and movements using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to determine the cat's physical condition and mood. The transmission unit transmits the analysis results to a smartphone via Bluetooth using, for example, the communication I / F 44 of the headset terminal 314. The display unit displays the analysis results on, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] 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.

[0177] 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.

[0178] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0179] 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.

[0180] For example, the sensor unit is realized by at least one of the robot 414 and the data processing device 12. For example, the body temperature sensor, heart rate sensor, and acceleration sensor collect the cat's body temperature, heart rate, and movements using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a machine learning algorithm to determine the cat's physical condition and mood. The transmission unit transmits the analysis results to a smartphone via Bluetooth using, for example, the communication I / F 44 of the robot 414. The display unit displays the analysis results on, for example, a display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Appendix 1) A sensor unit that collects the cat's body temperature, heart rate, and movement, an analysis unit that analyzes the data collected by the sensor unit and determines the physical condition of the cat; a transmission unit that transmits the analysis result obtained by the analysis unit to a smartphone; a display unit that displays the analysis result transmitted by the transmission unit. A system characterized by: (Appendix 2) The analysis unit Analyze data using machine learning algorithms 2. The system of claim 1. (Appendix 3) The transmission unit Sending data using Bluetooth 2. The system of claim 1. (Appendix 4) The display unit Ability to display data in real time 2. The system of claim 1. (Appendix 5) The display unit Equipped with a function to store and analyze past data 2. The system of claim 1. (Appendix 6) The analysis unit It has a cat language translation unit that analyzes cat meows and displays the results in text. 2. The system of claim 1. (Appendix 7) The sensor unit Estimate the cat's emotions and adjust the sensor sensitivity based on the estimated emotions. 2. The system of claim 1. (Appendix 8) The sensor unit Analyzing the cat's past physical condition data and selecting the appropriate sensor placement 2. The system of claim 1. (Appendix 9) The sensor unit Equipped with a function that automatically adjusts the frequency of data collection according to the cat's activity level 2. The system of claim 1. (Appendix 10) The sensor unit Adding sensors to measure the cat's environmental temperature and humidity will improve the accuracy of determining its physical condition. 2. The system of claim 1. (Appendix 11) The sensor unit Equipped with a function to monitor your cat's food and water intake 2. The system of claim 1. (Appendix 12) The analysis unit Inferring cat emotions and adjusting the analysis algorithm based on the inferred emotions 2. The system of claim 1. (Appendix 13) The analysis unit Improve analysis accuracy by referencing cat's past health data 2. The system of claim 1. (Appendix 14) The analysis unit It has the ability to detect specific behavioral patterns of cats and detect abnormalities early on. 2. The system of claim 1. (Appendix 15) The analysis unit Estimate the cat's emotions and adjust the display of the analysis results based on the estimated cat's emotions. 2. The system of claim 1. (Appendix 16) The analysis unit Incorporating feedback from cat owners to improve analysis accuracy 2. The system of claim 1. (Appendix 17) The analysis unit Analyzing the frequency and volume of a cat's meows to help determine its physical condition and mood 2. The system of claim 1. (Appendix 18) The transmission unit Estimate the cat's emotions and adjust the frequency of data transmission based on the estimated cat emotions. 2. The system of claim 1. (Appendix 19) The transmission unit Prioritize sending based on data importance 2. The system of claim 1. (Appendix 20) The transmission unit Encrypting data for added security 2. The system of claim 1. (Appendix 21) The transmission unit Estimate the cat's emotions and adjust the content of the data sent based on the estimated emotions. 2. The system of claim 1. (Appendix 22) The transmission unit Options to transmit data using Wi-Fi or mobile networks 2. The system of claim 1. (Appendix 23) The transmission unit Compresses data to improve transmission speed 2. The system of claim 1. (Appendix 24) The display unit Estimate the cat's emotions and customize the display content based on the estimated cat emotions 2. The system of claim 1. (Appendix 25) The display unit Equipped with a function to visually display past data in graphs and charts 2. The system of claim 1. (Appendix 26) The display unit Equipped with an alert function based on the cat's health condition 2. The system of claim 1. (Appendix 27) The display unit Estimate the cat's emotions and adjust the display interface based on the estimated emotions. 2. The system of claim 1. (Appendix 28) The display unit Customize the display method according to the notification settings of the owner's smartphone 2. The system of claim 1. (Appendix 29) The display unit It has the ability to display data for multiple cats at the same time. 2. The system of claim 1. (Appendix 30) The cat language translation unit Estimate the cat's emotions and adjust the translation results based on the estimated emotions. 2. The system of claim 1. (Appendix 31) The cat language translation unit Improve translation accuracy by referencing past cat meow data 2. The system of claim 1. (Appendix 32) The cat language translation unit It has the ability to detect specific cat meow patterns and display their meaning in more detail. 2. The system of claim 1. (Appendix 33) The cat language translation unit Estimate the cat's emotions and adjust the way the translation results are displayed based on the estimated cat's emotions 2. The system of claim 1. (Appendix 34) The cat language translation unit Analyzing the volume and frequency of cat meows to improve translation accuracy 2. The system of claim 1. (Appendix 35) The cat language translation unit Has the ability to display translation results in multiple languages 2. The system of claim 1. [Explanation of symbols]

[0200] 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 sensor unit that collects the cat's body temperature, heart rate, and movement, an analysis unit that analyzes the data collected by the sensor unit and determines the cat's physical condition and stress level; a transmission unit that transmits the analysis result obtained by the analysis unit to a smartphone; a display unit that displays the analysis result transmitted by the transmission unit, The analysis unit generates advice for reducing the stress of the cat based on the determined stress level of the cat, The display unit displays the advice. A system characterized by:

2. The analysis unit Analyze data using machine learning algorithms 2. The system of claim 1.

3. The display unit Ability to display data in real time 2. The system of claim 1.

4. The display unit Equipped with the ability to store and analyze past data 2. The system of claim 1.

5. The analysis unit It has a cat language translation unit that analyzes cat meows and displays the results in text.

2. The system of claim 1.

6. The sensor unit Estimate the cat's emotions and adjust the sensor sensitivity based on the estimated emotions.

2. The system of claim 1.

7. The sensor unit The cat's past physical condition data is analyzed, and the appropriate sensor placement is selected by optimizing the positions of the body temperature sensor, heart rate sensor, and acceleration sensor based on the physical condition data.

2. The system of claim 1.

Images