system

Abstract

The system according to the embodiment aims to analyze pet waste and detect health conditions at an early stage. [Solution] A system according to an embodiment includes a transmission unit, an analysis unit, a determination unit, and a notification unit. The transmission unit transmits a photograph of excrement. The analysis unit analyzes the photograph transmitted by the transmission unit. The determination unit determines the health condition based on the information analyzed by the analysis unit. The notification unit notifies of an abnormality determined by the determination unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to detect a pet's health condition early from its excrement.

[0005] The system according to the embodiment aims to analyze pet waste and detect health conditions at an early stage. [Means for solving the problem]

[0006] The system according to the embodiment includes a transmission unit, an analysis unit, a determination unit, and a notification unit. The transmission unit transmits a photograph of the excrement. The analysis unit analyzes the photograph transmitted by the transmission unit. The determination unit determines the health condition based on the information analyzed by the analysis unit. The notification unit notifies the user of an abnormality determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze pet waste and detect health conditions at an early stage. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The pet health monitoring system according to an embodiment of the present invention utilizes AI to analyze and monitor pet waste (primarily dogs and cats) after photos of the waste are submitted. The system aims to maintain the pet's health at a pre-disease stage and detect any abnormalities early. Specifically, it consists of the following steps: First, the owner takes a photo of the pet's waste and sends it to the system. Next, the AI ​​analyzes the photo and evaluates the color, shape, texture, etc. of the waste. Based on this information, the AI ​​determines the pet's health and notifies the owner if any abnormalities are detected. Notifications are sent via a smartphone app. In the future, this system is expected to be applied to monitoring infant waste. For example, a function to learn the characteristics of infant waste and determine the pet's health status could be added. This allows the pet health monitoring system to maintain the pet's health at a pre-disease stage and detect any abnormalities early.

[0029] A pet health monitoring system according to an embodiment includes a transmission unit, an analysis unit, a determination unit, and a notification unit. The transmission unit allows a pet owner to take a photo of the pet's excrement and transmit it to the system. For example, the transmission unit can take a photo of the excrement using a smartphone camera and transmit it to the system via a dedicated app. The transmission unit can also automatically adjust the resolution and shooting angle of the photo and transmit it in a format suitable for analysis. The analysis unit analyzes the photo transmitted by the transmission unit. For example, the analysis unit can evaluate the color, shape, texture, etc. of the excrement using an image analysis algorithm. The analysis unit can extract characteristics of the excrement using AI and generate data for determining the pet's health condition. The determination unit determines the health condition based on the information analyzed by the analysis unit. For example, the determination unit evaluates the pet's health condition based on changes in the color and shape of the excrement. The determination unit uses AI to determine whether there is an abnormality and notifies the pet owner as necessary. The notification unit notifies the pet owner of the abnormality determined by the determination unit. For example, the notification unit can notify the owner of the details of the abnormality and how to deal with it via a smartphone app. The notification unit can also automatically adjust the timing and content of the notification so that the owner can respond quickly. As a result, the pet health monitoring system according to the embodiment can analyze the pet's excrement, determine the health condition, and detect abnormalities early.

[0030] The analysis unit may include an evaluation unit that evaluates the color or shape of the excrement. The evaluation unit may use, for example, a color analysis algorithm to evaluate the color of the excrement. For example, the evaluation unit may convert the color of the excrement into RGB values ​​or HSV values ​​and compare them with a normal range to detect abnormalities. The evaluation unit may also use a shape analysis algorithm to evaluate the shape of the excrement. For example, the evaluation unit may extract the outline of the excrement and detect abnormalities based on the shape. Furthermore, the evaluation unit may use a texture analysis algorithm to evaluate the texture of the excrement. For example, the evaluation unit may analyze the roughness or smoothness of the surface of the excrement to detect abnormalities. This allows the evaluation unit to evaluate the color and shape of the excrement and determine the health condition in more detail.

[0031] The notification unit may include an app notification unit that notifies the owner via a smartphone app. The app notification unit may notify the owner of the abnormality and how to deal with it, for example, via the smartphone app. For example, the app notification unit may notify the owner of the abnormality in real time using a push notification. The app notification unit may also customize the content of the notification. For example, the app notification unit may adjust the frequency and detail of notifications according to the owner's settings. Furthermore, the app notification unit may save a notification history and refer to the content of past notifications. For example, the app notification unit may record what actions the owner took based on the past notification history and reflect this in the next notification. In this way, the app notification unit may notify the owner via the smartphone app, allowing the owner to quickly check for abnormalities.

[0032] The analysis unit may include a learning unit that learns the characteristics of infant excrement. The learning unit may use, for example, a machine learning algorithm to learn the characteristics of infant excrement. For example, the learning unit may collect data such as the color, shape, and texture of infant excrement and build a model for determining a health condition based on that data. The learning unit may also use data augmentation technology to learn the characteristics of infant excrement. For example, the learning unit may generate new data by transforming or rotating existing data to increase the diversity of the training data. Furthermore, the learning unit may use transfer learning technology to learn the characteristics of infant excrement. For example, the learning unit may add new data based on an existing model to update the model and improve learning efficiency. In this way, the learning unit may learn the characteristics of infant excrement and apply it to determining the health condition of infants.

[0033] The assessment unit may include an infant assessment unit that assesses the health condition of an infant. The infant assessment unit may use an algorithm for assessing the health condition based on, for example, the characteristics of the infant's excrement. For example, the infant assessment unit may evaluate the health condition of an infant based on changes in the color or shape of the excrement. The infant assessment unit may also assess the presence or absence of an abnormality based on the characteristics of the infant's excrement. For example, the infant assessment unit may detect an abnormality when the color of the excrement is different from normal or when the shape is abnormal. Furthermore, the infant assessment unit may predict changes in the health condition based on the characteristics of the infant's excrement. For example, the infant assessment unit may predict future changes in the health condition based on past data and take early measures. In this way, the infant assessment unit may also be useful for managing the health of infants by assessing the health condition of infants.

[0034] The sending unit can analyze the owner's past sending history and select an appropriate sending method. The sending unit can use, for example, a data analysis algorithm to analyze the owner's past sending history. For example, the sending unit can prioritize and suggest sending methods (email, app, etc.) that the owner has frequently used based on the past sending history. The sending unit can also suggest a method for sending during a specific time period based on the past sending history. For example, the sending unit can select a method for sending during a specific time period based on the owner's past history of sending photos during that time period. Furthermore, the sending unit can analyze the past sending history and select the most efficient sending method. For example, the sending unit can evaluate the success rate and sending time based on the past sending history and select the optimal sending method. In this way, the sending unit can select the optimal sending method by analyzing the past sending history.

[0035] The transmitting unit can filter photos based on the pet's current health condition and living environment when transmitting the photos. The transmitting unit can use a data filtering algorithm to filter photos to be transmitted based on the pet's current health condition and living environment, for example. For example, the transmitting unit uses a normal transmission method when the pet's health condition is good. The transmitting unit can also use a transmission method including detailed information when the pet's health condition is unstable. For example, when the pet's health condition is deteriorating, the transmitting unit transmits photos including detailed analysis results and countermeasures. Furthermore, the transmitting unit can adjust the transmission method when the pet's living environment changes, taking that information into account. For example, when the pet moves to a new environment, the transmitting unit selects a transmission method appropriate for that environment. In this way, the transmitting unit can transmit appropriate information by filtering based on the pet's health condition and living environment.

[0036] When transmitting photos, the transmission unit can prioritize transmitting highly relevant photos by taking into account the owner's geographical location information. The transmission unit can, for example, use a location information analysis algorithm to consider the owner's geographical location information. For example, when the owner is at home, the transmission unit can prioritize transmitting everyday photos. Furthermore, when the owner is traveling, the transmission unit can prioritize transmitting important photos related to the pet's health condition. Furthermore, when the owner is at work, the transmission unit can prioritize transmitting photos that can be viewed in a short time. In this way, the transmission unit can prioritize transmitting highly relevant photos by taking into account the owner's geographical location information.

[0037] When sending photos, the sending unit can analyze the owner's social media activity and send relevant photos. The sending unit can use, for example, a data analysis algorithm to analyze the owner's social media activity. For example, if the owner frequently posts photos of their pet on social media, the sending unit can use AI to send photos that match that tendency. Also, if the owner is participating in a specific event, the sending unit can send photos related to that event. Furthermore, if the owner shares health information on social media, the sending unit can prioritize sending photos related to the owner's health. This allows the sending unit to send relevant photos by analyzing the owner's social media activity.

[0038] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the excrement. The analysis unit can use, for example, an importance evaluation algorithm to evaluate the importance of the excrement. For example, the analysis unit performs a detailed analysis when the color of the excrement is abnormal. The analysis unit can also perform a detailed analysis when the shape of the excrement is abnormal. Furthermore, the analysis unit can also perform a detailed analysis when the texture of the excrement is abnormal. In this way, the analysis unit can appropriately provide necessary information by adjusting the level of detail of the analysis based on the importance of the excrement.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of excrement. For example, the analysis unit can use a category identification algorithm to identify the category of excrement. For example, the analysis unit can apply a shape analysis algorithm to solid excrement. The analysis unit can also apply a color analysis algorithm to liquid excrement. Furthermore, the analysis unit can apply a composite analysis algorithm to mixed excrement. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of excrement.

[0040] During analysis, the analysis unit can determine the priority of analysis based on the time when the excrement was photographed. The analysis unit can use, for example, a time evaluation algorithm to evaluate the time when the excrement was photographed. For example, the analysis unit prioritizes analyzing excrement photographed recently. The analysis unit can also prioritize analyzing excrement photographed during a specific time period. Furthermore, the analysis unit can prioritize analyzing excrement photographed during a time period specified by the owner. In this way, the analysis unit can prioritize analyzing the most recent information by determining the priority of analysis based on the time when the excrement was photographed.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the excrement. The analysis unit can use, for example, a relevance evaluation algorithm to evaluate the relevance of the excrement. For example, the analysis unit can analyze excrement from the same pet consecutively. The analysis unit can also analyze excrement from different pets alternately. Furthermore, the analysis unit can analyze the excrement in an order specified by the owner. This allows the analysis unit to adjust the order of analysis based on the relevance of the excrement, thereby enabling efficient analysis.

[0042] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between the excrements. The determination unit can use, for example, a correlation evaluation algorithm to evaluate the interrelationships between the excrements. For example, the determination unit makes the determination by referring to past excrement data of the same pet. The determination unit can also make the determination by comparing excrement data of different pets. Furthermore, the determination unit can make the determination by taking into account additional information provided by the owner. In this way, the determination unit can improve the accuracy of the determination by taking into account the interrelationships between the excrements, thereby enabling more accurate determination.

[0043] When making a judgment, the judgment unit can make a judgment taking into account attribute information of the person who submitted the excrement. The judgment unit can use, for example, an attribute information evaluation algorithm to evaluate the attribute information of the person who submitted the excrement. For example, if the person who submitted the excrement is a pet owner, the judgment unit makes a judgment by referring to the owner's past data. Furthermore, if the person who submitted the excrement is a veterinarian, the judgment unit can also make a judgment taking into account professional knowledge. Furthermore, if the person who submitted the excrement is a third party, the judgment unit can make a judgment based on general standards. This allows the judgment unit to make a more appropriate judgment by making a judgment taking into account the attribute information of the person who submitted the excrement.

[0044] The determination unit can make a determination taking into account the geographical distribution of excrement. The determination unit can use, for example, a geographical distribution evaluation algorithm to evaluate the geographical distribution of excrement. For example, the determination unit makes a determination by referring to excrement data from the same region. The determination unit can also make a determination by comparing excrement data from different regions. Furthermore, the determination unit can make a determination taking into account geographical factors. In this way, the determination unit can make a determination that reflects region-specific factors by making a determination taking into account the geographical distribution of excrement.

[0045] The determination unit can improve the accuracy of the determination by referring to literature related to excrement during the determination. The determination unit can use a literature reference algorithm to refer to literature related to excrement, for example. For example, the determination unit makes the determination by referring to the latest research papers. The determination unit can also make the determination by referring to past research data. Furthermore, the determination unit can make the determination by referring to specialized books. In this way, the determination unit can improve the accuracy of the determination by referring to literature related to excrement.

[0046] The notification unit can optimally adjust the current notification by referring to past notification data when notifying. The notification unit can use, for example, a data analysis algorithm to refer to the past notification data. For example, the notification unit analyzes the past notification data and selects the optimal notification method. The notification unit can also adjust the timing of the notification by referring to the past notification data. Furthermore, the notification unit can optimize the content of the notification based on the past notification data. As a result, the notification unit can optimize the current notification by referring to the past notification data.

[0047] The notification unit can apply different notification methods for each category of excrement when making a notification. The notification unit can use, for example, a category identification algorithm to identify the category of excrement. For example, the notification unit can provide a notification including detailed information for solid excrement. The notification unit can also provide a notification including concise information for liquid excrement. Furthermore, the notification unit can provide a notification including complex information for mixed excrement. In this way, the notification unit can provide appropriate information by applying different notification methods for each category of excrement.

[0048] At the time of notification, the notification unit can analyze changes in the notification based on the time when the excrement was photographed. The notification unit can use, for example, a timing evaluation algorithm to evaluate the time when the excrement was photographed. For example, the notification unit can prioritize displaying notifications related to excrement photographed recently. The notification unit can also analyze notifications related to excrement photographed during a specific time period and provide notifications at an appropriate time. Furthermore, the notification unit can prioritize displaying notifications related to excrement photographed during a time period specified by the owner. In this way, the notification unit can provide notifications at an appropriate time by analyzing changes in the notification based on the time when the excrement was photographed.

[0049] The notification unit can analyze the notification by referring to market data related to excrement when issuing a notification. For example, the notification unit can use a market data analysis algorithm to refer to the market data related to excrement. For example, the notification unit can optimize notifications regarding pet health conditions based on the market data. The notification unit can also adjust the content of the notification by referring to the market data. Furthermore, the notification unit can optimize the timing of the notification based on the market data. In this way, the notification unit can optimize the content and timing of the notification by referring to the market data related to excrement.

[0050] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the excrements during the evaluation. The evaluation unit can use, for example, a correlation evaluation algorithm to evaluate the interrelationships between the excrements. For example, the evaluation unit can make the evaluation by referring to past excrement data of the same pet. The evaluation unit can also make the evaluation by comparing excrement data of different pets. Furthermore, the evaluation unit can make the evaluation by taking into account additional information provided by the owner. In this way, the evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the excrements, thereby enabling a more accurate evaluation.

[0051] When making an evaluation, the evaluation unit can take into account attribute information of the person who submitted the excrement. The evaluation unit can use, for example, an attribute information evaluation algorithm to evaluate the attribute information of the person who submitted the excrement. For example, if the person who submitted the excrement is a pet owner, the evaluation unit can make the evaluation by referring to the owner's past data. Furthermore, if the person who submitted the excrement is a veterinarian, the evaluation unit can also make the evaluation by taking into account professional knowledge. Furthermore, if the person who submitted the excrement is a third party, the evaluation unit can make the evaluation based on general standards. This allows the evaluation unit to make a more appropriate evaluation by taking into account the attribute information of the person who submitted the excrement.

[0052] The evaluation unit can perform the evaluation taking into account the geographical distribution of excrement. The evaluation unit can use, for example, a geographical distribution evaluation algorithm to evaluate the geographical distribution of excrement. For example, the evaluation unit performs the evaluation by referring to excrement data from the same region. The evaluation unit can also perform the evaluation by comparing excrement data from different regions. Furthermore, the evaluation unit can also perform the evaluation taking into account geographical factors. In this way, the evaluation unit can perform the evaluation taking into account the geographical distribution of excrement, thereby enabling an evaluation that reflects factors specific to the region.

[0053] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to excrement during the evaluation. The evaluation unit can use a literature reference algorithm to refer to literature related to excrement, for example. For example, the evaluation unit performs the evaluation by referring to the latest research papers. The evaluation unit can also perform the evaluation by referring to past research data. Furthermore, the evaluation unit can perform the evaluation by referring to specialized books. In this way, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to excrement.

[0054] When an app notification is provided, the app notification unit can optimize the current notification by referring to past notification data. The app notification unit can use a data analysis algorithm to refer to the past notification data. For example, the app notification unit can analyze the past notification data and select the optimal notification method. The app notification unit can also adjust the timing of the notification by referring to the past notification data. Furthermore, the app notification unit can optimize the content of the notification based on the past notification data. This allows the app notification unit to optimize the current notification by referring to the past notification data.

[0055] The app notification unit can apply different notification methods for each excrement category when providing an app notification. The app notification unit can use, for example, a category identification algorithm to identify the excrement category. For example, the app notification unit can provide a notification including detailed information for solid excrement. The app notification unit can also provide a notification including concise information for liquid excrement. Furthermore, the app notification unit can provide a notification including complex information for mixed excrement. In this way, the app notification unit can provide appropriate information by applying different notification methods for each excrement category.

[0056] The app notification unit can analyze changes in notifications based on the time when the excrement was photographed when the app notification was provided. The app notification unit can use, for example, a timing evaluation algorithm to evaluate the time when the excrement was photographed. For example, the app notification unit can prioritize displaying notifications related to recently photographed excrement. The app notification unit can also analyze notifications related to excrement photographed during a specific time period and provide notifications at an appropriate time. Furthermore, the app notification unit can prioritize displaying notifications related to excrement photographed during a time period specified by the owner. In this way, the app notification unit can provide notifications at an appropriate time by analyzing changes in notifications based on the time when the excrement was photographed.

[0057] The app notification unit can analyze the notification by referring to market data related to excrement when providing the app notification. For example, the app notification unit can use a market data analysis algorithm to refer to the market data related to excrement. For example, the app notification unit can optimize notifications related to pet health conditions based on the market data. The app notification unit can also adjust the content of the notification by referring to the market data. Furthermore, the app notification unit can optimize the timing of the notification based on the market data. As a result, the app notification unit can optimize the content and timing of the notification by referring to the market data related to excrement.

[0058] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can use a data analysis algorithm to refer to past learning data, for example. For example, the learning unit analyzes past learning data and selects an optimal learning algorithm. The learning unit can also improve learning efficiency by referring to past learning data. Furthermore, the learning unit can improve learning accuracy based on past learning data. This allows the learning unit to optimize the learning algorithm by referring to past learning data.

[0059] During learning, the learning unit can weight the learning data based on the time when the excrement was photographed. The learning unit can use, for example, a time evaluation algorithm to evaluate the time when the excrement was photographed. For example, the learning unit can prioritize data of excrement photographed recently during learning. The learning unit can also prioritize data of excrement photographed during a specific time period during learning. Furthermore, the learning unit can also prioritize data of excrement photographed during a time period specified by the owner during learning. In this way, by weighting the learning data based on the time when the excrement was photographed, the learning unit can prioritize important data during learning.

[0060] The infant determination unit can improve the accuracy of the determination by taking into account the interrelationships between excrement when determining whether an infant is an infant. The infant determination unit can use, for example, an interrelationship evaluation algorithm to evaluate the interrelationships between excrement. For example, the infant determination unit makes the determination by referring to past excrement data of the same infant. The infant determination unit can also make the determination by comparing excrement data of different infants. Furthermore, the infant determination unit can make the determination by taking into account additional information provided by the owner. In this way, the infant determination unit can improve the accuracy of the determination by taking into account the interrelationships between excrement, thereby enabling more accurate determination.

[0061] When determining whether an excrement is an infant, the infant determination unit can make a determination taking into account attribute information of the person who submitted the excrement. The infant determination unit can use, for example, an attribute information evaluation algorithm to evaluate the attribute information of the person who submitted the excrement. For example, if the submitter is a parent, the infant determination unit makes a determination by referring to the parent's past data. Furthermore, if the submitter is a doctor, the infant determination unit can make a determination taking into account professional knowledge. Furthermore, if the submitter is a third party, the infant determination unit can make a determination based on general standards. This allows the infant determination unit to make a more appropriate determination by taking into account the attribute information of the submitter.

[0062] The infant determination unit can make a determination taking into account the geographical distribution of excrement when determining whether the infant is an infant. The infant determination unit can use, for example, a geographical distribution evaluation algorithm to evaluate the geographical distribution of excrement. For example, the infant determination unit makes a determination by referring to excrement data from the same region. The infant determination unit can also make a determination by comparing excrement data from different regions. Furthermore, the infant determination unit can make a determination taking into account geographical factors. In this way, the infant determination unit can make a determination that reflects region-specific factors by making a determination taking into account the geographical distribution of excrement.

[0063] The infant determination unit can improve the accuracy of the determination by referring to literature related to excrement when determining whether the infant is an infant. The infant determination unit can use a literature reference algorithm to refer to literature related to excrement, for example. For example, the infant determination unit makes the determination by referring to the latest research papers. The infant determination unit can also make the determination by referring to past research data. Furthermore, the infant determination unit can make the determination by referring to specialized books. In this way, the infant determination unit can improve the accuracy of the determination by referring to literature related to excrement.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] When sending photos of pet excrement, the sending unit can analyze the owner's past sending history and select the optimal sending method. For example, the sending unit can prioritize and suggest sending methods (email, app, etc.) that the owner has frequently used based on the past sending history. The sending unit can also suggest a method for sending during a specific time period based on the past sending history. Furthermore, the sending unit can analyze the past sending history and select the most efficient sending method. In this way, the sending unit can select the optimal sending method by analyzing the past sending history.

[0066] The analysis unit may include an odor evaluation unit that evaluates the odor of excrement in addition to evaluating the color and shape of the excrement. The odor evaluation unit, for example, uses an odor sensor to detect the odor of excrement and analyzes the data. The odor evaluation unit can generate data for determining the health condition based on the intensity and type of odor. For example, the odor evaluation unit can detect odors associated with specific diseases and detect abnormalities early. The odor evaluation unit can also track changes in odor and monitor changes in the health condition. This allows the odor evaluation unit to evaluate the odor of excrement and determine the health condition in more detail.

[0067] The analysis unit may include a learning unit that learns the characteristics of an infant's excrement, as well as a diet learning unit that learns the infant's dietary content. The diet learning unit, for example, records the infant's dietary content and learns the characteristics of the excrement based on that data. The diet learning unit can analyze the relationship between the dietary content and the excrement and build a model for assessing the health condition. For example, the diet learning unit can learn the effect of specific ingredients on the excrement and detect abnormalities early. The diet learning unit can also predict the characteristics of the excrement according to changes in the dietary content and monitor changes in the health condition. In this way, the diet learning unit can learn the infant's dietary content and more accurately assess the health condition.

[0068] When transmitting photos of pet excrement, the transmitting unit can filter based on the pet's current health condition and living environment. For example, if the pet's health condition is good, the transmitting unit uses a normal transmission method. Furthermore, if the pet's health condition is unstable, the transmitting unit can use a transmission method including detailed information. For example, if the pet's health condition is deteriorating, the transmitting unit transmits photos including detailed analysis results and countermeasures. Furthermore, if the pet's living environment changes, the transmitting unit can adjust the transmission method taking that information into consideration. For example, if the pet moves to a new environment, the transmitting unit selects a transmission method appropriate for that environment. In this way, the transmitting unit can transmit appropriate information by filtering based on the pet's health condition and living environment.

[0069] The analysis unit may include a sound evaluation unit that evaluates the sound of excrement in addition to evaluating the color and shape of the excrement. The sound evaluation unit, for example, detects the sound of excrement using a sound sensor and analyzes the data. The sound evaluation unit can generate data for determining the health condition based on the intensity and type of sound. For example, the sound evaluation unit can detect sounds related to specific diseases and detect abnormalities early. The sound evaluation unit can also track changes in sound and monitor changes in the health condition. This allows the sound evaluation unit to evaluate the sound of excrement and determine the health condition in more detail.

[0070] When sending notifications through a smartphone app, the notification unit can prioritize sending highly relevant photos by taking into account the owner's geographical location information. For example, if the owner is at home, the notification unit can prioritize sending everyday photos. Also, if the owner is traveling, the notification unit can prioritize sending important photos related to the pet's health condition. Furthermore, if the owner is at work, the notification unit can prioritize sending photos that can be viewed in a short time. In this way, the notification unit can prioritize sending highly relevant photos by taking into account the owner's geographical location information.

[0071] The analysis unit may include a temperature evaluation unit that evaluates the temperature of the excrement in addition to evaluating the color and shape of the excrement. The temperature evaluation unit, for example, detects the temperature of the excrement using a temperature sensor and analyzes the data. The temperature evaluation unit can generate data for determining the health condition based on temperature changes or abnormalities. For example, the temperature evaluation unit can detect temperature changes associated with specific diseases and detect abnormalities early. The temperature evaluation unit can also track temperature changes and monitor changes in the health condition. This allows the temperature evaluation unit to evaluate the temperature of the excrement and determine the health condition in more detail.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The owner takes a photo of their pet's excrement and sends it to the system. For example, the transmitter can take a photo of the excrement using a smartphone camera and send it to the system via a dedicated app. The transmitter can also automatically adjust the resolution and angle of the photo and send it in a format suitable for analysis. Step 2: The analysis unit analyzes the photo sent by the transmission unit. For example, the analysis unit uses an image analysis algorithm to evaluate the color, shape, texture, etc. of the excrement. The analysis unit can use AI to extract characteristics of the excrement and generate data to determine the health status. Step 3: The judgment unit judges the pet's health condition based on the information analyzed by the analysis unit. For example, the judgment unit evaluates the pet's health condition based on changes in the color and shape of the excrement. The judgment unit uses AI to determine whether there are any abnormalities and notify the owner if necessary. Step 4: The notification unit notifies the owner of the abnormality determined by the determination unit. For example, the notification unit can notify the owner of the nature of the abnormality and how to deal with it via a smartphone app. The notification unit can also automatically adjust the timing and content of the notification so that the owner can respond promptly.

[0074] (Example 2) The pet health monitoring system according to an embodiment of the present invention utilizes AI to analyze and monitor pet feces (mainly dogs and cats) using photos of the excrement submitted. This system aims to maintain the pet's health at a pre-disease stage and detect any abnormalities early. Specifically, it consists of the following steps: First, the owner takes a photo of the pet's excrement and sends it to the system. Next, the AI ​​analyzes the photo and evaluates the color, shape, texture, etc. of the excrement. Based on this information, the AI ​​determines the pet's health and notifies the owner if any abnormalities are detected. Notifications are sent via a smartphone app. In the future, this system is expected to be applied to monitoring infant excrement as well. For example, a function to learn the characteristics of infant excrement and determine the pet's health will be added. This allows the pet health monitoring system to maintain the pet's health at a pre-disease stage and detect any abnormalities early.

[0075] A pet health monitoring system according to an embodiment includes a transmission unit, an analysis unit, a determination unit, and a notification unit. The transmission unit allows a pet owner to take a photo of the pet's excrement and transmit it to the system. For example, the transmission unit can take a photo of the excrement using a smartphone camera and transmit it to the system via a dedicated app. The transmission unit can also automatically adjust the resolution and shooting angle of the photo and transmit it in a format suitable for analysis. The analysis unit analyzes the photo transmitted by the transmission unit. For example, the analysis unit can evaluate the color, shape, texture, etc. of the excrement using an image analysis algorithm. The analysis unit can extract characteristics of the excrement using AI and generate data for determining the pet's health condition. The determination unit determines the health condition based on the information analyzed by the analysis unit. For example, the determination unit evaluates the pet's health condition based on changes in the color and shape of the excrement. The determination unit uses AI to determine whether there is an abnormality and notifies the pet owner as necessary. The notification unit notifies the pet owner of the abnormality determined by the determination unit. For example, the notification unit can notify the owner of the details of the abnormality and how to deal with it via a smartphone app. The notification unit can also automatically adjust the timing and content of the notification so that the owner can respond quickly. As a result, the pet health monitoring system according to the embodiment can analyze the pet's excrement, determine the health condition, and detect abnormalities early.

[0076] The analysis unit may include an evaluation unit that evaluates the color or shape of the excrement. The evaluation unit may use, for example, a color analysis algorithm to evaluate the color of the excrement. For example, the evaluation unit may convert the color of the excrement into RGB values ​​or HSV values ​​and compare them with a normal range to detect abnormalities. The evaluation unit may also use a shape analysis algorithm to evaluate the shape of the excrement. For example, the evaluation unit may extract the outline of the excrement and detect abnormalities based on the shape. Furthermore, the evaluation unit may use a texture analysis algorithm to evaluate the texture of the excrement. For example, the evaluation unit may analyze the roughness or smoothness of the surface of the excrement to detect abnormalities. This allows the evaluation unit to evaluate the color and shape of the excrement and determine the health condition in more detail.

[0077] The notification unit may include an app notification unit that notifies the owner via a smartphone app. The app notification unit may notify the owner of the abnormality and how to deal with it, for example, via the smartphone app. For example, the app notification unit may notify the owner of the abnormality in real time using a push notification. The app notification unit may also customize the content of the notification. For example, the app notification unit may adjust the frequency and detail of notifications according to the owner's settings. Furthermore, the app notification unit may save a notification history and refer to the content of past notifications. For example, the app notification unit may record what actions the owner took based on the past notification history and reflect this in the next notification. In this way, the app notification unit may notify the owner via the smartphone app, allowing the owner to quickly check for abnormalities.

[0078] The analysis unit may include a learning unit that learns the characteristics of infant excrement. The learning unit may use, for example, a machine learning algorithm to learn the characteristics of infant excrement. For example, the learning unit may collect data such as the color, shape, and texture of infant excrement and build a model for determining a health condition based on that data. The learning unit may also use data augmentation technology to learn the characteristics of infant excrement. For example, the learning unit may generate new data by transforming or rotating existing data to increase the diversity of the training data. Furthermore, the learning unit may use transfer learning technology to learn the characteristics of infant excrement. For example, the learning unit may add new data based on an existing model to update the model and improve learning efficiency. In this way, the learning unit may learn the characteristics of infant excrement and apply it to determining the health condition of infants.

[0079] The assessment unit may include an infant assessment unit that assesses the health condition of an infant. The infant assessment unit may use an algorithm for assessing the health condition based on, for example, the characteristics of the infant's excrement. For example, the infant assessment unit may evaluate the health condition of an infant based on changes in the color or shape of the excrement. The infant assessment unit may also assess the presence or absence of an abnormality based on the characteristics of the infant's excrement. For example, the infant assessment unit may detect an abnormality when the color of the excrement is different from normal or when the shape is abnormal. Furthermore, the infant assessment unit may predict changes in the health condition based on the characteristics of the infant's excrement. For example, the infant assessment unit may predict future changes in the health condition based on past data and take early measures. In this way, the infant assessment unit may also be useful for managing the health of infants by assessing the health condition of infants.

[0080] The sending unit can estimate the owner's emotions and adjust the timing of sending photos based on the estimated owner's emotions. The sending unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the sending unit can analyze the owner's facial expressions and voice to estimate the emotions. The sending unit can also adjust the timing of sending photos based on the owner's emotions. For example, if the owner is feeling stressed, the sending unit can automatically send photos using AI, thereby reducing the owner's burden. Furthermore, if the owner is relaxed, the sending unit can prompt the owner to manually send photos, thereby increasing the owner's engagement. Furthermore, if the owner is busy, the sending unit can select the optimal timing of sending photos and send them according to the owner's schedule. In this way, the sending unit can reduce the owner's burden by adjusting the timing of sending photos according to the owner's emotions.

[0081] The sending unit can analyze the owner's past sending history and select an appropriate sending method. The sending unit can use, for example, a data analysis algorithm to analyze the owner's past sending history. For example, the sending unit can prioritize and suggest sending methods (email, app, etc.) that the owner has frequently used based on the past sending history. The sending unit can also suggest a method for sending during a specific time period based on the past sending history. For example, the sending unit can select a method for sending during a specific time period based on the owner's past history of sending photos during that time period. Furthermore, the sending unit can analyze the past sending history and select the most efficient sending method. For example, the sending unit can evaluate the success rate and sending time based on the past sending history and select the optimal sending method. In this way, the sending unit can select the optimal sending method by analyzing the past sending history.

[0082] The transmitting unit can filter photos based on the pet's current health condition and living environment when transmitting the photos. The transmitting unit can use a data filtering algorithm to filter photos to be transmitted based on the pet's current health condition and living environment, for example. For example, the transmitting unit uses a normal transmission method when the pet's health condition is good. The transmitting unit can also use a transmission method including detailed information when the pet's health condition is unstable. For example, when the pet's health condition is deteriorating, the transmitting unit transmits photos including detailed analysis results and countermeasures. Furthermore, the transmitting unit can adjust the transmission method when the pet's living environment changes, taking that information into account. For example, when the pet moves to a new environment, the transmitting unit selects a transmission method appropriate for that environment. In this way, the transmitting unit can transmit appropriate information by filtering based on the pet's health condition and living environment.

[0083] The transmitting unit can estimate the owner's emotions and determine the priority of photos to be sent based on the estimated owner's emotions. The transmitting unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the transmitting unit can analyze the owner's facial expressions and voice to estimate emotions. The transmitting unit can also determine the priority of photos to be sent based on the owner's emotions. For example, if the owner is feeling anxious, the transmitting unit can have the AI ​​prioritize sending important photos related to the owner's health. Also, if the owner is relaxed, the transmitting unit can have the AI ​​prioritize sending everyday photos. Furthermore, if the owner is busy, the transmitting unit can have the AI ​​select and send the most important photos. In this way, the transmitting unit can prioritize the transmission of important information by determining the priority of photos to be sent based on the owner's emotions.

[0084] When transmitting photos, the transmission unit can prioritize transmitting highly relevant photos by taking into account the owner's geographical location information. The transmission unit can, for example, use a location information analysis algorithm to consider the owner's geographical location information. For example, when the owner is at home, the transmission unit can prioritize transmitting everyday photos. Furthermore, when the owner is traveling, the transmission unit can prioritize transmitting important photos related to the pet's health condition. Furthermore, when the owner is at work, the transmission unit can prioritize transmitting photos that can be viewed in a short time. In this way, the transmission unit can prioritize transmitting highly relevant photos by taking into account the owner's geographical location information.

[0085] When sending photos, the sending unit can analyze the owner's social media activity and send relevant photos. The sending unit can use, for example, a data analysis algorithm to analyze the owner's social media activity. For example, if the owner frequently posts photos of their pet on social media, the sending unit can use AI to send photos that match that tendency. Also, if the owner is participating in a specific event, the sending unit can send photos related to that event. Furthermore, if the owner shares health information on social media, the sending unit can prioritize sending photos related to the owner's health. This allows the sending unit to send relevant photos by analyzing the owner's social media activity.

[0086] The analysis unit can estimate the owner's emotions and adjust the way the analysis is presented based on the estimated owner's emotions. The analysis unit can, for example, use an emotion estimation algorithm to estimate the owner's emotions. For example, the analysis unit can analyze the owner's facial expressions and voice to estimate the emotions. The analysis unit can also adjust the way the analysis is presented based on the owner's emotions. For example, the analysis unit can display the analysis results concisely and clearly if the owner is feeling anxious. The analysis unit can also provide detailed analysis results if the owner is relaxed. Furthermore, the analysis unit can display analysis results that are concise if the owner is busy. In this way, the analysis unit can provide analysis results that are easy for the owner to understand by adjusting the way the analysis is presented according to the owner's emotions.

[0087] During the analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the excrement. The analysis unit can use, for example, an importance evaluation algorithm to evaluate the importance of the excrement. For example, the analysis unit performs a detailed analysis when the color of the excrement is abnormal. The analysis unit can also perform a detailed analysis when the shape of the excrement is abnormal. Furthermore, the analysis unit can also perform a detailed analysis when the texture of the excrement is abnormal. In this way, the analysis unit can appropriately provide necessary information by adjusting the level of detail of the analysis based on the importance of the excrement.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the category of excrement. For example, the analysis unit can use a category identification algorithm to identify the category of excrement. For example, the analysis unit can apply a shape analysis algorithm to solid excrement. The analysis unit can also apply a color analysis algorithm to liquid excrement. Furthermore, the analysis unit can apply a composite analysis algorithm to mixed excrement. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of excrement.

[0089] The analysis unit can estimate the owner's emotions and adjust the length of the analysis based on the estimated owner's emotions. The analysis unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the analysis unit can analyze the owner's facial expressions and voice to estimate the emotions. The analysis unit can also adjust the length of the analysis based on the owner's emotions. For example, the analysis unit can provide a short and concise analysis result if the owner is feeling anxious. The analysis unit can also provide a detailed analysis result if the owner is relaxed. Furthermore, the analysis unit can provide a concise analysis result if the owner is busy. In this way, the analysis unit can adjust the length of the analysis according to the owner's emotions, thereby providing an appropriate amount of information for the owner.

[0090] During analysis, the analysis unit can determine the priority of analysis based on the time when the excrement was photographed. The analysis unit can use, for example, a time evaluation algorithm to evaluate the time when the excrement was photographed. For example, the analysis unit prioritizes analyzing excrement photographed recently. The analysis unit can also prioritize analyzing excrement photographed during a specific time period. Furthermore, the analysis unit can prioritize analyzing excrement photographed during a time period specified by the owner. In this way, the analysis unit can prioritize analyzing the most recent information by determining the priority of analysis based on the time when the excrement was photographed.

[0091] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the excrement. The analysis unit can use, for example, a relevance evaluation algorithm to evaluate the relevance of the excrement. For example, the analysis unit can analyze excrement from the same pet consecutively. The analysis unit can also analyze excrement from different pets alternately. Furthermore, the analysis unit can analyze the excrement in an order specified by the owner. This allows the analysis unit to adjust the order of analysis based on the relevance of the excrement, thereby enabling efficient analysis.

[0092] The determination unit can estimate the owner's emotions and adjust the determination criteria based on the estimated owner's emotions. The determination unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the determination unit can analyze the owner's facial expressions and voice to estimate the emotions. The determination unit can also adjust the determination criteria based on the owner's emotions. For example, the determination unit can make a determination based on strict criteria when the owner is feeling anxious. The determination unit can also make a determination based on normal criteria when the owner is relaxed. Furthermore, the determination unit can make a determination based on simple criteria when the owner is busy. In this way, the determination unit can provide an appropriate determination result for the owner by adjusting the determination criteria according to the owner's emotions.

[0093] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between the excrements. The determination unit can use, for example, a correlation evaluation algorithm to evaluate the interrelationships between the excrements. For example, the determination unit makes the determination by referring to past excrement data of the same pet. The determination unit can also make the determination by comparing excrement data of different pets. Furthermore, the determination unit can make the determination by taking into account additional information provided by the owner. In this way, the determination unit can improve the accuracy of the determination by taking into account the interrelationships between the excrements, thereby enabling more accurate determination.

[0094] When making a judgment, the judgment unit can make a judgment taking into account attribute information of the person who submitted the excrement. The judgment unit can use, for example, an attribute information evaluation algorithm to evaluate the attribute information of the person who submitted the excrement. For example, if the person who submitted the excrement is a pet owner, the judgment unit makes a judgment by referring to the owner's past data. Furthermore, if the person who submitted the excrement is a veterinarian, the judgment unit can also make a judgment taking into account professional knowledge. Furthermore, if the person who submitted the excrement is a third party, the judgment unit can make a judgment based on general standards. This allows the judgment unit to make a more appropriate judgment by making a judgment taking into account the attribute information of the person who submitted the excrement.

[0095] The determination unit can estimate the owner's emotions and adjust the order in which the determination results are displayed based on the estimated owner's emotions. The determination unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the determination unit can analyze the owner's facial expressions and voice to estimate the emotions. The determination unit can also adjust the order in which the determination results are displayed based on the owner's emotions. For example, the determination unit can display important results first if the owner is feeling anxious. The determination unit can also display detailed results sequentially if the owner is relaxed. Furthermore, the determination unit can display results that emphasize the main points first if the owner is busy. In this way, the determination unit can provide results that are easy for the owner to understand by adjusting the order in which the determination results are displayed according to the owner's emotions.

[0096] The determination unit can make a determination taking into account the geographical distribution of excrement. The determination unit can use, for example, a geographical distribution evaluation algorithm to evaluate the geographical distribution of excrement. For example, the determination unit makes a determination by referring to excrement data from the same region. The determination unit can also make a determination by comparing excrement data from different regions. Furthermore, the determination unit can make a determination taking into account geographical factors. In this way, the determination unit can make a determination that reflects region-specific factors by making a determination taking into account the geographical distribution of excrement.

[0097] The determination unit can improve the accuracy of the determination by referring to literature related to excrement during the determination. The determination unit can use a literature reference algorithm to refer to literature related to excrement, for example. For example, the determination unit makes the determination by referring to the latest research papers. The determination unit can also make the determination by referring to past research data. Furthermore, the determination unit can make the determination by referring to specialized books. In this way, the determination unit can improve the accuracy of the determination by referring to literature related to excrement.

[0098] The notification unit can estimate the owner's emotions and adjust the notification display method based on the estimated owner's emotions. The notification unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the notification unit can analyze the owner's facial expressions and voice to estimate the emotions. The notification unit can also adjust the notification display method based on the owner's emotions. For example, the notification unit can display a concise and clear notification if the owner is feeling anxious. The notification unit can also display a detailed notification if the owner is relaxed. Furthermore, the notification unit can display a notification that focuses on the main points if the owner is busy. In this way, the notification unit can provide notifications that are easy for the owner to understand by adjusting the notification display method according to the owner's emotions.

[0099] The notification unit can optimally adjust the current notification by referring to past notification data when notifying. The notification unit can use, for example, a data analysis algorithm to refer to the past notification data. For example, the notification unit analyzes the past notification data and selects the optimal notification method. The notification unit can also adjust the timing of the notification by referring to the past notification data. Furthermore, the notification unit can optimize the content of the notification based on the past notification data. As a result, the notification unit can optimize the current notification by referring to the past notification data.

[0100] The notification unit can apply different notification methods for each category of excrement when making a notification. The notification unit can use, for example, a category identification algorithm to identify the category of excrement. For example, the notification unit can provide a notification including detailed information for solid excrement. The notification unit can also provide a notification including concise information for liquid excrement. Furthermore, the notification unit can provide a notification including complex information for mixed excrement. In this way, the notification unit can provide appropriate information by applying different notification methods for each category of excrement.

[0101] The notification unit can estimate the owner's emotions and adjust the importance of notifications based on the estimated owner's emotions. The notification unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the notification unit can analyze the owner's facial expressions and voice to estimate the emotions. The notification unit can also adjust the importance of notifications based on the owner's emotions. For example, the notification unit can prioritize displaying important notifications when the owner is feeling anxious. The notification unit can also display regular notifications when the owner is relaxed. Furthermore, the notification unit can prioritize displaying notifications that focus on the main points when the owner is busy. In this way, the notification unit can prioritize providing important information by adjusting the importance of notifications according to the owner's emotions.

[0102] At the time of notification, the notification unit can analyze changes in the notification based on the time when the excrement was photographed. The notification unit can use, for example, a timing evaluation algorithm to evaluate the time when the excrement was photographed. For example, the notification unit can prioritize displaying notifications related to excrement photographed recently. The notification unit can also analyze notifications related to excrement photographed during a specific time period and provide notifications at an appropriate time. Furthermore, the notification unit can prioritize displaying notifications related to excrement photographed during a time period specified by the owner. In this way, the notification unit can provide notifications at an appropriate time by analyzing changes in the notification based on the time when the excrement was photographed.

[0103] The notification unit can analyze the notification by referring to market data related to excrement when issuing a notification. For example, the notification unit can use a market data analysis algorithm to refer to the market data related to excrement. For example, the notification unit can optimize notifications regarding pet health conditions based on the market data. The notification unit can also adjust the content of the notification by referring to the market data. Furthermore, the notification unit can optimize the timing of the notification based on the market data. In this way, the notification unit can optimize the content and timing of the notification by referring to the market data related to excrement.

[0104] The evaluation unit can estimate the owner's emotions and adjust the evaluation criteria based on the estimated owner's emotions. The evaluation unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the evaluation unit can analyze the owner's facial expressions and voice to estimate the emotions. The evaluation unit can also adjust the evaluation criteria based on the owner's emotions. For example, the evaluation unit can perform evaluation using strict criteria when the owner is feeling anxious. The evaluation unit can also perform evaluation using normal criteria when the owner is relaxed. Furthermore, the evaluation unit can also perform evaluation using simple criteria when the owner is busy. In this way, the evaluation unit can provide an appropriate evaluation result for the owner by adjusting the evaluation criteria according to the owner's emotions.

[0105] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the excrements during the evaluation. The evaluation unit can use, for example, a correlation evaluation algorithm to evaluate the interrelationships between the excrements. For example, the evaluation unit can make the evaluation by referring to past excrement data of the same pet. The evaluation unit can also make the evaluation by comparing excrement data of different pets. Furthermore, the evaluation unit can make the evaluation by taking into account additional information provided by the owner. In this way, the evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between the excrements, thereby enabling a more accurate evaluation.

[0106] When making an evaluation, the evaluation unit can take into account attribute information of the person who submitted the excrement. The evaluation unit can use, for example, an attribute information evaluation algorithm to evaluate the attribute information of the person who submitted the excrement. For example, if the person who submitted the excrement is a pet owner, the evaluation unit can make the evaluation by referring to the owner's past data. Furthermore, if the person who submitted the excrement is a veterinarian, the evaluation unit can also make the evaluation by taking into account professional knowledge. Furthermore, if the person who submitted the excrement is a third party, the evaluation unit can make the evaluation based on general standards. This allows the evaluation unit to make a more appropriate evaluation by taking into account the attribute information of the person who submitted the excrement.

[0107] The evaluation unit can estimate the owner's emotions and adjust the order in which the evaluation results are displayed based on the estimated owner's emotions. The evaluation unit can, for example, use an emotion estimation algorithm to estimate the owner's emotions. For example, the evaluation unit can analyze the owner's facial expressions and voice to estimate the emotions. The evaluation unit can also adjust the order in which the evaluation results are displayed based on the owner's emotions. For example, if the owner is feeling anxious, the evaluation unit can display important results first. Also, if the owner is relaxed, the evaluation unit can sequentially display detailed results. Furthermore, if the owner is busy, the evaluation unit can display results that highlight the main points first. In this way, the evaluation unit can provide results that are easy for the owner to understand by adjusting the order in which the evaluation results are displayed according to the owner's emotions.

[0108] The evaluation unit can perform the evaluation taking into account the geographical distribution of excrement. The evaluation unit can use, for example, a geographical distribution evaluation algorithm to evaluate the geographical distribution of excrement. For example, the evaluation unit performs the evaluation by referring to excrement data from the same region. The evaluation unit can also perform the evaluation by comparing excrement data from different regions. Furthermore, the evaluation unit can also perform the evaluation taking into account geographical factors. In this way, the evaluation unit can perform the evaluation taking into account the geographical distribution of excrement, thereby enabling an evaluation that reflects factors specific to the region.

[0109] The evaluation unit can improve the accuracy of the evaluation by referring to literature related to excrement during the evaluation. The evaluation unit can use a literature reference algorithm to refer to literature related to excrement, for example. For example, the evaluation unit performs the evaluation by referring to the latest research papers. The evaluation unit can also perform the evaluation by referring to past research data. Furthermore, the evaluation unit can perform the evaluation by referring to specialized books. In this way, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to excrement.

[0110] The app notification unit can estimate the owner's emotions and adjust the display method of the app notification based on the estimated owner's emotions. The app notification unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the app notification unit can analyze the owner's facial expressions and voice to estimate the emotion. The app notification unit can also adjust the display method of the app notification based on the owner's emotions. For example, the app notification unit can display a concise and clear notification if the owner is feeling anxious. The app notification unit can also display a detailed notification if the owner is relaxed. Furthermore, the app notification unit can display a notification that focuses on the main points if the owner is busy. In this way, the app notification unit can provide notifications that are easy for the owner to understand by adjusting the display method of the app notification according to the owner's emotions.

[0111] When an app notification is provided, the app notification unit can optimize the current notification by referring to past notification data. The app notification unit can use a data analysis algorithm to refer to the past notification data. For example, the app notification unit can analyze the past notification data and select the optimal notification method. The app notification unit can also adjust the timing of the notification by referring to the past notification data. Furthermore, the app notification unit can optimize the content of the notification based on the past notification data. This allows the app notification unit to optimize the current notification by referring to the past notification data.

[0112] The app notification unit can apply different notification methods for each excrement category when providing an app notification. The app notification unit can use, for example, a category identification algorithm to identify the excrement category. For example, the app notification unit can provide a notification including detailed information for solid excrement. The app notification unit can also provide a notification including concise information for liquid excrement. Furthermore, the app notification unit can provide a notification including complex information for mixed excrement. In this way, the app notification unit can provide appropriate information by applying different notification methods for each excrement category.

[0113] The app notification unit can estimate the owner's emotions and adjust the importance of app notifications based on the estimated owner's emotions. The app notification unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the app notification unit can analyze the owner's facial expressions and voice to estimate the emotions. The app notification unit can also adjust the importance of app notifications based on the owner's emotions. For example, the app notification unit can prioritize displaying important notifications when the owner is feeling anxious. The app notification unit can also display regular notifications when the owner is relaxed. Furthermore, the app notification unit can prioritize displaying notifications that focus on the main points when the owner is busy. In this way, the app notification unit can prioritize providing important information by adjusting the importance of app notifications according to the owner's emotions.

[0114] The app notification unit can analyze changes in notifications based on the time when the excrement was photographed when the app notification was provided. The app notification unit can use, for example, a timing evaluation algorithm to evaluate the time when the excrement was photographed. For example, the app notification unit can prioritize displaying notifications related to recently photographed excrement. The app notification unit can also analyze notifications related to excrement photographed during a specific time period and provide notifications at an appropriate time. Furthermore, the app notification unit can prioritize displaying notifications related to excrement photographed during a time period specified by the owner. In this way, the app notification unit can provide notifications at an appropriate time by analyzing changes in notifications based on the time when the excrement was photographed.

[0115] The app notification unit can analyze the notification by referring to market data related to excrement when providing the app notification. For example, the app notification unit can use a market data analysis algorithm to refer to the market data related to excrement. For example, the app notification unit can optimize notifications related to pet health conditions based on the market data. The app notification unit can also adjust the content of the notification by referring to the market data. Furthermore, the app notification unit can optimize the timing of the notification based on the market data. As a result, the app notification unit can optimize the content and timing of the notification by referring to the market data related to excrement.

[0116] The learning unit can estimate the owner's emotions and select learning data based on the estimated owner's emotions. The learning unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the learning unit can analyze the owner's facial expressions and voice to estimate the emotions. The learning unit can also select learning data based on the owner's emotions. For example, the learning unit can prioritize learning important data when the owner is feeling anxious. The learning unit can also learn detailed data when the owner is relaxed. Furthermore, the learning unit can learn data that focuses on the main points when the owner is busy. In this way, the learning unit can prioritize learning important data by selecting learning data according to the owner's emotions.

[0117] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can use a data analysis algorithm to refer to past learning data, for example. For example, the learning unit analyzes past learning data and selects an optimal learning algorithm. The learning unit can also improve learning efficiency by referring to past learning data. Furthermore, the learning unit can improve learning accuracy based on past learning data. This allows the learning unit to optimize the learning algorithm by referring to past learning data.

[0118] The learning unit can estimate the owner's emotions and adjust the frequency of learning based on the estimated owner's emotions. The learning unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the learning unit can analyze the owner's facial expressions and voice to estimate the emotions. The learning unit can also adjust the frequency of learning based on the owner's emotions. For example, the learning unit can perform learning more frequently if the owner is feeling anxious. The learning unit can also perform learning at a normal frequency if the owner is relaxed. Furthermore, the learning unit can reduce the frequency of learning if the owner is busy. In this way, the learning unit can perform learning at an appropriate frequency by adjusting the frequency of learning according to the owner's emotions.

[0119] During learning, the learning unit can weight the learning data based on the time when the excrement was photographed. The learning unit can use, for example, a time evaluation algorithm to evaluate the time when the excrement was photographed. For example, the learning unit can prioritize data of excrement photographed recently during learning. The learning unit can also prioritize data of excrement photographed during a specific time period during learning. Furthermore, the learning unit can also prioritize data of excrement photographed during a time period specified by the owner during learning. In this way, by weighting the learning data based on the time when the excrement was photographed, the learning unit can prioritize important data during learning.

[0120] The infant determination unit can estimate the owner's emotions and adjust the criteria for infant determination based on the estimated owner's emotions. The infant determination unit can use, for example, an emotion estimation algorithm to estimate the owner's emotions. For example, the infant determination unit can analyze the owner's facial expressions and voice to estimate the emotions. The infant determination unit can also adjust the criteria for infant determination based on the owner's emotions. For example, the infant determination unit can make a determination using strict criteria when the owner is anxious. The infant determination unit can also make a determination using normal criteria when the owner is relaxed. Furthermore, the infant determination unit can make a determination using simple criteria when the owner is busy. In this way, the infant determination unit can provide an appropriate determination result by adjusting the criteria for infant determination according to the owner's emotions.

[0121] The infant determination unit can improve the accuracy of the determination by taking into account the interrelationships between excrement when determining whether an infant is an infant. The infant determination unit can use, for example, an interrelationship evaluation algorithm to evaluate the interrelationships between excrement. For example, the infant determination unit makes the determination by referring to past excrement data of the same infant. The infant determination unit can also make the determination by comparing excrement data of different infants. Furthermore, the infant determination unit can make the determination by taking into account additional information provided by the owner. In this way, the infant determination unit can improve the accuracy of the determination by taking into account the interrelationships between excrement, thereby enabling more accurate determination.

[0122] When determining whether an excrement is an infant, the infant determination unit can make a determination taking into account attribute information of the person who submitted the excrement. The infant determination unit can use, for example, an attribute information evaluation algorithm to evaluate the attribute information of the person who submitted the excrement. For example, if the submitter is a parent, the infant determination unit makes a determination by referring to the parent's past data. Furthermore, if the submitter is a doctor, the infant determination unit can make a determination taking into account professional knowledge. Furthermore, if the submitter is a third party, the infant determination unit can make a determination based on general standards. This allows the infant determination unit to make a more appropriate determination by taking into account the attribute information of the submitter.

[0123] The infant determination unit can estimate the owner's emotions and adjust the order in which the infant determination results are displayed based on the estimated owner's emotions. The infant determination unit can use an emotion estimation algorithm to estimate the owner's emotions, for example. For example, the infant determination unit can analyze the owner's facial expressions and voice to estimate the emotions. The infant determination unit can also adjust the order in which the infant determination results are displayed based on the owner's emotions. For example, the infant determination unit can display important results first if the owner is feeling anxious. The infant determination unit can also sequentially display detailed results if the owner is relaxed. Furthermore, the infant determination unit can also display key results first if the owner is busy. In this way, the infant determination unit can provide results that are easy for the owner to understand by adjusting the order in which the infant determination results are displayed based on the owner's emotions.

[0124] The infant determination unit can make a determination taking into account the geographical distribution of excrement when determining whether the infant is an infant. The infant determination unit can use, for example, a geographical distribution evaluation algorithm to evaluate the geographical distribution of excrement. For example, the infant determination unit makes a determination by referring to excrement data from the same region. The infant determination unit can also make a determination by comparing excrement data from different regions. Furthermore, the infant determination unit can make a determination taking into account geographical factors. In this way, the infant determination unit can make a determination that reflects region-specific factors by making a determination taking into account the geographical distribution of excrement.

[0125] The infant determination unit can improve the accuracy of the determination by referring to literature related to excrement when determining whether the infant is an infant. The infant determination unit can use a literature reference algorithm to refer to literature related to excrement, for example. For example, the infant determination unit makes the determination by referring to the latest research papers. The infant determination unit can also make the determination by referring to past research data. Furthermore, the infant determination unit can make the determination by referring to specialized books. In this way, the infant determination unit can improve the accuracy of the determination by referring to literature related to excrement. === Hard Collateral 1-1 === Each of the multiple elements, including the transmission unit, analysis unit, determination unit, and notification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the transmission unit takes a photo of the excrement using the camera 42 of the smart device 14 and transmits the photo to the system via a dedicated app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the color, shape, texture, etc. of the excrement using an image analysis algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the health condition based on the analyzed information. The notification unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the owner of the nature of the abnormality and how to deal with it. === Hard Collateral 1-2 === Each of the multiple elements, including the transmission unit, analysis unit, determination unit, and notification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the transmission unit takes a photo of the excrement using the camera 42 of the smart glasses 214 and transmits the photo to the system via a dedicated app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the color, shape, texture, etc. of the excrement using an image analysis algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the health condition based on the analyzed information. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the owner of the nature of the abnormality and how to deal with it. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned transmission unit, analysis unit, determination unit, and notification unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the transmission unit takes a photo of the excrement using the camera 42 of the headset-type terminal 314 and transmits the photo to the system via a dedicated app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the color, shape, texture, etc. of the excrement using an image analysis algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the health condition based on the analyzed information. The notification unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and notifies the owner of the nature of the abnormality and how to deal with it. === Hard Collateral 1-4 === Each of the multiple elements including the transmission unit, analysis unit, determination unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the transmission unit takes a photo of the excrement using the camera 42 of the robot 414 and transmits the photo to the system via a dedicated app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the color, shape, texture, etc. of the excrement using an image analysis algorithm. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the health condition based on the analyzed information. The notification unit is realized, for example, by the control unit 46A of the robot 414 and notifies the owner of the nature of the abnormality and how to deal with it.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] When sending photos of pet excrement, the sending unit can analyze the owner's past sending history and select the optimal sending method. For example, the sending unit can prioritize and suggest sending methods (email, app, etc.) that the owner has frequently used based on the past sending history. The sending unit can also suggest a method for sending during a specific time period based on the past sending history. Furthermore, the sending unit can analyze the past sending history and select the most efficient sending method. In this way, the sending unit can select the optimal sending method by analyzing the past sending history.

[0128] The analysis unit may include an odor evaluation unit that evaluates the odor of excrement in addition to evaluating the color and shape of the excrement. The odor evaluation unit, for example, uses an odor sensor to detect the odor of excrement and analyzes the data. The odor evaluation unit can generate data for determining the health condition based on the intensity and type of odor. For example, the odor evaluation unit can detect odors associated with specific diseases and detect abnormalities early. The odor evaluation unit can also track changes in odor and monitor changes in the health condition. This allows the odor evaluation unit to evaluate the odor of excrement and determine the health condition in more detail.

[0129] When sending a notification through a smartphone app, the notification unit can estimate the owner's emotions and adjust the notification display method based on the estimated owner's emotions. For example, the notification unit can analyze the owner's facial expressions and voice to estimate the owner's emotions. The notification unit can also adjust the notification display method based on the owner's emotions. For example, if the owner is feeling anxious, a concise and clear notification can be displayed. Alternatively, if the owner is relaxed, a detailed notification can be displayed. Furthermore, if the owner is busy, a notification that focuses on the main points can be displayed. In this way, the notification unit can provide notifications that are easy for the owner to understand by adjusting the notification display method according to the owner's emotions.

[0130] The analysis unit may include a learning unit that learns the characteristics of an infant's excrement, as well as a diet learning unit that learns the infant's dietary content. The diet learning unit, for example, records the infant's dietary content and learns the characteristics of the excrement based on that data. The diet learning unit can analyze the relationship between the dietary content and the excrement and build a model for assessing the health condition. For example, the diet learning unit can learn the effect of specific ingredients on the excrement and detect abnormalities early. The diet learning unit can also predict the characteristics of the excrement according to changes in the dietary content and monitor changes in the health condition. In this way, the diet learning unit can learn the infant's dietary content and more accurately assess the health condition.

[0131] When determining the health condition of an infant, the determination unit can estimate the infant's emotions and adjust the determination criteria based on the estimated infant's emotions. For example, the determination unit analyzes the infant's facial expressions and crying to estimate the emotions. The determination unit can also adjust the determination criteria based on the infant's emotions. For example, if the infant feels anxious, the determination can be made using strict criteria. If the infant is relaxed, the determination can be made using normal criteria. Furthermore, if the infant is tired, the determination can be made using simple criteria. In this way, the determination unit can provide an appropriate determination result by adjusting the determination criteria according to the infant's emotions.

[0132] When transmitting photos of pet excrement, the transmitting unit can filter based on the pet's current health condition and living environment. For example, if the pet's health condition is good, the transmitting unit uses a normal transmission method. Furthermore, if the pet's health condition is unstable, the transmitting unit can use a transmission method including detailed information. For example, if the pet's health condition is deteriorating, the transmitting unit transmits photos including detailed analysis results and countermeasures. Furthermore, if the pet's living environment changes, the transmitting unit can adjust the transmission method taking that information into consideration. For example, if the pet moves to a new environment, the transmitting unit selects a transmission method appropriate for that environment. In this way, the transmitting unit can transmit appropriate information by filtering based on the pet's health condition and living environment.

[0133] The analysis unit may include a sound evaluation unit that evaluates the sound of excrement in addition to evaluating the color and shape of the excrement. The sound evaluation unit, for example, detects the sound of excrement using a sound sensor and analyzes the data. The sound evaluation unit can generate data for determining the health condition based on the intensity and type of sound. For example, the sound evaluation unit can detect sounds related to specific diseases and detect abnormalities early. The sound evaluation unit can also track changes in sound and monitor changes in the health condition. This allows the sound evaluation unit to evaluate the sound of excrement and determine the health condition in more detail.

[0134] When sending notifications through a smartphone app, the notification unit can prioritize sending highly relevant photos by taking into account the owner's geographical location information. For example, if the owner is at home, the notification unit can prioritize sending everyday photos. Also, if the owner is traveling, the notification unit can prioritize sending important photos related to the pet's health condition. Furthermore, if the owner is at work, the notification unit can prioritize sending photos that can be viewed in a short time. In this way, the notification unit can prioritize sending highly relevant photos by taking into account the owner's geographical location information.

[0135] The sending unit can estimate the owner's emotions and determine the priority of photos to be sent based on the estimated owner's emotions. For example, the sending unit can analyze the owner's facial expressions and voice to estimate emotions. The sending unit can also determine the priority of photos to be sent based on the owner's emotions. For example, if the owner is feeling anxious, the AI ​​can prioritize sending important photos related to the owner's health. Also, if the owner is relaxed, the AI ​​can prioritize sending everyday photos. Furthermore, if the owner is busy, the AI ​​can select and send the most important photos. In this way, the sending unit can prioritize the transmission of important information by determining the priority of photos to be sent according to the owner's emotions.

[0136] The analysis unit may include a temperature evaluation unit that evaluates the temperature of the excrement in addition to evaluating the color and shape of the excrement. The temperature evaluation unit, for example, detects the temperature of the excrement using a temperature sensor and analyzes the data. The temperature evaluation unit can generate data for determining the health condition based on temperature changes or abnormalities. For example, the temperature evaluation unit can detect temperature changes associated with specific diseases and detect abnormalities early. The temperature evaluation unit can also track temperature changes and monitor changes in the health condition. This allows the temperature evaluation unit to evaluate the temperature of the excrement and determine the health condition in more detail.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: The owner takes a photo of their pet's excrement and sends it to the system. For example, the transmitter can take a photo of the excrement using a smartphone camera and send it to the system via a dedicated app. The transmitter can also automatically adjust the resolution and angle of the photo and send it in a format suitable for analysis. Step 2: The analysis unit analyzes the photo sent by the transmission unit. For example, the analysis unit uses an image analysis algorithm to evaluate the color, shape, texture, etc. of the excrement. The analysis unit can use AI to extract characteristics of the excrement and generate data to determine the health status. Step 3: The judgment unit judges the pet's health condition based on the information analyzed by the analysis unit. For example, the judgment unit evaluates the pet's health condition based on changes in the color and shape of the excrement. The judgment unit uses AI to determine whether there are any abnormalities and notify the owner if necessary. Step 4: The notification unit notifies the owner of the abnormality determined by the determination unit. For example, the notification unit can notify the owner of the nature of the abnormality and how to deal with it via a smartphone app. The notification unit can also automatically adjust the timing and content of the notification so that the owner can respond promptly.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0169] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0186] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0187] 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.

[0188] 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.

[0189] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0190] 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.

[0191] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Explanation of symbols]

[0211] 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 transmitting unit that transmits a photograph of excrement; an analysis unit that analyzes the photograph transmitted by the transmission unit; a determination unit that determines a health state based on the information analyzed by the analysis unit; a notification unit that notifies the abnormality determined by the determination unit; Equipped with A system characterized by:

2. The analysis unit Equipped with an evaluation unit that evaluates the color or shape of excrement The system of claim 1 .

3. The notification unit Equipped with an app notification section that notifies you via a smartphone app The system of claim 1 .

4. The analysis unit Equipped with a learning section that teaches the characteristics of infant excrement The system of claim 1 .

5. The determination unit Equipped with an infant assessment unit that assesses the health status of infants The system of claim 1 .

6. The transmission unit Estimates the owner's emotions and adjusts the timing of sending photos based on the estimated emotions. The system of claim 1 .

7. The transmission unit Analyze the owner's past sending history and select the appropriate sending method The system of claim 1 .

8. The transmission unit Filter photos submitted based on your pet's current health and living environment The system of claim 1 .

9. The transmission unit Estimate the owner's emotions and prioritize photos to send based on the estimated emotions. The system of claim 1 .

Images