System

Abstract

An object of the system according to the embodiment is to perform urinalysis without applying stress to a pet or an owner.SOLUTION: A system includes a unit for collecting urine, a test unit, an analysis unit, and a feedback unit. The urine collecting part collects urine of the pet. The test unit tests the urine collected by the urine collecting unit. The analysis unit analyzes a result inspected by the inspection unit. The feedback part feeds back the result analyzed by the analysis part to the owner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that conducting a pet's urine test can be stressful for both the pet and the owner.

[0005] The system according to the embodiment aims to perform urine tests without causing stress to pets or their owners. [Means for solving the problem]

[0006] The system according to the embodiment includes a urine collection unit, a testing unit, an analysis unit, and a feedback unit. The urine collection unit collects urine from a pet. The testing unit tests the urine collected by the urine collection unit. The analysis unit analyzes the results of the test by the testing unit. The feedback unit feeds back the results of the analysis by the analysis unit to the owner. [Effects of the Invention]

[0007] The system according to the embodiment allows urine tests to be performed without causing stress to pets or their owners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The urine testing system according to an embodiment of the present invention is a system that allows pet owners to easily test their pet's urine at home, analyze it using AI, and understand their pet's health condition. This reduces the stress of pet checkups and allows owners to easily understand their pet's health condition at home.

[0029] The urine testing system according to the embodiment includes a urine collection unit, a testing unit, an analysis unit, and a feedback unit. The urine collection unit collects urine from a pet. For example, the urine collection unit may collect urine using cat litter sheets or dog urine collection pads. Alternatively, the urine collection unit may use a special gel that automatically absorbs the pet's urine. For example, the special gel absorbs the urine and prevents leakage and evaporation. Furthermore, the urine collection unit may use a pad with a sensor that detects the pet's weight and movement to automatically collect urine. For example, a weight sensor detects the pet's weight and optimizes the timing of urine collection. The testing unit tests the urine collected by the urine collection unit. For example, the testing unit may use a microfluidic device for analyzing urine components in real time. Alternatively, the testing unit may use a sensor that utilizes nanotechnology to detect urine components. For example, nanoparticles may be used to detect trace components in urine with high accuracy. The analysis unit analyzes the results of the test performed by the testing unit. For example, the analysis unit may also consider the pet's past health and lifestyle data when analyzing the urine test data. Furthermore, when analyzing urine test data, the analysis unit can perform customized analysis according to the pet's species, age, and gender. Furthermore, when analyzing urine test data, the analysis unit can compare the urine test data with data from other pets to detect abnormal values. The feedback unit feeds back the results of the analysis performed by the analysis unit to the pet owner. For example, when feeding back the analysis results, the feedback unit can provide a specific action plan according to the pet's health condition. Furthermore, when feeding back the analysis results, the feedback unit can also provide dietary and exercise advice according to the pet's health condition. Furthermore, when feeding back the analysis results, the feedback unit can analyze the pet owner's emotions using an emotion estimation function and customize the feedback content. As a result, the urine testing system according to the embodiment allows pet owners to easily test their pet's urine at home and understand their pet's health condition. For example, pet owners can regularly monitor their pet's health condition and detect health problems early.

[0030] The urine collection unit uses a special gel that automatically absorbs pet urine, preventing leakage or evaporation. The urine collection unit, for example, uses a special gel in the urine collection container to automatically absorb pet urine. This gel contains a highly absorbent polymer, which instantly absorbs urine and prevents leakage or evaporation. For example, the gel solidifies as it absorbs urine, preventing the liquid from moving within the container. This prevents urine leakage and evaporation, enabling accurate testing.

[0031] The urine collection unit can use a sensor-equipped pad that detects the pet's weight or movement to automatically collect urine. For example, the urine collection unit incorporates a weight sensor into the urine collection pad, which automatically collects urine when the pet stands on the pad. This sensor detects the pet's weight and optimizes the timing at which the urine is absorbed by the pad. For example, when the weight sensor detects the pet's weight, the absorption mechanism is activated. This allows urine to be collected automatically by detecting the pet's weight and movement.

[0032] The testing unit can use a device for analyzing urine components in real time. For example, the testing unit incorporates a microfluidic device into a urine test kit to analyze urine components in real time. This device passes urine through a microchannel and rapidly detects components. For example, it measures sugar and protein in urine in real time. This allows urine components to be analyzed in real time.

[0033] The testing unit can use a sensor to detect urine components. For example, the testing unit can incorporate a sensor that utilizes nanotechnology into a urine test kit to detect urine components with high accuracy. This sensor uses nanoparticles to detect trace components in urine. For example, it can measure trace proteins in urine with high accuracy. This allows urine components to be detected with high accuracy.

[0034] When analyzing urine test data, the analysis unit can also take into account the pet's past health data or lifestyle data. For example, when the generation AI analyzes urine test data, the analysis unit takes into account the pet's past health data. This data includes past urine test results and medical history, and the AI ​​comprehensively evaluates the health condition. For example, it compares the data with past data to check for any abnormalities. This allows the analysis to take into account the pet's past health data and lifestyle data.

[0035] When analyzing urine test data, the analysis unit can perform a customized analysis according to the pet's species, age, and sex. For example, when the generation AI analyzes urine test data, the analysis unit performs a customized analysis according to the pet's species. For example, it takes into account the differences in the urine components of dogs and cats and performs an analysis appropriate for each. This makes it possible to perform a customized analysis according to the pet's species, age, and sex.

[0036] When analyzing urine test data, the analysis unit can compare it with data from other pets to detect outliers. For example, when the generation AI analyzes urine test data, the analysis unit compares it with data from other pets. This comparison detects outliers. For example, it identifies abnormalities by comparing it with data from pets of the same species or age. This makes it possible to detect outliers by comparing it with data from other pets.

[0037] When feeding back the analysis results, the feedback unit can provide a specific action plan according to the pet's health condition. For example, when the generation AI feeds back the analysis results, the feedback unit provides a specific action plan according to the pet's health condition. For example, if the sugar level in the urine is high, the feedback unit suggests reviewing the diet and increasing the amount of exercise. This makes it possible to provide a specific action plan according to the pet's health condition.

[0038] When feeding back the analysis results, the feedback unit can provide dietary or exercise advice according to the pet's health condition. For example, when the generation AI feeds back the analysis results, the feedback unit provides dietary advice according to the pet's health condition. For example, if the sugar level in the urine is high, a low-carbohydrate diet is suggested. This makes it possible to provide dietary and exercise advice according to the pet's health condition.

[0039] The analysis unit can analyze long-term trends in the pet's health condition when analyzing the regular monitoring data. For example, when the generative AI analyzes the regular monitoring data, the analysis unit can analyze long-term trends in the pet's health condition. For example, fluctuations in sugar and protein in urine can be tracked over a long period of time to detect changes in the health condition. This makes it possible to analyze long-term trends in the pet's health condition.

[0040] The analysis unit can provide an alert function for early detection of abnormalities in the pet's health condition when analyzing the regular monitoring data. For example, when the generation AI analyzes the regular monitoring data, the analysis unit provides an alert function for early detection of abnormalities in the pet's health condition. For example, an alert can be issued if there is a sudden increase in sugar or protein in the urine. This allows for early detection of abnormalities in the pet's health condition.

[0041] When analyzing the regular monitoring data, the analysis unit can compare it with data from other pets to detect outliers. For example, when the generation AI analyzes the regular monitoring data, the analysis unit compares it with data from other pets. This comparison detects outliers. For example, it identifies abnormalities by comparing it with data from pets of the same species or age. This makes it possible to detect outliers by comparing it with data from other pets.

[0042] When analyzing regular monitoring data, the analysis unit also takes into account the pet's lifestyle data, enabling more accurate analysis. For example, when the generation AI analyzes regular monitoring data, the analysis unit takes into account the pet's lifestyle data. This data includes records of diet and exercise, and the AI ​​comprehensively evaluates the health condition. For example, the analysis correlates the contents of diet and amount of exercise with urine test results. This enables more accurate analysis by taking into account the pet's lifestyle data.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The urine testing system can also be equipped with sensors to detect the color and odor of pet urine. For example, an optical sensor can be used to detect urine color and monitor changes in urine color in real time. A gas sensor can also be used to detect the odor of urine and detect volatile components in urine. This allows changes in urine color and odor to be detected and abnormalities in health to be detected early.

[0045] The urine testing system can also be equipped with a sensor to measure the pH value of your pet's urine. For example, an electrode sensor can be used to measure the pH value of urine, allowing for real-time monitoring of the acidity or alkalinity of urine. It can also record fluctuations in urine pH and analyze long-term health trends. This allows for early detection of health abnormalities.

[0046] The urine testing system can also be equipped with a sensor that measures hormone levels in a pet's urine. For example, a biosensor can be used to measure stress hormones and sex hormones in urine, allowing for real-time monitoring of hormone level fluctuations. It can also record hormone level fluctuations and evaluate a pet's stress and reproductive status. This allows for early detection of health abnormalities by detecting hormone level fluctuations in urine.

[0047] The urine testing system can also be equipped with sensors to measure the concentration of vitamins and minerals in your pet's urine. For example, a chemical sensor can be used to measure the concentration of vitamin C or calcium in urine, allowing for real-time monitoring of nutritional status. It can also record fluctuations in vitamin and mineral concentrations to assess your pet's nutritional status. This allows for early detection of health abnormalities by detecting fluctuations in the concentration of vitamins and minerals in urine.

[0048] The urine testing system can also be equipped with a sensor to measure the concentration of drugs or toxins in a pet's urine. For example, a chromatography sensor can be used to measure the concentration of drugs or toxins in urine, allowing for real-time monitoring of the presence of drugs or toxins. Fluctuations in drug or toxin concentration can also be recorded to assess the pet's intoxication status. This allows for early detection of abnormalities in health by detecting fluctuations in the concentration of drugs or toxins in urine.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The urine collection unit collects the pet's urine. For example, the urine collection unit can use cat litter sheets or dog urine collection pads to collect urine. The urine collection unit can also use a special gel that automatically absorbs the pet's urine. Furthermore, the urine collection unit can also use a pad with a sensor that detects the pet's weight and movement to automatically collect urine. Step 2: The testing unit tests the urine collected by the urine collection unit. For example, the testing unit may use a microfluidic device to analyze urine components in real time. The testing unit may also use a nanotechnology-based sensor to detect urine components. Step 3: The analysis unit analyzes the results of the test performed by the testing unit. For example, when analyzing urine test data, the analysis unit may also consider the pet's past health data and lifestyle data. When analyzing urine test data, the analysis unit may also perform customized analysis according to the pet's species, age, and sex. Furthermore, when analyzing urine test data, the analysis unit may compare the data with data from other pets to detect abnormal values. Step 4: The feedback unit feeds back the results of the analysis by the analysis unit to the owner. For example, when feeding back the analysis results, the feedback unit may provide a specific action plan according to the pet's health condition. When feeding back the analysis results, the feedback unit may also provide advice on diet and exercise according to the pet's health condition. Furthermore, when feeding back the analysis results, the feedback unit may analyze the owner's emotions using an emotion estimation function and customize the feedback content.

[0051] (Example 2) The urine testing system according to an embodiment of the present invention is a system that allows pet owners to easily test their pet's urine at home, analyze it using AI, and understand their pet's health condition. This reduces the stress of pet checkups and allows owners to easily understand their pet's health condition at home.

[0052] The urine testing system according to the embodiment includes a urine collection unit, a testing unit, an analysis unit, and a feedback unit. The urine collection unit collects urine from a pet. For example, the urine collection unit may collect urine using cat litter sheets or dog urine collection pads. Alternatively, the urine collection unit may use a special gel that automatically absorbs the pet's urine. For example, the special gel absorbs the urine and prevents leakage and evaporation. Furthermore, the urine collection unit may use a pad with a sensor that detects the pet's weight and movement to automatically collect urine. For example, a weight sensor detects the pet's weight and optimizes the timing of urine collection. The testing unit tests the urine collected by the urine collection unit. For example, the testing unit may use a microfluidic device for analyzing urine components in real time. Alternatively, the testing unit may use a sensor that utilizes nanotechnology to detect urine components. For example, nanoparticles may be used to detect trace components in urine with high accuracy. The analysis unit analyzes the results of the test performed by the testing unit. For example, the analysis unit may also consider the pet's past health and lifestyle data when analyzing the urine test data. Furthermore, when analyzing urine test data, the analysis unit can perform customized analysis according to the pet's species, age, and gender. Furthermore, when analyzing urine test data, the analysis unit can compare the urine test data with data from other pets to detect abnormal values. The feedback unit feeds back the results of the analysis performed by the analysis unit to the pet owner. For example, when feeding back the analysis results, the feedback unit can provide a specific action plan according to the pet's health condition. Furthermore, when feeding back the analysis results, the feedback unit can also provide dietary and exercise advice according to the pet's health condition. Furthermore, when feeding back the analysis results, the feedback unit can analyze the pet owner's emotions using an emotion estimation function and customize the feedback content. As a result, the urine testing system according to the embodiment allows pet owners to easily test their pet's urine at home and understand their pet's health condition. For example, pet owners can regularly monitor their pet's health condition and detect health problems early.

[0053] The urine collection unit uses a special gel that automatically absorbs pet urine, preventing leakage or evaporation. The urine collection unit, for example, uses a special gel in the urine collection container to automatically absorb pet urine. This gel contains a highly absorbent polymer, which instantly absorbs urine and prevents leakage or evaporation. For example, the gel solidifies as it absorbs urine, preventing the liquid from moving within the container. This prevents urine leakage and evaporation, enabling accurate testing.

[0054] The urine collection unit can use a sensor-equipped pad that detects the pet's weight or movement to automatically collect urine. For example, the urine collection unit incorporates a weight sensor into the urine collection pad, which automatically collects urine when the pet stands on the pad. This sensor detects the pet's weight and optimizes the timing at which the urine is absorbed by the pad. For example, when the weight sensor detects the pet's weight, the absorption mechanism is activated. This allows urine to be collected automatically by detecting the pet's weight and movement.

[0055] The urine collection unit can use the emotion estimation function to detect when the pet is relaxed and collect urine at that timing. The urine collection unit, for example, uses the emotion estimation function to detect the pet's relaxed state. This function monitors the pet's heart rate and breathing pattern to identify when the pet is relaxed. For example, urine is collected when the heart rate is stable. This allows urine to be collected when the pet is relaxed.

[0056] The testing unit can use a device for analyzing urine components in real time. For example, the testing unit incorporates a microfluidic device into a urine test kit to analyze urine components in real time. This device passes urine through a microchannel and rapidly detects components. For example, it measures sugar and protein in urine in real time. This allows urine components to be analyzed in real time.

[0057] The testing unit can use a sensor to detect urine components. For example, the testing unit can incorporate a sensor that utilizes nanotechnology into a urine test kit to detect urine components with high accuracy. This sensor uses nanoparticles to detect trace components in urine. For example, it can measure trace proteins in urine with high accuracy. This allows urine components to be detected with high accuracy.

[0058] When analyzing urine test data, the analysis unit can also take into account the pet's past health data or lifestyle data. For example, when the generation AI analyzes urine test data, the analysis unit takes into account the pet's past health data. This data includes past urine test results and medical history, and the AI ​​comprehensively evaluates the health condition. For example, it compares the data with past data to check for any abnormalities. This allows the analysis to take into account the pet's past health data and lifestyle data.

[0059] When analyzing urine test data, the analysis unit can perform a customized analysis according to the pet's species, age, and sex. For example, when the generation AI analyzes urine test data, the analysis unit performs a customized analysis according to the pet's species. For example, it takes into account the differences in the urine components of dogs and cats and performs an analysis appropriate for each. This makes it possible to perform a customized analysis according to the pet's species, age, and sex.

[0060] When analyzing urine test data, the analysis unit can compare it with data from other pets to detect outliers. For example, when the generation AI analyzes urine test data, the analysis unit compares it with data from other pets. This comparison detects outliers. For example, it identifies abnormalities by comparing it with data from pets of the same species or age. This makes it possible to detect outliers by comparing it with data from other pets.

[0061] When feeding back the analysis results, the feedback unit can provide a specific action plan according to the pet's health condition. For example, when the generation AI feeds back the analysis results, the feedback unit provides a specific action plan according to the pet's health condition. For example, if the sugar level in the urine is high, the feedback unit suggests reviewing the diet and increasing the amount of exercise. This makes it possible to provide a specific action plan according to the pet's health condition.

[0062] When feeding back the analysis results, the feedback unit can provide dietary or exercise advice according to the pet's health condition. For example, when the generation AI feeds back the analysis results, the feedback unit provides dietary advice according to the pet's health condition. For example, if the sugar level in the urine is high, a low-carbohydrate diet is suggested. This makes it possible to provide dietary and exercise advice according to the pet's health condition.

[0063] When feeding back the analysis results, the feedback unit can analyze the owner's emotions using an emotion estimation function and customize the feedback content. The feedback unit, for example, uses the emotion estimation function to analyze the owner's emotions. This function analyzes the owner's facial expressions and voice to identify the emotion. For example, if the owner is feeling anxious, the feedback unit can provide feedback to help the owner relax. This makes it possible to provide feedback content that corresponds to the owner's emotions.

[0064] The analysis unit can analyze long-term trends in the pet's health condition when analyzing the regular monitoring data. For example, when the generative AI analyzes the regular monitoring data, the analysis unit can analyze long-term trends in the pet's health condition. For example, fluctuations in sugar and protein in urine can be tracked over a long period of time to detect changes in the health condition. This makes it possible to analyze long-term trends in the pet's health condition.

[0065] The analysis unit can provide an alert function for early detection of abnormalities in the pet's health condition when analyzing the regular monitoring data. For example, when the generation AI analyzes the regular monitoring data, the analysis unit provides an alert function for early detection of abnormalities in the pet's health condition. For example, an alert can be issued if there is a sudden increase in sugar or protein in the urine. This allows for early detection of abnormalities in the pet's health condition.

[0066] When analyzing the regular monitoring data, the analysis unit can compare it with data from other pets to detect outliers. For example, when the generation AI analyzes the regular monitoring data, the analysis unit compares it with data from other pets. This comparison detects outliers. For example, it identifies abnormalities by comparing it with data from pets of the same species or age. This makes it possible to detect outliers by comparing it with data from other pets.

[0067] When analyzing regular monitoring data, the analysis unit also takes into account the pet's lifestyle data, enabling more accurate analysis. For example, when the generation AI analyzes regular monitoring data, the analysis unit takes into account the pet's lifestyle data. This data includes records of diet and exercise, and the AI ​​comprehensively evaluates the health condition. For example, the analysis correlates the contents of diet and amount of exercise with urine test results. This enables more accurate analysis by taking into account the pet's lifestyle data.

[0068] When feeding back the analysis results, the feedback unit can use the emotion estimation function to analyze the owner's emotions and provide positive feedback. The feedback unit, for example, uses the emotion estimation function to analyze the owner's emotions. This function analyzes the owner's facial expressions and voice to identify the emotions. For example, if the owner is feeling anxious, positive feedback is provided. This makes it possible to provide positive feedback according to the owner's emotions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The urine testing system can also be equipped with sensors to detect the color and odor of pet urine. For example, an optical sensor can be used to detect urine color and monitor changes in urine color in real time. A gas sensor can also be used to detect the odor of urine and detect volatile components in urine. This allows changes in urine color and odor to be detected and abnormalities in health to be detected early.

[0071] The urine testing system can also be equipped with a sensor to measure the pH value of your pet's urine. For example, an electrode sensor can be used to measure the pH value of urine, allowing for real-time monitoring of the acidity or alkalinity of urine. It can also record fluctuations in urine pH and analyze long-term health trends. This allows for early detection of health abnormalities.

[0072] The urine testing system can also be equipped with a sensor that measures hormone levels in a pet's urine. For example, a biosensor can be used to measure stress hormones and sex hormones in urine, allowing for real-time monitoring of hormone level fluctuations. It can also record hormone level fluctuations and evaluate a pet's stress and reproductive status. This allows for early detection of health abnormalities by detecting hormone level fluctuations in urine.

[0073] The urine testing system can also be equipped with sensors to measure the concentration of vitamins and minerals in your pet's urine. For example, a chemical sensor can be used to measure the concentration of vitamin C or calcium in urine, allowing for real-time monitoring of nutritional status. It can also record fluctuations in vitamin and mineral concentrations to assess your pet's nutritional status. This allows for early detection of health abnormalities by detecting fluctuations in the concentration of vitamins and minerals in urine.

[0074] The urine testing system can also be equipped with a sensor to measure the concentration of drugs or toxins in a pet's urine. For example, a chromatography sensor can be used to measure the concentration of drugs or toxins in urine, allowing for real-time monitoring of the presence of drugs or toxins. Fluctuations in drug or toxin concentration can also be recorded to assess the pet's intoxication status. This allows for early detection of abnormalities in health by detecting fluctuations in the concentration of drugs or toxins in urine.

[0075] Furthermore, when analyzing the pet's urine test results, the urine testing system can estimate the owner's emotions and customize the feedback provided based on the analysis results. For example, if the owner is feeling anxious, it can provide reassuring feedback. If the owner is excited, it can also provide advice on how to calm down. This allows the system to provide feedback tailored to the owner's emotions and support more effective health management.

[0076] Furthermore, when analyzing the pet's urine test results, the urine testing system can estimate the owner's emotions and customize the feedback provided based on the analysis results. For example, if the owner is feeling stressed, it can provide advice on how to relax. If the owner is happy, it can emphasize positive feedback. This allows the system to provide feedback that reflects the owner's emotions and support more effective health management.

[0077] Furthermore, when analyzing the pet's urine test results, the urine testing system can estimate the owner's emotions and customize the feedback provided based on the analysis results. For example, if the owner is feeling anxious, it can provide reassuring feedback. If the owner is excited, it can also provide advice on how to calm down. This allows the system to provide feedback tailored to the owner's emotions and support more effective health management.

[0078] Furthermore, when analyzing the pet's urine test results, the urine testing system can estimate the owner's emotions and customize the feedback provided based on the analysis results. For example, if the owner is feeling stressed, it can provide advice on how to relax. If the owner is happy, it can emphasize positive feedback. This allows the system to provide feedback that reflects the owner's emotions and support more effective health management.

[0079] Furthermore, when analyzing the pet's urine test results, the urine testing system can estimate the owner's emotions and customize the feedback provided based on the analysis results. For example, if the owner is feeling anxious, it can provide reassuring feedback. If the owner is excited, it can also provide advice on how to calm down. This allows the system to provide feedback tailored to the owner's emotions and support more effective health management.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The urine collection unit collects the pet's urine. For example, the urine collection unit can use cat litter sheets or dog urine collection pads to collect urine. The urine collection unit can also use a special gel that automatically absorbs the pet's urine. Furthermore, the urine collection unit can also use a pad with a sensor that detects the pet's weight and movement to automatically collect urine. Step 2: The testing unit tests the urine collected by the urine collection unit. For example, the testing unit may use a microfluidic device to analyze urine components in real time. The testing unit may also use a nanotechnology-based sensor to detect urine components. Step 3: The analysis unit analyzes the results of the test performed by the testing unit. For example, when analyzing urine test data, the analysis unit may also consider the pet's past health data and lifestyle data. When analyzing urine test data, the analysis unit may also perform customized analysis according to the pet's species, age, and sex. Furthermore, when analyzing urine test data, the analysis unit may compare the data with data from other pets to detect abnormal values. Step 4: The feedback unit feeds back the results of the analysis by the analysis unit to the owner. For example, when feeding back the analysis results, the feedback unit may provide a specific action plan according to the pet's health condition. When feeding back the analysis results, the feedback unit may also provide advice on diet and exercise according to the pet's health condition. Furthermore, when feeding back the analysis results, the feedback unit may analyze the owner's emotions using an emotion estimation function and customize the feedback content.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] 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.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] 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.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 urine collection unit for collecting urine from pets; an examination unit that examines the urine collected by the urine collection unit; an analysis unit that analyzes the results of the inspection by the inspection unit; a feedback unit that feeds back the results of the analysis by the analysis unit to the owner. A system characterized by:

2. The urine collection unit includes: Uses a special gel that automatically absorbs pet urine, preventing leakage or evaporation 2. The system of claim 1.

3. The inspection unit Use a device to analyze urine components in real time 2. The system of claim 1.

4. The analysis unit Consider your pet's past health or lifestyle data when analyzing urine test data 2. The system of claim 1.

5. The feedback unit When providing feedback on the analysis results, provide specific action plans according to the pet's health condition 2. The system of claim 1.

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