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 a description and related instruction sentence regarding 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] JP 2022-180282 A 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 body temperature, heart rate, and movements of the cat. The analysis unit analyzes the data collected by the sensor unit and determines the physical condition of the cat. 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. Effect of the Invention

[0007] The system according to the embodiment can grasp the physical condition and mood of a cat in real time. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system for determining the physical condition and mood of a cat according to the embodiment of the present invention is a collar that uses AI to determine the physical condition and mood of a cat and transmits the information to a smartphone. The system includes a sensor unit that collects the body temperature, heart rate, and movement of the cat, an analysis unit that analyzes the data collected by the sensor unit and determines the physical condition and mood of the cat, 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 has a built-in 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 analysis unit analyzes the collected data using a machine learning algorithm to determine the physical condition and mood of the cat. For example, a high body temperature may indicate a fever, and a fast heart rate may indicate excitement. 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 physical condition and mood of the cat. In addition, the cat language translation option can analyze a cat's meows and display their meaning in text. For example, messages such as "I'm hungry" or "I want to play" can be displayed. This system makes it easier for owners to understand their cat's health and mood, and provide appropriate care. As a result, the system that determines a cat's health and mood can determine the cat's health and mood in real time and send it to a smartphone.

[0029] The system for determining the physical condition and mood of a cat according to the embodiment includes a sensor unit, an analysis unit, a transmission unit, and a display unit. The sensor unit collects the body temperature, heart rate, and movement of the cat. For example, the sensor unit has a body temperature sensor, a heart rate sensor, and an acceleration sensor built in, and collects data such as the body temperature, heart rate, and movement of the cat. The body temperature sensor is, for example, disposed on a part attached to the cat's neck, and measures the body temperature of the cat. The heart rate sensor is, for example, disposed on the cat's chest, and measures the heart rate. The acceleration sensor is, for example, disposed inside the collar to detect the movement of the cat. The analysis unit analyzes the collected data and determines the physical condition and mood of the cat. For example, the analysis unit analyzes the data using a machine learning algorithm. The machine learning algorithm can use, for example, technologies such as deep learning and support vector machines. The analysis unit determines that if the body temperature is high, there is a possibility of fever, and if the heart rate is fast, there is a possibility of excitement. The transmission unit transmits the analysis result to a smartphone. For example, the transmission unit transmits the data using Bluetooth. For example, versions of Bluetooth such as Bluetooth 4.0 and Bluetooth Low Energy can be used. The transmission unit transmits the analysis results to the smartphone in real time, allowing the owner to immediately check the condition of the cat. The display unit displays the analysis results transmitted to the smartphone. For example, the display unit has a function of displaying data in real time. The display unit also has a function of storing and analyzing past data, enabling long-term health management of the cat. As a result, the system for determining the physical condition and mood of a cat according to the embodiment can determine the physical condition and mood of the cat in real time and transmit it to the smartphone.

[0030] The sensor unit collects the body temperature, heart rate, and movement of the cat. For example, the sensor unit has a built-in body temperature sensor, a heart rate sensor, and an acceleration sensor, and collects data such as the body temperature, heart rate, and movement of the cat. The body temperature sensor is, for example, disposed on a part that is attached to the cat's neck, and measures the body temperature of the cat. The body temperature sensor can measure the body temperature non-contact using infrared technology, and accurate body temperature data can be obtained without causing stress to the cat. The heart rate sensor is, for example, disposed on the cat's chest, and measures the heart rate. The heart rate sensor detects changes in blood flow through the skin using photoplethysmography (PPG) to measure the heart rate. The acceleration sensor is, for example, disposed inside the collar to detect the movement of the cat. The acceleration sensor can record the direction and strength of the movement of the cat in detail using a three-axis accelerometer. As a result, the sensor unit can collect data on the body temperature, heart rate, and movement of the cat with high accuracy, and provide basic data for accurately grasping the physical condition and mood of the cat. In addition, the sensor unit is equipped with a memory that temporarily stores collected data, preventing data loss. The sensor unit is designed to be flexible so that it can follow the movements of the cat, 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 and determines the physical condition and mood of the cat. 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. Deep learning can automatically extract features of data using a multi-layer neural network and perform advanced pattern recognition. Support vector machines have excellent performance in data classification and regression analysis, and can determine the physical condition and mood of the cat with high accuracy. The analysis unit determines that a high body temperature indicates a possibility of fever, and that a fast heart rate indicates a possibility of excitement. Furthermore, the analysis unit can analyze the movement patterns of the cat and detect abnormal movements and changes in behavior. For example, if the cat moves less than usual, it can be determined that this is a sign of poor health or stress. The analysis unit can monitor changes in the cat's physical condition and mood over the long term by comparing with past data, and can issue an early warning if an abnormality occurs. This allows the analysis unit to quickly and accurately analyze the collected data and determine the physical condition and mood of the cat in real time. Furthermore, the analysis unit also 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 they need to take appropriate action.

[0032] The transmitting unit transmits the analysis results to the smartphone. For example, the transmitting unit transmits data using Bluetooth. Bluetooth can use versions such as Bluetooth 4.0 and Bluetooth Low Energy. Bluetooth Low Energy can transmit data with low power consumption, and can extend the battery life of the cat collar. The transmitting unit transmits the analysis results to the smartphone in real time, allowing the owner to immediately check the condition of the cat. The transmitting unit has an error check function to ensure stable communication so that data transmission is not interrupted. In addition, the transmitting unit has a data encryption function to ensure the security of the data to be transmitted. This allows the transmitting unit to safely and securely transmit the analysis results to the smartphone, allowing the owner to grasp the physical condition and feelings of the cat in real time. The transmitting unit works in conjunction with a smartphone app and can receive data even when the app is running in the background. This allows the owner to constantly monitor the condition of the cat and respond immediately if an abnormality occurs. The transmitting unit also has a function to adjust the frequency of data transmission, and can set the optimal transmission frequency according to the cat's activity status and the remaining battery level. This enables the transmitting section 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 a function to display data in real time. The display unit also has a function to store and analyze past data, enabling long-term health management of cats. The display unit visualizes the analysis results in graphs and charts, allowing owners to intuitively understand the condition of their cats. For example, it can display fluctuations in body temperature and heart rate in a line graph and the cat's activity level in a bar graph. The display unit also has a function to display an alert and alert the owner when an abnormality is detected. For example, if the body temperature exceeds a certain range or the heart rate changes rapidly, it can display an alert and encourage the owner to take early action. The display unit has a data storage function, and by referring to past data, it is possible to monitor changes in the cat's physical condition and mood over the long term. This allows the owner to comprehensively grasp the cat's health condition and take appropriate action. Furthermore, the display unit has a data sharing function, allowing data to be shared with veterinarians and other owners. This allows the owner to manage the cat's health while referring to the opinions of experts. The display unit has an intuitive and easy-to-use user interface that allows owners to operate it easily, enabling them to understand their cat's physical condition and mood in real time and provide 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 determine the physical condition and mood of the cat with high accuracy. The support vector machine is used for data classification and regression analysis and can determine the physical condition and mood of the cat with high accuracy. For example, when deep learning is used, the analysis unit uses a neural network model that inputs data such as the cat's body temperature, heart rate, and movement, and outputs the physical condition and mood of the cat. When a support vector machine is used, the analysis unit uses a support vector machine model that inputs data such as the cat's body temperature, heart rate, and movement, and outputs the physical condition and mood of the cat. As a result, the accuracy of data analysis is improved by using the machine learning algorithm. A part 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 transmitting unit can transmit data using Bluetooth. The transmitting unit transmits data using versions such as Bluetooth 4.0 and Bluetooth Low Energy. Bluetooth 4.0 can transmit data with low power consumption, which can extend the battery life of the cat collar. Bluetooth Low Energy can transmit data with even lower power consumption, which enables long-term data transmission. For example, the transmitting unit transmits data such as the cat's body temperature, heart rate, and movement to a smartphone using Bluetooth 4.0. The transmitting unit can also transmit analysis results to a smartphone in real time using Bluetooth Low Energy. As a result, the use of Bluetooth stabilizes data transmission. A part or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI.

[0036] The display unit can have a function of displaying data in real time. The display unit displays data in real time on, for example, a smartphone screen. For example, the display unit displays data such as the cat's body temperature, heart rate, and movement in real time, allowing the owner to immediately check the cat's condition. The display unit realizes real-time data display by setting a high data update frequency and minimizing delay time. For example, the display unit updates data every second to immediately reflect changes in the cat's physical condition and mood. In addition, the display unit devise a visual display method for 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 the heart rate in a chart. As a result, the owner can immediately check the cat's condition by displaying the data in real time. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The display unit can have a function of storing and analyzing past data. The display unit has, for example, a function of storing and analyzing past data. For example, the display unit stores data such as the cat's body temperature, heart rate, and movement for a certain period of time, and analyzes the past data, thereby enabling long-term health management of the cat. The display unit sets a storage period and automatically stores data at certain intervals. For example, the display unit stores data every week and holds data for the past year. In addition, the display unit can analyze the stored data and grasp changes in the cat's physical condition and mood. For example, the display unit displays the past data in graphs and charts, visually showing the trends in the cat's physical condition and mood. As a result, long-term health management of the cat is possible by storing and analyzing the past data. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI.

[0038] The analysis unit may include a cat language translation unit that analyzes the cat's meow and displays the analysis result in text. The analysis unit may include, for example, a cat language translation unit that analyzes the cat's meow and displays the meaning in text. The cat language translation unit may analyze the cat's meow using, for example, a voice analysis technique and display the meaning in text. For example, the cat language translation unit records the cat's meow and extracts the features of the meow using a voice analysis algorithm. The voice analysis algorithm may use, for example, a frequency analysis or a volume analysis technique. The cat language translation unit determines the meaning of the cat's meow based on the extracted features and displays it in text. For example, the cat language translation unit may display messages such as "I'm hungry" or "I want to play". This makes it easier for the owner to understand the cat's requests by analyzing the cat's meow and displaying the meaning in text. Some or all of the above-mentioned processes 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 emotion and adjust the sensitivity of the sensor based on the estimated emotion of the cat. The sensor unit, for example, estimates the cat's emotion and adjusts the sensitivity of the sensor based on the estimated emotion of the cat. For example, when the cat is relaxed, the sensor unit can set the sensor sensitivity low and reduce the frequency of data collection. Also, when the cat is excited, the sensor unit can set the sensor sensitivity high and increase the frequency of data collection. Furthermore, when the cat is stressed, the sensor unit can set the sensor sensitivity to a medium level and balance the data collection. Thereby, by adjusting the sensor sensitivity based on the cat's emotion, the accuracy of data collection is improved. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sensor unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sensor unit can input a cat's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the sensor sensitivity.

[0040] The sensor unit can analyze the cat's past physical condition data and select an appropriate sensor placement. The sensor unit can, for example, analyze the cat's past physical condition data and select an 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 the sensor placement based on the past physical condition data. Part or all of the above-mentioned processing in the sensor unit can be performed, for example, using AI or without AI. For example, the sensor unit can input the cat's past physical condition data to the generation AI and cause the generation AI to select the optimal sensor placement.

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

[0042] The sensor unit can add a sensor that measures the cat's environmental temperature and humidity to improve the accuracy of the physical condition determination. The sensor unit can add a sensor that measures the cat's environmental temperature and humidity to improve the accuracy of the 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. As a result, the accuracy of the physical condition determination is improved by measuring the environmental temperature and humidity. Part or all of the above-mentioned processing in the sensor unit may be performed, for example, using AI, or may be performed without using AI. For example, the sensor unit can input environmental temperature and humidity data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the physical condition determination.

[0043] The sensor unit can have a function of monitoring the cat's diet and water intake. The sensor unit can have a function of monitoring 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. The sensor unit can also 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 the cat's health to be managed more accurately by monitoring the diet and water intake. Some or all of the above-mentioned processing in the sensor unit can be performed, for example, using AI, or can be performed without using AI. For example, the sensor unit can input the cat's diet and water intake data to the generation AI and cause the generation AI to perform physical condition determination.

[0044] The analysis unit can estimate the cat's emotions and adjust the analysis algorithm based on the estimated cat's emotions. The analysis unit, for example, estimates the cat's emotions and adjusts the analysis algorithm based on the estimated cat's emotions. For example, the analysis unit sets the analysis algorithm gently when the cat is relaxed. Also, the analysis unit can set the analysis algorithm quickly when the cat is excited. Furthermore, the analysis unit can set the analysis algorithm to a medium level when the cat is stressed. This improves the analysis accuracy by adjusting the analysis algorithm based on the cat's emotions. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the cat's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0045] The analysis unit can improve the analysis accuracy by referring to the cat's past health data. The analysis unit can improve the analysis accuracy 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 the current body temperature data for analysis. The analysis unit can also refer to the cat's past heart rate data and compare it with the current heart rate data for analysis. Furthermore, the analysis unit can refer to the cat's past movement data and compare it with the current movement data for analysis. As a result, the analysis accuracy is improved by referring to the past health data. A part 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 the cat's past health data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0046] The analysis unit can have a function of detecting a specific behavioral pattern of the cat and detecting abnormalities at an early stage. The analysis unit has a function of detecting a specific behavioral pattern of the cat and detecting abnormalities at an early stage, for example. For example, the analysis unit detects abnormalities when the cat moves differently than normal. The analysis unit can also detect abnormalities when the cat's heart rate is different from normal. Furthermore, the analysis unit can detect abnormalities when the cat's body temperature is different from normal. By detecting specific behavioral patterns, abnormalities can be detected at an early stage. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the cat's behavioral data to the generation AI and cause the generation AI to perform early detection of abnormalities.

[0047] The analysis unit can estimate the cat's emotion and adjust the display method of the analysis result based on the estimated cat's emotion. The analysis unit, for example, estimates the cat's emotion and adjusts the display method of the analysis result based on the estimated cat's emotion. For example, the analysis unit displays the analysis result simply when the cat is relaxed. Also, the analysis unit can display the analysis result in detail when the cat is excited. Furthermore, the analysis unit can display the analysis result moderately when the cat is stressed. This makes it easier to understand the analysis result by adjusting the display method based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the cat's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0048] The analysis unit can improve the analysis accuracy by incorporating feedback from the cat's owner. The analysis unit can improve the analysis accuracy by incorporating, for example, feedback from the cat's owner. For example, the analysis unit reflects the cat's physical condition information provided by the owner in the analysis. The analysis unit can also reflect the cat's behavior information provided by the owner in the analysis. Furthermore, the analysis unit can reflect the cat's diet information provided by the owner in the analysis. As a result, the analysis accuracy is improved by incorporating the owner's feedback. A part 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 owner's feedback data into the generation AI and cause the generation AI to improve the analysis accuracy.

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

[0050] The transmission unit can estimate the cat's emotion and adjust the frequency of data transmission based on the estimated cat's emotion. The transmission unit, for example, estimates the cat's emotion and adjusts the frequency of data transmission based on the estimated cat's emotion. For example, the transmission unit sets the data transmission frequency low when the cat is relaxed. Also, the transmission unit can set the data transmission frequency high when the cat is excited. Furthermore, the transmission unit can set the data transmission frequency to a medium level when the cat is stressed. This allows efficient data transmission by adjusting the frequency of data transmission based on the cat's emotion. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the transmission unit can input the cat's emotion data to the generation AI and cause the generation 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, when the body temperature data is abnormal, the transmitting unit transmits the data with the highest priority. Also, when the heart rate data is abnormal, the transmitting unit can transmit the data with priority. Furthermore, when the movement data is abnormal, the transmitting unit can transmit the data with normal priority. As a result, important data is transmitted with priority by determining the transmission priority based on the importance of the data. A part or all of the above-mentioned processing in the transmitting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmitting unit can input the importance of the data to the generating AI and cause the generating AI to determine the transmission priority.

[0052] The transmitting unit can encrypt data and strengthen security. The transmitting unit, for example, encrypts data and strengthens security. For example, the transmitting unit encrypts and transmits body temperature data. The transmitting unit can also encrypt and transmit heart rate data. Furthermore, the transmitting unit can also encrypt and transmit movement data. Thereby, security is strengthened by encrypting data. A part or all of the above-mentioned processing in the transmitting unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmitting unit can input an encryption algorithm to the generating AI and cause the generating AI to encrypt the data.

[0053] The transmission unit can estimate the cat's emotion and adjust the content of the transmission data based on the estimated cat's emotion. The transmission unit, for example, estimates the cat's emotion and adjusts the content of the transmission data based on the estimated cat's emotion. For example, the transmission unit transmits only basic data when the cat is relaxed. Also, the transmission unit can transmit detailed data when the cat is excited. Furthermore, the transmission unit can transmit moderate data when the cat is stressed. In this way, the necessary information is appropriately transmitted by adjusting the content of the transmission data based on the cat's emotion. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the transmission unit can input the cat's emotion data to the generation AI and cause the generation AI to adjust the content of the transmission data.

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

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

[0056] The display unit can estimate the cat's emotion and customize the display content based on the estimated cat's emotion. The display unit, for example, estimates the cat's emotion and customizes the display content based on the estimated cat's emotion. 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. This allows the owner to be provided with information that is easier to understand by customizing the display content based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to customize the display content.

[0057] The display unit can have a function of visually displaying past data in a graph or chart. The display unit can have a function of visually displaying past data in a graph or chart. For example, the display unit can display the cat's body temperature data in a graph. The display unit can also display the cat's heart rate data in a chart. Furthermore, the display unit can display the cat's movement data in a graph. This makes it easier to understand the data by visually displaying the past data. A part or all of the above-mentioned 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 past data to a generation AI and cause the generation AI to generate a graph or chart.

[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 when the cat's body temperature is abnormal. Also, the display unit can display an alert when the cat's heart rate is abnormal. Furthermore, the display unit can display an alert when the cat's movements are abnormal. This allows the owner to respond quickly by displaying an alert according to the cat's health condition. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the cat's health data to the generation AI and cause the generation AI to display an alert.

[0059] The display unit can estimate the cat's emotion and adjust the display interface based on the estimated cat's emotion. The display unit, for example, estimates the cat's emotion and adjusts the display interface based on the estimated cat's emotion. For example, when the cat is relaxed, the display unit provides an interface with a calm color. Also, when the cat is excited, the display unit can provide an interface with a bright color. Furthermore, when the cat is stressed, the display unit can provide a simple and highly visible interface. This allows the owner to check information more comfortably by adjusting the display interface based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to 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, when the owner's smartphone is in silent mode, the display unit notifies by vibration. Also, when the owner's smartphone is in normal mode, the display unit can notify by voice. Furthermore, when the owner's smartphone is in do not disturb mode, the display unit can suppress the notification. As a result, the display method is customized according to the notification settings of the owner's smartphone, so that the notification is appropriately performed. A part or all of the above-mentioned 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 the notification setting data of the smartphone to the generation AI and cause the generation AI to execute customization of the display method.

[0061] The display unit can have a function of simultaneously displaying data of multiple cats. The display unit has a function of simultaneously displaying data of multiple cats, for example. For example, the display unit can simultaneously display body temperature data of multiple cats. Also, the display unit can simultaneously display heart rate data of multiple cats. Furthermore, the display unit can simultaneously display movement data of multiple cats. This allows the owner to check the condition of multiple cats at once by simultaneously displaying data of multiple cats. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data of multiple cats to the generation AI and cause the generation AI to execute the setting for simultaneous display.

[0062] The cat language translation unit can estimate the cat's emotion and adjust the expression method of the translation result based on the estimated cat's emotion. The cat language translation unit, for example, estimates the cat's emotion and adjusts the expression method of the translation result based on the estimated cat's emotion. For example, the cat language translation unit displays a simple translation result when the cat is relaxed. In addition, the cat language translation unit can display a detailed translation result when the cat is excited. Furthermore, the cat language translation unit can display a medium translation result when the cat is stressed. This allows the owner to provide a translation result that is easier to understand by adjusting the expression method of the translation result based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, an AI, or may be performed without using an 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 the translation accuracy by referring to the cat's past meow data. The cat language translation unit can improve the translation accuracy by referring to the cat's past meow data, for example. For example, the cat language translation unit can refer to the cat's past meow data and translate it by comparing it with the current meow. The cat language translation unit can also refer to the cat's past meow pattern and translate it by comparing it with the current meow. Furthermore, the cat language translation unit can refer to the frequency of the cat's past meow and translate it by comparing it with the current meow. As a result, the translation accuracy is improved by referring to the past meow data. A part or all of the above-mentioned processing in the cat language translation unit can be performed, for example, using AI or can be performed without using AI. For example, the cat language translation unit can input the cat's past meow data to the generation AI and cause the generation AI to improve the translation accuracy.

[0064] The cat language translation unit can have a function of detecting a specific cat meow pattern and displaying the meaning in more detail. The cat language translation unit can, for example, detect a specific cat meow pattern and display the meaning in more detail. For example, the cat language translation unit can detect a specific cat meow pattern and display "I'm hungry". The cat language translation unit can also detect a specific cat meow pattern and display "I want to play". The cat language translation unit can also detect a specific cat meow pattern and display "I'm sleepy". By detecting a specific meow pattern, the meaning can be displayed in more detail. A part or all of the above-mentioned processing in the cat language translation unit can be performed, for example, using AI or can be performed without using AI. For example, the cat language translation unit can input cat meow data to the generation AI and cause the generation AI to detect a specific meow pattern and display the meaning.

[0065] The cat language translation unit can estimate the cat's emotion and adjust the display method of the translation result based on the estimated cat's emotion. The cat language translation unit, for example, estimates the cat's emotion and adjusts the display method of the translation result based on the estimated cat's emotion. For example, the cat language translation unit provides 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 owner to provide a translation result that is easier to understand by adjusting the display method of the translation result based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cat language translation unit can input cat emotion data to the generation AI and cause the generation AI to adjust the display method of the translation result.

[0066] The cat language translation unit can analyze the volume and frequency of the cat's meow to improve the translation accuracy. The cat language translation unit can analyze, for example, the volume and frequency of the cat's meow to improve the translation accuracy. For example, the cat language translation unit can analyze the volume of the cat's meow to improve the translation accuracy. In addition, the cat language translation unit can analyze the frequency of the cat's meow to improve the translation accuracy. Furthermore, the cat language translation unit can analyze the pattern of the cat's meow to improve the translation accuracy. As a result, the translation accuracy is improved by analyzing the volume and frequency of the meow. A part or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cat language translation unit can input cat's meow data to the generation AI and cause the generation AI to perform the analysis of the volume and frequency.

[0067] The cat language translation unit can have a function of displaying the translation result in multiple languages. The cat language translation unit has a function of displaying the translation result in multiple languages, for example. For example, the cat language translation unit displays the translation result in English. Also, the cat language translation unit can display the translation result in Japanese. Furthermore, the cat language translation unit can 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, for example, using AI, or may be performed without using AI. For example, the cat language translation unit can input the translation result to a generation AI and cause the generation AI to display 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 include a function for learning the cat's behavioral patterns and detecting abnormal behavior. For example, the analysis unit detects abnormal behavior when the cat moves differently than usual. The analysis unit can also detect abnormal behavior when the cat's food intake or water intake is different from normal. Furthermore, the analysis unit can detect abnormal behavior when the cat's meowing pattern is different from normal. This allows the owner to quickly respond by learning the cat's behavioral patterns and discovering abnormal behavior early.

[0070] The system for determining the physical condition and mood of a cat can further include a function for monitoring the cat's sleep pattern and evaluating the quality of the 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 the 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 the sleep. In this way, by monitoring the cat's sleep pattern and evaluating the quality of the sleep, owners can help manage their cat's health.

[0071] The system for determining the physical condition and mood of a cat can further include a function for monitoring the amount of movement of the cat and providing advice for maintaining an appropriate amount of movement. For example, the sensor unit monitors the movement of the cat and measures the amount of movement. The analysis unit can analyze the data on the amount of movement of the cat and evaluate the appropriate amount of movement. Furthermore, the analysis unit can evaluate the amount of movement based on the weight data of the cat. This makes it possible to more effectively manage the health of a cat by monitoring the amount of movement of the cat and providing advice for maintaining an appropriate amount of movement.

[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 food. For example, the sensor unit monitors the amount of food eaten by the cat and analyzes the eating pattern. The analysis unit can also evaluate the quality of the food based on the cat's weight data. The analysis unit can also evaluate the quality of the food based on the cat's body temperature data. In this way, monitoring the cat's eating pattern and evaluating the quality of the food can be useful for owners in managing their cat's health.

[0073] The process flow of the first embodiment will be briefly described below.

[0074] Step 1: The sensor unit collects the cat's body temperature, heart rate, and movement. For example, the sensor unit has a built-in body temperature sensor, heart rate sensor, and acceleration sensor, and collects data such as the cat's body temperature, heart rate, and movement. The body temperature sensor is, for example, placed on a part that is worn around the cat's neck, and measures the cat's body temperature. The heart rate sensor is, for example, placed on the cat's chest, and measures the heart rate. The acceleration sensor is, for example, placed inside the collar to detect the cat's movement. 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 techniques such as deep learning and support vector machines. The analysis unit determines that a high body temperature indicates a possibility of fever, and that a fast heart rate indicates a possibility of excitement. Step 3: The transmitter transmits the analysis results to a smartphone. For example, the transmitter transmits data using Bluetooth. Bluetooth can use versions such as Bluetooth 4.0 and Bluetooth Low Energy. The transmitter transmits the analysis results to the smartphone in real time, allowing the owner to instantly check the condition of the cat. Step 4: The display unit displays the analysis results sent to the smartphone. For example, the display unit has a function to display data in real time. The display unit also has a function to store and analyze past data, enabling long-term health management of cats.

[0075] (Example 2) The system for determining the physical condition and mood of a cat according to an embodiment of the present invention is a collar that uses AI to determine the physical condition and mood of a cat and transmits the information to a smartphone. The system includes a sensor unit that collects the body temperature, heart rate, and movement of the cat, an analysis unit that analyzes the data collected by the sensor unit and determines the physical condition and mood of the cat, 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 has a built-in 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 analysis unit analyzes the collected data using a machine learning algorithm to determine the physical condition and mood of the cat. For example, a high body temperature may indicate a fever, and a fast heart rate may indicate excitement. 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 physical condition and mood of the cat. In addition, the cat language translation option can analyze a cat's meows and display their meaning in text. For example, messages such as "I'm hungry" or "I want to play" can be displayed. This system makes it easier for owners to understand their cat's health and mood, and provide appropriate care. As a result, the system that determines a cat's health and mood can determine the cat's health and mood in real time and send it to a smartphone.

[0076] The system for determining the physical condition and mood of a cat according to the embodiment includes a sensor unit, an analysis unit, a transmission unit, and a display unit. The sensor unit collects the body temperature, heart rate, and movement of the cat. For example, the sensor unit has a body temperature sensor, a heart rate sensor, and an acceleration sensor built in, and collects data such as the body temperature, heart rate, and movement of the cat. The body temperature sensor is, for example, disposed on a part attached to the cat's neck, and measures the body temperature of the cat. The heart rate sensor is, for example, disposed on the cat's chest, and measures the heart rate. The acceleration sensor is, for example, disposed inside the collar to detect the movement of the cat. The analysis unit analyzes the collected data and determines the physical condition and mood of the cat. For example, the analysis unit analyzes the data using a machine learning algorithm. The machine learning algorithm can use, for example, technologies such as deep learning and support vector machines. The analysis unit determines that if the body temperature is high, there is a possibility of fever, and if the heart rate is fast, there is a possibility of excitement. The transmission unit transmits the analysis result to a smartphone. For example, the transmission unit transmits the data using Bluetooth. For example, versions of Bluetooth such as Bluetooth 4.0 and Bluetooth Low Energy can be used. The transmission unit transmits the analysis results to the smartphone in real time, allowing the owner to immediately check the condition of the cat. The display unit displays the analysis results transmitted to the smartphone. For example, the display unit has a function of displaying data in real time. The display unit also has a function of storing and analyzing past data, enabling long-term health management of the cat. As a result, the system for determining the physical condition and mood of a cat according to the embodiment can determine the physical condition and mood of the cat in real time and transmit it to the smartphone.

[0077] The sensor unit collects the body temperature, heart rate, and movement of the cat. For example, the sensor unit has a built-in body temperature sensor, a heart rate sensor, and an acceleration sensor, and collects data such as the body temperature, heart rate, and movement of the cat. The body temperature sensor is, for example, disposed on a part that is attached to the cat's neck, and measures the body temperature of the cat. The body temperature sensor can measure the body temperature non-contact using infrared technology, and accurate body temperature data can be obtained without causing stress to the cat. The heart rate sensor is, for example, disposed on the cat's chest, and measures the heart rate. The heart rate sensor detects changes in blood flow through the skin using photoplethysmography (PPG) to measure the heart rate. The acceleration sensor is, for example, disposed inside the collar to detect the movement of the cat. The acceleration sensor can record the direction and strength of the movement of the cat in detail using a three-axis accelerometer. As a result, the sensor unit can collect data on the body temperature, heart rate, and movement of the cat with high accuracy, and provide basic data for accurately grasping the physical condition and mood of the cat. In addition, the sensor unit is equipped with a memory that temporarily stores collected data, preventing data loss. The sensor unit is designed to be flexible so that it can follow the movements of the cat, 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 and determines the physical condition and mood of the cat. 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. Deep learning can automatically extract features of data using a multi-layer neural network and perform advanced pattern recognition. Support vector machines have excellent performance in data classification and regression analysis, and can determine the physical condition and mood of the cat with high accuracy. The analysis unit determines that a high body temperature indicates a possibility of fever, and that a fast heart rate indicates a possibility of excitement. Furthermore, the analysis unit can analyze the movement patterns of the cat and detect abnormal movements and changes in behavior. For example, if the cat moves less than usual, it can be determined that this is a sign of poor health or stress. The analysis unit can monitor changes in the cat's physical condition and mood over the long term by comparing with past data, and can issue an early warning if an abnormality occurs. This allows the analysis unit to quickly and accurately analyze the collected data and determine the physical condition and mood of the cat in real time. Furthermore, the analysis unit also 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 they need to take appropriate action.

[0079] The transmitting unit transmits the analysis results to the smartphone. For example, the transmitting unit transmits data using Bluetooth. Bluetooth can use versions such as Bluetooth 4.0 and Bluetooth Low Energy. Bluetooth Low Energy can transmit data with low power consumption, and can extend the battery life of the cat collar. The transmitting unit transmits the analysis results to the smartphone in real time, allowing the owner to immediately check the condition of the cat. The transmitting unit has an error check function to ensure stable communication so that data transmission is not interrupted. In addition, the transmitting unit has a data encryption function to ensure the security of the data to be transmitted. This allows the transmitting unit to safely and securely transmit the analysis results to the smartphone, allowing the owner to grasp the physical condition and feelings of the cat in real time. The transmitting unit works in conjunction with a smartphone app and can receive data even when the app is running in the background. This allows the owner to constantly monitor the condition of the cat and respond immediately if an abnormality occurs. The transmitting unit also has a function to adjust the frequency of data transmission, and can set the optimal transmission frequency according to the cat's activity status and the remaining battery level. This enables the transmitting section 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 a function to display data in real time. The display unit also has a function to store and analyze past data, enabling long-term health management of cats. The display unit visualizes the analysis results in graphs and charts, allowing owners to intuitively understand the condition of their cats. For example, it can display fluctuations in body temperature and heart rate in a line graph and the cat's activity level in a bar graph. The display unit also has a function to display an alert and alert the owner when an abnormality is detected. For example, if the body temperature exceeds a certain range or the heart rate changes rapidly, it can display an alert and encourage the owner to take early action. The display unit has a data storage function, and by referring to past data, it is possible to monitor changes in the cat's physical condition and mood over the long term. This allows the owner to comprehensively grasp the cat's health condition and take appropriate action. Furthermore, the display unit has a data sharing function, allowing data to be shared with veterinarians and other owners. This allows the owner to manage the cat's health while referring to the opinions of experts. The display unit has an intuitive and easy-to-use user interface that allows owners to operate it easily, enabling them to understand their cat's physical condition and mood in real time and provide 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 determine the physical condition and mood of the cat with high accuracy. The support vector machine is used for data classification and regression analysis and can determine the physical condition and mood of the cat with high accuracy. For example, when deep learning is used, the analysis unit uses a neural network model that inputs data such as the cat's body temperature, heart rate, and movement, and outputs the physical condition and mood of the cat. When a support vector machine is used, the analysis unit uses a support vector machine model that inputs data such as the cat's body temperature, heart rate, and movement, and outputs the physical condition and mood of the cat. As a result, the accuracy of data analysis is improved by using the machine learning algorithm. A part 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 transmitting unit can transmit data using Bluetooth. The transmitting unit transmits data using versions such as Bluetooth 4.0 and Bluetooth Low Energy. Bluetooth 4.0 can transmit data with low power consumption, which can extend the battery life of the cat collar. Bluetooth Low Energy can transmit data with even lower power consumption, which enables long-term data transmission. For example, the transmitting unit transmits data such as the cat's body temperature, heart rate, and movement to a smartphone using Bluetooth 4.0. The transmitting unit can also transmit analysis results to a smartphone in real time using Bluetooth Low Energy. As a result, the use of Bluetooth stabilizes data transmission. A part or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI.

[0083] The display unit can have a function of displaying data in real time. The display unit displays data in real time on, for example, a smartphone screen. For example, the display unit displays data such as the cat's body temperature, heart rate, and movement in real time, allowing the owner to immediately check the cat's condition. The display unit realizes real-time data display by setting a high data update frequency and minimizing delay time. For example, the display unit updates data every second to immediately reflect changes in the cat's physical condition and mood. In addition, the display unit devise a visual display method for 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 the heart rate in a chart. As a result, the owner can immediately check the cat's condition by displaying the data in real time. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI.

[0084] The display unit can have a function of storing and analyzing past data. The display unit has, for example, a function of storing and analyzing past data. For example, the display unit stores data such as the cat's body temperature, heart rate, and movement for a certain period of time, and analyzes the past data, thereby enabling long-term health management of the cat. The display unit sets a storage period and automatically stores data at certain intervals. For example, the display unit stores data every week and holds data for the past year. In addition, the display unit can analyze the stored data and grasp changes in the cat's physical condition and mood. For example, the display unit displays the past data in graphs and charts, visually showing the trends in the cat's physical condition and mood. As a result, long-term health management of the cat is possible by storing and analyzing the past data. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI.

[0085] The analysis unit may include a cat language translation unit that analyzes the cat's meow and displays the analysis result in text. The analysis unit may include, for example, a cat language translation unit that analyzes the cat's meow and displays the meaning in text. The cat language translation unit may analyze the cat's meow using, for example, a voice analysis technique and display the meaning in text. For example, the cat language translation unit records the cat's meow and extracts the features of the meow using a voice analysis algorithm. The voice analysis algorithm may use, for example, a frequency analysis or a volume analysis technique. The cat language translation unit determines the meaning of the cat's meow based on the extracted features and displays it in text. For example, the cat language translation unit may display messages such as "I'm hungry" or "I want to play". This makes it easier for the owner to understand the cat's requests by analyzing the cat's meow and displaying the meaning in text. Some or all of the above-mentioned processes 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 emotion and adjust the sensitivity of the sensor based on the estimated emotion of the cat. The sensor unit, for example, estimates the cat's emotion and adjusts the sensitivity of the sensor based on the estimated emotion of the cat. For example, when the cat is relaxed, the sensor unit can set the sensor sensitivity low and reduce the frequency of data collection. Also, when the cat is excited, the sensor unit can set the sensor sensitivity high and increase the frequency of data collection. Furthermore, when the cat is stressed, the sensor unit can set the sensor sensitivity to a medium level and balance the data collection. Thereby, by adjusting the sensor sensitivity based on the cat's emotion, the accuracy of data collection is improved. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sensor unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sensor unit can input a cat's emotion data into the generation AI and have the generation AI estimate the emotion and adjust the sensor sensitivity.

[0087] The sensor unit can analyze the cat's past physical condition data and select an appropriate sensor placement. The sensor unit can, for example, analyze the cat's past physical condition data and select an 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 the sensor placement based on the past physical condition data. Part or all of the above-mentioned processing in the sensor unit can be performed, for example, using AI or without AI. For example, the sensor unit can input the cat's past physical condition data to the generation AI and cause the generation AI to select the optimal sensor placement.

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

[0089] The sensor unit can add a sensor that measures the cat's environmental temperature and humidity to improve the accuracy of the physical condition determination. The sensor unit can add a sensor that measures the cat's environmental temperature and humidity to improve the accuracy of the 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. As a result, the accuracy of the physical condition determination is improved by measuring the environmental temperature and humidity. Part or all of the above-mentioned processing in the sensor unit may be performed, for example, using AI, or may be performed without using AI. For example, the sensor unit can input environmental temperature and humidity data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the physical condition determination.

[0090] The sensor unit can have a function of monitoring the cat's diet and water intake. The sensor unit can have a function of monitoring 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. The sensor unit can also 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 the cat's health to be managed more accurately by monitoring the diet and water intake. Some or all of the above-mentioned processing in the sensor unit can be performed, for example, using AI, or can be performed without using AI. For example, the sensor unit can input the cat's diet and water intake data to the generation AI and cause the generation AI to perform physical condition determination.

[0091] The analysis unit can estimate the cat's emotions and adjust the analysis algorithm based on the estimated cat's emotions. The analysis unit, for example, estimates the cat's emotions and adjusts the analysis algorithm based on the estimated cat's emotions. For example, the analysis unit sets the analysis algorithm gently when the cat is relaxed. Also, the analysis unit can set the analysis algorithm quickly when the cat is excited. Furthermore, the analysis unit can set the analysis algorithm to a medium level when the cat is stressed. This improves the analysis accuracy by adjusting the analysis algorithm based on the cat's emotions. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the cat's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0092] The analysis unit can improve the analysis accuracy by referring to the cat's past health data. The analysis unit can improve the analysis accuracy 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 the current body temperature data for analysis. The analysis unit can also refer to the cat's past heart rate data and compare it with the current heart rate data for analysis. Furthermore, the analysis unit can refer to the cat's past movement data and compare it with the current movement data for analysis. As a result, the analysis accuracy is improved by referring to the past health data. A part 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 the cat's past health data to the generation AI and cause the generation AI to improve the analysis accuracy.

[0093] The analysis unit can have a function of detecting a specific behavioral pattern of the cat and detecting abnormalities at an early stage. The analysis unit has a function of detecting a specific behavioral pattern of the cat and detecting abnormalities at an early stage, for example. For example, the analysis unit detects abnormalities when the cat moves differently than normal. The analysis unit can also detect abnormalities when the cat's heart rate is different from normal. Furthermore, the analysis unit can detect abnormalities when the cat's body temperature is different from normal. By detecting specific behavioral patterns, abnormalities can be detected at an early stage. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the cat's behavioral data to the generation AI and cause the generation AI to perform early detection of abnormalities.

[0094] The analysis unit can estimate the cat's emotion and adjust the display method of the analysis result based on the estimated cat's emotion. The analysis unit, for example, estimates the cat's emotion and adjusts the display method of the analysis result based on the estimated cat's emotion. For example, the analysis unit displays the analysis result simply when the cat is relaxed. Also, the analysis unit can display the analysis result in detail when the cat is excited. Furthermore, the analysis unit can display the analysis result moderately when the cat is stressed. This makes it easier to understand the analysis result by adjusting the display method based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the cat's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0095] The analysis unit can improve the analysis accuracy by incorporating feedback from the cat's owner. The analysis unit can improve the analysis accuracy by incorporating, for example, feedback from the cat's owner. For example, the analysis unit reflects the cat's physical condition information provided by the owner in the analysis. The analysis unit can also reflect the cat's behavior information provided by the owner in the analysis. Furthermore, the analysis unit can reflect the cat's diet information provided by the owner in the analysis. As a result, the analysis accuracy is improved by incorporating the owner's feedback. A part 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 owner's feedback data into the generation AI and cause the generation AI to improve the analysis accuracy.

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

[0097] The transmission unit can estimate the cat's emotion and adjust the frequency of data transmission based on the estimated cat's emotion. The transmission unit, for example, estimates the cat's emotion and adjusts the frequency of data transmission based on the estimated cat's emotion. For example, the transmission unit sets the data transmission frequency low when the cat is relaxed. Also, the transmission unit can set the data transmission frequency high when the cat is excited. Furthermore, the transmission unit can set the data transmission frequency to a medium level when the cat is stressed. This allows efficient data transmission by adjusting the frequency of data transmission based on the cat's emotion. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the transmission unit can input the cat's emotion data to the generation AI and cause the generation 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, when the body temperature data is abnormal, the transmitting unit transmits the data with the highest priority. Also, when the heart rate data is abnormal, the transmitting unit can transmit the data with priority. Furthermore, when the movement data is abnormal, the transmitting unit can transmit the data with normal priority. As a result, important data is transmitted with priority by determining the transmission priority based on the importance of the data. A part or all of the above-mentioned processing in the transmitting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmitting unit can input the importance of the data to the generating AI and cause the generating AI to determine the transmission priority.

[0099] The transmitting unit can encrypt data and strengthen security. The transmitting unit, for example, encrypts data and strengthens security. For example, the transmitting unit encrypts and transmits body temperature data. The transmitting unit can also encrypt and transmit heart rate data. Furthermore, the transmitting unit can also encrypt and transmit movement data. Thereby, security is strengthened by encrypting data. A part or all of the above-mentioned processing in the transmitting unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmitting unit can input an encryption algorithm to the generating AI and cause the generating AI to encrypt the data.

[0100] The transmission unit can estimate the cat's emotion and adjust the content of the transmission data based on the estimated cat's emotion. The transmission unit, for example, estimates the cat's emotion and adjusts the content of the transmission data based on the estimated cat's emotion. For example, the transmission unit transmits only basic data when the cat is relaxed. Also, the transmission unit can transmit detailed data when the cat is excited. Furthermore, the transmission unit can transmit moderate data when the cat is stressed. In this way, the necessary information is appropriately transmitted by adjusting the content of the transmission data based on the cat's emotion. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI or may be performed without using AI. For example, the transmission unit can input the cat's emotion data to the generation AI and cause the generation AI to adjust the content of the transmission data.

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

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

[0103] The display unit can estimate the cat's emotion and customize the display content based on the estimated cat's emotion. The display unit, for example, estimates the cat's emotion and customizes the display content based on the estimated cat's emotion. 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. This allows the owner to be provided with information that is easier to understand by customizing the display content based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to customize the display content.

[0104] The display unit can have a function of visually displaying past data in a graph or chart. The display unit can have a function of visually displaying past data in a graph or chart. For example, the display unit can display the cat's body temperature data in a graph. The display unit can also display the cat's heart rate data in a chart. Furthermore, the display unit can display the cat's movement data in a graph. This makes it easier to understand the data by visually displaying the past data. A part or all of the above-mentioned 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 past data to a generation AI and cause the generation AI to generate a graph or chart.

[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 when the cat's body temperature is abnormal. Also, the display unit can display an alert when the cat's heart rate is abnormal. Furthermore, the display unit can display an alert when the cat's movements are abnormal. This allows the owner to respond quickly by displaying an alert according to the cat's health condition. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the cat's health data to the generation AI and cause the generation AI to display an alert.

[0106] The display unit can estimate the cat's emotion and adjust the display interface based on the estimated cat's emotion. The display unit, for example, estimates the cat's emotion and adjusts the display interface based on the estimated cat's emotion. For example, when the cat is relaxed, the display unit provides an interface with a calm color. Also, when the cat is excited, the display unit can provide an interface with a bright color. Furthermore, when the cat is stressed, the display unit can provide a simple and highly visible interface. This allows the owner to check information more comfortably by adjusting the display interface based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to 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, when the owner's smartphone is in silent mode, the display unit notifies by vibration. Also, when the owner's smartphone is in normal mode, the display unit can notify by voice. Furthermore, when the owner's smartphone is in do not disturb mode, the display unit can suppress the notification. As a result, the display method is customized according to the notification settings of the owner's smartphone, so that the notification is appropriately performed. A part or all of the above-mentioned 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 the notification setting data of the smartphone to the generation AI and cause the generation AI to execute customization of the display method.

[0108] The display unit can have a function of simultaneously displaying data of multiple cats. The display unit has a function of simultaneously displaying data of multiple cats, for example. For example, the display unit can simultaneously display body temperature data of multiple cats. Also, the display unit can simultaneously display heart rate data of multiple cats. Furthermore, the display unit can simultaneously display movement data of multiple cats. This allows the owner to check the condition of multiple cats at once by simultaneously displaying data of multiple cats. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input data of multiple cats to the generation AI and cause the generation AI to execute the setting for simultaneous display.

[0109] The cat language translation unit can estimate the cat's emotion and adjust the expression method of the translation result based on the estimated cat's emotion. The cat language translation unit, for example, estimates the cat's emotion and adjusts the expression method of the translation result based on the estimated cat's emotion. For example, the cat language translation unit displays a simple translation result when the cat is relaxed. In addition, the cat language translation unit can display a detailed translation result when the cat is excited. Furthermore, the cat language translation unit can display a medium translation result when the cat is stressed. This allows the owner to provide a translation result that is easier to understand by adjusting the expression method of the translation result based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, an AI, or may be performed without using an 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 the translation accuracy by referring to the cat's past meow data. The cat language translation unit can improve the translation accuracy by referring to the cat's past meow data, for example. For example, the cat language translation unit can refer to the cat's past meow data and translate it by comparing it with the current meow. The cat language translation unit can also refer to the cat's past meow pattern and translate it by comparing it with the current meow. Furthermore, the cat language translation unit can refer to the frequency of the cat's past meow and translate it by comparing it with the current meow. As a result, the translation accuracy is improved by referring to the past meow data. A part or all of the above-mentioned processing in the cat language translation unit can be performed, for example, using AI or can be performed without using AI. For example, the cat language translation unit can input the cat's past meow data to the generation AI and cause the generation AI to improve the translation accuracy.

[0111] The cat language translation unit can have a function of detecting a specific cat meow pattern and displaying the meaning in more detail. The cat language translation unit can, for example, detect a specific cat meow pattern and display the meaning in more detail. For example, the cat language translation unit can detect a specific cat meow pattern and display "I'm hungry". The cat language translation unit can also detect a specific cat meow pattern and display "I want to play". The cat language translation unit can also detect a specific cat meow pattern and display "I'm sleepy". By detecting a specific meow pattern, the meaning can be displayed in more detail. A part or all of the above-mentioned processing in the cat language translation unit can be performed, for example, using AI or can be performed without using AI. For example, the cat language translation unit can input cat meow data to the generation AI and cause the generation AI to detect a specific meow pattern and display the meaning.

[0112] The cat language translation unit can estimate the cat's emotion and adjust the display method of the translation result based on the estimated cat's emotion. The cat language translation unit, for example, estimates the cat's emotion and adjusts the display method of the translation result based on the estimated cat's emotion. For example, the cat language translation unit provides 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 owner to provide a translation result that is easier to understand by adjusting the display method of the translation result based on the cat's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cat language translation unit can input cat emotion data to the generation AI and cause the generation AI to adjust the display method of the translation result.

[0113] The cat language translation unit can analyze the volume and frequency of the cat's meow to improve the translation accuracy. The cat language translation unit can analyze, for example, the volume and frequency of the cat's meow to improve the translation accuracy. For example, the cat language translation unit can analyze the volume of the cat's meow to improve the translation accuracy. In addition, the cat language translation unit can analyze the frequency of the cat's meow to improve the translation accuracy. Furthermore, the cat language translation unit can analyze the pattern of the cat's meow to improve the translation accuracy. As a result, the translation accuracy is improved by analyzing the volume and frequency of the meow. A part or all of the above-mentioned processing in the cat language translation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the cat language translation unit can input cat's meow data to the generation AI and cause the generation AI to perform the analysis of the volume and frequency.

[0114] The cat language translation unit can have a function of displaying the translation result in multiple languages. The cat language translation unit has a function of displaying the translation result in multiple languages, for example. For example, the cat language translation unit displays the translation result in English. Also, the cat language translation unit can display the translation result in Japanese. Furthermore, the cat language translation unit can 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, for example, using AI, or may be performed without using AI. For example, the cat language translation unit can input the translation result to a generation AI and cause the generation AI to display 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 include a function for learning the cat's behavioral patterns and detecting abnormal behavior. For example, the analysis unit detects abnormal behavior when the cat moves differently than usual. The analysis unit can also detect abnormal behavior when the cat's food intake or water intake is different from normal. Furthermore, the analysis unit can detect abnormal behavior when the cat's meowing pattern is different from normal. This allows the owner to quickly respond by learning the cat's behavioral patterns and discovering abnormal behavior early.

[0117] The system for determining the physical condition and mood of a cat can further include a function for evaluating the stress level of the cat 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. The analysis unit can also evaluate the stress level based on the cat's body temperature data. This makes it possible to more effectively manage the cat's health by evaluating the cat's stress level and providing the owner with advice for stress reduction.

[0118] The system for determining the physical condition and mood of a cat can further include a function for monitoring the cat's sleep pattern and evaluating the quality of the 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 the 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 the sleep. In this way, by monitoring the cat's sleep pattern and evaluating the quality of the sleep, owners can help manage their cat's health.

[0119] The system for determining the physical condition and mood of a cat can further include a function for monitoring the amount of movement of the cat and providing advice for maintaining an appropriate amount of movement. For example, the sensor unit monitors the movement of the cat and measures the amount of movement. The analysis unit can analyze the data on the amount of movement of the cat and evaluate the appropriate amount of movement. Furthermore, the analysis unit can evaluate the amount of movement based on the weight data of the cat. This makes it possible to more effectively manage the health of a cat by monitoring the amount of movement of the cat and providing advice for maintaining an appropriate amount of movement.

[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 food. For example, the sensor unit monitors the amount of food eaten by the cat and analyzes the eating pattern. The analysis unit can also evaluate the quality of the food based on the cat's weight data. The analysis unit can also evaluate the quality of the food based on the cat's body temperature data. In this way, monitoring the cat's eating pattern and evaluating the quality of the food can be useful for owners in managing their cat's health.

[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 likes 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 games that will relax the cat if the cat is stressed. In this way, by suggesting appropriate games based on the cat's emotions, it is possible to help reduce the cat's stress and maintain its health.

[0122] The system for determining the physical condition and mood of a cat can further include a function for estimating the cat's emotions and suggesting the cat's favorite food based on the estimated emotions. For example, the analysis unit can suggest a normal 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 it possible to more effectively manage the cat's health by suggesting an appropriate meal based on the cat's emotions.

[0123] The system for determining the physical condition and mood of a cat 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, by suggesting appropriate environmental settings based on the cat's emotions, it is possible to help reduce the cat's stress and maintain its 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. Also, 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, by suggesting appropriate music based on the cat's emotions, it is possible to help reduce the cat's stress and maintain its 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 helps the cat relax if the cat is stressed. In this way, by suggesting an appropriate toy based on the cat's emotions, it is possible to help reduce the cat's stress and maintain its health.

[0126] The process flow of the second embodiment will be briefly described below.

[0127] Step 1: The sensor unit collects the cat's body temperature, heart rate, and movement. For example, the sensor unit has a built-in body temperature sensor, heart rate sensor, and acceleration sensor, and collects data such as the cat's body temperature, heart rate, and movement. The body temperature sensor is, for example, placed on a part that is worn around the cat's neck, and measures the cat's body temperature. The heart rate sensor is, for example, placed on the cat's chest, and measures the heart rate. The acceleration sensor is, for example, placed inside the collar to detect the cat's movement. 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 techniques such as deep learning and support vector machines. The analysis unit determines that a high body temperature indicates a possibility of fever, and that a fast heart rate indicates a possibility of excitement. Step 3: The transmitter transmits the analysis results to a smartphone. For example, the transmitter transmits data using Bluetooth. Bluetooth can use versions such as Bluetooth 4.0 and Bluetooth Low Energy. The transmitter transmits the analysis results to the smartphone in real time, allowing the owner to instantly check the condition of the cat. Step 4: The display unit displays the analysis results sent to the smartphone. For example, the display unit has a function to display data in real time. The display unit also has a function to store and analyze past data, enabling 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice 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<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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such 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 above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required 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, the heart rate sensor, and the acceleration sensor collect the body temperature, the heart rate, and the movement of the cat using the camera 42 and the 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 physical condition and the mood of the cat. The transmission unit transmits the analysis result to a smartphone by Bluetooth using, for example, the communication I / F 44 of the smart device 14. The display unit displays the analysis result by, for example, the display 40A of the smart device 14. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

[0139] Fig. 4 shows an example of 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[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, the heart rate sensor, and the acceleration sensor collect the body temperature, the heart rate, and the movement of the cat using the camera 42 and the 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 physical condition and the mood of the cat. The transmission unit transmits the analysis result to a smartphone by Bluetooth using, for example, the communication I / F 44 of the smart glasses 214. The display unit displays the analysis result on, for example, the display of the smart glasses 214. The correspondence relationship between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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.

[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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[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, the heart rate sensor, and the acceleration sensor collect the body temperature, the heart rate, and the movement of the cat using the camera 42 and the 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 physical condition and the mood of the cat. The transmission unit transmits the analysis result to a smartphone by Bluetooth using, for example, the communication I / F 44 of the headset terminal 314. The display unit displays the analysis result by, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes 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, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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, the RAM 30, and the 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 the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of 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 performed in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) 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 a voice indicating a user input for 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of 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 specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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, the heart rate sensor, and the acceleration sensor collect the body temperature, the heart rate, and the movement of the cat using the camera 42 and the 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 physical condition and the mood of the cat. The transmission unit transmits the analysis result to a smartphone by Bluetooth using, for example, the communication I / F 44 of the robot 414. The display unit displays the analysis result on, for example, the display of the robot 414. The correspondence between each unit and the device or the control unit is not limited to the above-mentioned example, and various changes are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0182] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in 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 of emotion map 400 and usually 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 inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0185] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0186] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," 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 Universal Serial Bus (USB) 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] In addition, 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 upon 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] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0193] The hardware resource that executes the specific process may be one of these various processors, or may be 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 configuration using one 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 configuration using a processor that realizes the functions of the 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, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0197] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and 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 is Analyze data using machine learning algorithms 2. The system of claim 1, (Appendix 3) The transmission unit is Sending data using Bluetooth 2. The system of claim 1, (Appendix 4) The display unit is Ability to display data in real time 2. The system of claim 1, (Appendix 5) The display unit is Equipped with a function to store and analyze past data 2. The system of claim 1, (Appendix 6) The analysis unit is It is equipped with a cat language translation section that analyzes cat meows and displays the results in text. 2. The system of claim 1, (Appendix 7) The sensor unit includes: Estimate the cat's emotions and adjust the sensitivity of the sensor based on the estimated emotions. 2. The system of claim 1, (Appendix 8) The sensor unit includes: Analyze the cat's past physical condition data and select appropriate sensor placement 2. The system of claim 1, (Appendix 9) The sensor unit includes: 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 includes: Add sensors to measure the cat's environmental temperature and humidity to improve the accuracy of determining its physical condition. 2. The system of claim 1, (Appendix 11) The sensor unit includes: Equipped with a function to monitor your cat's food and water intake 2. The system of claim 1, (Appendix 12) The analysis unit is Infer the cat's emotions and adjust the analysis algorithm based on the inferred emotions. 2. The system of claim 1, (Appendix 13) The analysis unit is Improve analysis accuracy by referencing cats' past health data 2. The system of claim 1, (Appendix 14) The analysis unit is Equipped with the ability to detect specific behavioral patterns of cats and detect abnormalities at an early stage 2. The system of claim 1, (Appendix 15) The analysis unit is Estimate the cat's emotions and adjust the way the analysis results are displayed based on the estimated cat's emotions 2. The system of claim 1, (Appendix 16) The analysis unit is Incorporating feedback from cat owners to improve analysis accuracy 2. The system of claim 1, (Appendix 17) The analysis unit is 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 is Infer the cat's emotions and adjust the frequency of data transmission based on the estimated cat's emotions. 2. The system of claim 1, (Appendix 19) The transmission unit is Prioritize sending based on data importance 2. The system of claim 1, (Appendix 20) The transmission unit is Encrypting data to enhance security 2. The system of claim 1, (Appendix 21) The transmission unit is Estimate the cat's emotions and adjust the content of the transmitted data based on the estimated cat's emotions. 2. The system of claim 1, (Appendix 22) The transmission unit is Option to transmit data using Wi-Fi or mobile networks 2. The system of claim 1, (Appendix 23) The transmission unit is Compresses data to improve transmission speed 2. The system of claim 1, (Appendix 24) The display unit is Estimate the cat's emotions and customize the display content based on the estimated cat's emotions 2. The system of claim 1, (Appendix 25) The display unit is Equipped with a function to visually display past data in graphs and charts 2. The system of claim 1, (Appendix 26) The display unit is Equipped with an alert function according to the cat's health condition 2. The system of claim 1, (Appendix 27) The display unit is Estimate the cat's emotions and adjust the display interface based on the estimated cat's emotions 2. The system of claim 1, (Appendix 28) The display unit is 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 is Equipped with 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 is Estimate the cat's emotions and adjust the way the translation is expressed based on the estimated cat's emotions 2. The system of claim 1, (Appendix 31) The cat language translation unit is Improve translation accuracy by referencing past cat meow data 2. The system of claim 1, (Appendix 32) The cat language translation unit is 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 is Estimate the cat's emotions and adjust the way the translation is displayed based on the estimated cat's emotions 2. The system of claim 1, (Appendix 34) The cat language translation unit is 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 is Equipped with 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 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:

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

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

4. The display unit is Equipped with a function to store and analyze past data 2. The system of claim 1 .

5. The analysis unit is It is equipped with a cat language translation section that analyzes cat meows and displays the results in text.

2. The system of claim 1 .

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

2. The system of claim 1 .

7. The sensor unit includes: Analyze the cat's past physical condition data and select appropriate sensor placement 2. The system of claim 1 .

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