system

Abstract

The system according to this embodiment aims to analyze the genetic information of pets and provide an optimal care plan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects genetic information of the pet. The analysis unit analyzes the genetic information collected by the collection unit. The generation unit generates a care plan based on the results analyzed by the analysis unit. The provision unit provides the care plan generated by the generation unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized care plans utilizing pet genetic information have not been sufficiently provided, and there is room for improvement.

[0005] <00000​​​​​​​The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects genetic information of pets. The analysis unit analyzes the genetic information collected by the collection unit. The generation unit generates a care plan based on the results of the analysis performed by the analysis unit. The provision unit provides the care plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a pet's genetic information and provide an optimal care plan. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 2 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network This specification is incorporated by reference in its entirety into the specification of the present application and forms a part of the specification of the present application. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The pet care system according to an embodiment of the present invention is a system that utilizes generative AI to perform genetic testing on pets and provide personalized care plans. The pet care system analyzes the pet's genetic information and, based on the results, generates an optimal customized food, exercise, and care plan to support pet health management and early detection of diseases. The aim is to maximize the happiness and bond between pets and their owners. For example, the pet care system collects the pet's genetic information. For example, it collects genetic samples from the pet's saliva or hair and sends them to an analysis laboratory. The analysis laboratory analyzes the genetic information to identify the pet's health status and genetic risks. Next, the pet care system uses generative AI to generate an optimal customized food, exercise, and care plan for the pet based on the analysis results. For example, if there is a specific genetic risk, it proposes a special diet and exercise plan to mitigate that risk. It also provides a care plan tailored to the pet's age, weight, and activity level. The generated care plan is provided to the owner. The owner manages the pet's health according to the customized food, exercise, and care plan proposed by the generative AI. For example, the owner maintains the pet's health by providing food rich in specific nutrients or ensuring appropriate exercise. This service makes pet health management easier and enables early detection of diseases. Pet owners can deepen their bond with their pets by constantly monitoring their health and providing appropriate care. Furthermore, the pet care industry can offer more advanced services by providing genetic testing and care plans utilizing generative AI. For example, veterinarians and breeders in the pet care industry can use the genetic information and care plans provided by generative AI to manage pets' health. This allows them to accurately understand the pet's health status and provide appropriate treatment and care. In this way, providing pet genetic testing and personalized care plans using generative AI aims to support pet health management and early detection of diseases, maximizing the happiness and bond between pets and their owners. As a result, the pet care system can support pet health management and early detection of diseases.

[0029] The pet care system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a supply unit. The collection unit collects the pet's genetic information. The collection unit collects genetic samples from, for example, the pet's saliva or hair. The collection unit, for example, collects a pet's saliva sample and sends it to an analysis laboratory. The collection unit can also collect a pet's hair sample and send it to an analysis laboratory. The collection unit can also collect a pet's blood sample and send it to an analysis laboratory. The analysis unit analyzes the genetic information collected by the collection unit. The analysis unit, for example, analyzes the genetic information to identify the pet's health status and genetic risks. The analysis unit can also, for example, analyze the genetic information to evaluate the pet's health status. The analysis unit can also, for example, analyze the genetic information to identify the pet's genetic risks. The generation unit generates a care plan based on the results analyzed by the analysis unit. The generation unit generates, for example, customized food, exercise, and a care plan that is optimal for the pet based on the analysis results. The generation unit can, for example, suggest special diet and exercise plans to mitigate specific genetic risks. The generation unit can also generate care plans tailored to the pet's age, weight, and activity level. The provision unit provides the care plans generated by the generation unit. The provision unit provides the generated care plans to the pet owners. The provision unit can also provide the generated care plans through a digital platform. The provision unit can also provide the generated care plans as printed materials. Thus, the pet care system according to this embodiment can support pet health management and early detection of diseases.

[0030] The collection unit collects genetic information from pets. Specifically, it collects genetic samples from pets' saliva, hair, blood, etc. For example, when collecting a pet's saliva sample, a special cotton swab is used to collect saliva from the mouth, which is then placed in a sealed container and sent to the analysis laboratory. Similarly, when collecting a pet's hair sample, shed hair or hair collected during brushing is used, and this too can be placed in a sealed container and sent to the analysis laboratory. Furthermore, when collecting a pet's blood sample, a veterinarian collects the blood and sends it to the analysis laboratory using appropriate storage methods. These sample collections are carried out to obtain accurate genetic information while minimizing the burden on the pet. The collection unit centrally manages these samples, allowing the analysis unit to access them quickly. The collection unit also ensures appropriate storage methods and transportation means to maintain sample quality, thereby improving the accuracy of the analysis results. In this way, the collection unit can efficiently and accurately collect pets' genetic information and improve the reliability of the entire system.

[0031] The analysis unit analyzes the genetic information collected by the data collection unit. Specifically, it analyzes the genetic information to identify the pet's health status and genetic risks. Using the latest genetic analysis technology, the analysis unit analyzes the pet's gene sequence in detail and detects specific genetic markers. This allows for the assessment of the pet's genetic health risks and susceptibility to specific diseases. For example, based on the pet's genetic information, the analysis unit can identify risks such as obesity, diabetes, and heart disease, enabling early intervention. Furthermore, based on the pet's genetic information, the analysis unit can identify optimal nutrients and exercise levels to help maintain the pet's health. In addition, based on the genetic information, the analysis unit can evaluate the pet's behavioral characteristics and personality traits and suggest appropriate care methods to the owner. As a result, the analysis unit can gain a detailed understanding of the pet's health status and genetic risks, and provide useful information to the owner.

[0032] The generation unit generates a care plan based on the results analyzed by the analysis unit. Specifically, it generates a customized food, exercise, and care plan that is optimal for the pet based on the analysis results. For example, if there is a specific genetic risk, it can suggest a special diet and exercise plan to mitigate that risk. The generation unit generates a care plan that is appropriate for the pet's age, weight, and activity level, supporting the maintenance of the pet's health. For example, for pets at high risk of obesity, it can suggest a low-calorie customized food and a moderate exercise plan. Also, for pets at risk of heart disease, it can suggest an exercise plan that does not strain the heart and a diet plan that includes specific nutrients. Furthermore, the generation unit can generate a care plan that takes into account the pet's behavioral characteristics and personality tendencies, and can also suggest appropriate care methods to the owner. In this way, the generation unit can provide an optimal care plan that is appropriate for the pet's health condition and genetic risks, supporting the maintenance of the pet's health.

[0033] The service provider provides the care plans generated by the generation service provider. Specifically, it provides the generated care plans to pet owners. The service provider can provide care plans through a digital platform. For example, pet owners can review and implement their pet's care plan through a dedicated application or website. The service provider can also provide the generated care plans in printed form. For example, a detailed care plan can be provided to pet owners as a booklet or leaflet to assist with daily care. Furthermore, the service provider can monitor the implementation status of the care plan and collect feedback as needed. For example, pet owners can report the results of implementing the care plan, and the service provider can revise and improve the care plan based on that. This allows the service provider to provide pet owners with appropriate care plans and support the management of their pets' health. In addition, through communication with pet owners, the service provider can continuously evaluate the pet's health condition and the effectiveness of the care plan, and provide optimal care. This allows the service provider to comprehensively support the management of pets' health and provide pet owners with peace of mind.

[0034] The collection unit can collect genetic samples from pet saliva, hair, etc. For example, the collection unit can collect a pet saliva sample. The collection unit can also collect a pet hair sample, for example. The collection unit can also collect a pet blood sample, for example. By collecting genetic samples from pet saliva, hair, etc., genetic information can be obtained. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect a pet saliva sample, input that sample into the AI, and have the AI ​​perform genetic information analysis.

[0035] The analysis unit can analyze the collected genetic information to identify the pet's health status and genetic risks. For example, the analysis unit can analyze the genetic information to evaluate the pet's health status. For example, the analysis unit can analyze the genetic information to identify the pet's genetic risks. For example, the analysis unit can analyze the genetic information to evaluate the pet's health status in detail. In this way, by analyzing the genetic information, the pet's health status and genetic risks can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected genetic information into AI and have the AI ​​perform the identification of the pet's health status and genetic risks.

[0036] The generation unit can generate customized food, exercise, and care plans optimized for pets based on the analysis results. For example, the generation unit can generate customized food optimized for pets based on the analysis results. The generation unit can also generate exercise plans optimized for pets based on the analysis results. The generation unit can also generate care plans optimized for pets based on the analysis results. This allows for support of pet health management by generating optimal care plans based on the analysis results. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the analysis results into the generation AI and have the generation AI execute the generation of customized food, exercise, and care plans optimized for pets.

[0037] The service provider can provide the generated care plan to the pet owner. The service provider can, for example, provide the generated care plan through a digital platform. The service provider can also, for example, provide the generated care plan as a printed document. The service provider can also, for example, provide the generated care plan via email. This allows the service provider to support pet health management by providing the generated care plan to the pet owner. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated care plan into an AI and have the AI ​​execute a method for providing it to the pet owner.

[0038] The generation unit can generate care plans tailored to the pet's age, weight, and activity level. For example, the generation unit can generate a care plan based on the pet's age. The generation unit can also generate a care plan based on the pet's weight. The generation unit can also generate a care plan based on the pet's activity level. This allows for the provision of individually optimized care by generating care plans tailored to the pet's age, weight, and activity level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the pet's age, weight, and activity level into the generation AI and have the generation AI generate the care plan.

[0039] The collection unit can estimate the pet's emotions and adjust the timing of gene sample collection based on the estimated emotions. For example, the collection unit can set the timing to collect a saliva sample when the pet is relaxed. The collection unit can also adjust the timing to avoid collecting a hair sample if the pet is excited. For example, if the pet is stressed, the collection unit can temporarily suspend collection and wait until the pet calms down. By adjusting the collection timing based on the pet's emotions, stress can be reduced and accurate samples can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input pet emotion data into an AI and have the AI ​​adjust the collection timing.

[0040] The data collection unit can analyze the pet's past health data and select the optimal sample collection method. For example, the data collection unit can select the most suitable sample collection method based on the pet's past health check data. The data collection unit can also avoid certain sample collection methods by considering the pet's past medical history. For example, the data collection unit can select a collection method to avoid allergic reactions based on the pet's past allergy information. By selecting the optimal collection method based on past health data, more accurate genetic information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's past health data into AI and have the AI ​​select the optimal sample collection method.

[0041] The collection unit can filter genetic samples based on the pet's current health status and living environment. For example, if the pet is healthy, the collection unit uses a standard sample collection method. If the pet is ill, for example, the collection unit can also use a special sample collection method. If the pet's living environment changes, for example, the collection unit can select a sample collection method suitable for the new environment. This allows for the collection of appropriate samples by filtering based on the current health status and living environment. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the pet's current health status and living environment into the AI ​​and have the AI ​​perform the filtering.

[0042] The collection unit can estimate the pet's emotions and determine the priority of samples to collect based on the estimated emotions. For example, if the pet is relaxed, the collection unit may prioritize collecting saliva samples. If the pet is excited, the collection unit may also prioritize collecting hair samples. If the pet is stressed, the collection unit may temporarily suspend collection and wait until the pet calms down. This reduces stress and ensures accurate sample collection by prioritizing samples based on the pet's emotions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input pet emotion data into AI and have the AI ​​determine the priority of samples.

[0043] The collection unit can prioritize the collection of highly relevant samples by considering the pet's geographical location when collecting genetic samples. For example, if the pet lives in a specific region, the collection unit will prioritize the collection of genetic samples related to that region. For example, if the pet is traveling, the collection unit can also collect samples suitable for the environment of the travel destination. For example, if the pet has moved, the collection unit can also prioritize the collection of samples related to the new region. By collecting samples while considering geographical location, it is possible to obtain genetic information that takes into account region-specific risks. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the pet's geographical location information into the AI ​​and have the AI ​​perform the priority collection of highly relevant samples.

[0044] The collection unit can analyze the social media activity of pet owners when collecting genetic samples and collect relevant samples. For example, if an owner shares information about their pet's health on social media, the collection unit can collect samples based on that information. For example, if an owner shares information about their pet's activities on social media, the collection unit can also collect samples based on that information. For example, if an owner shares information about their pet's diet on social media, the collection unit can also collect samples based on that information. In this way, by analyzing the owner's social media activity, it is possible to collect samples based on the pet's living situation. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the owner's social media activity into AI and have the AI ​​perform the collection of relevant samples.

[0045] The analysis unit can estimate the pet's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the pet is relaxed, the analysis unit can provide detailed analysis results. For example, if the pet is excited, the analysis unit can also provide concise analysis results. For example, if the pet is stressed, the analysis unit can also present the analysis results in a visually easy-to-understand manner. By adjusting the presentation of the analysis based on the pet's emotions, the analysis unit can provide results that are easy for the owner to understand. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information during the analysis. For example, the analysis unit performs a detailed analysis for important genetic information. For example, the analysis unit can perform a simplified analysis for less important genetic information. For example, the analysis unit can perform additional analysis for highly important genetic information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the genetic information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic information importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the genetic information during analysis. For example, the analysis unit applies a specific analysis algorithm to health-related genetic information. The analysis unit can also apply a different analysis algorithm to behavior-related genetic information. The analysis unit can also apply yet another analysis algorithm to reproductive-related genetic information. By applying different analysis algorithms depending on the category of genetic information, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic information category data into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0048] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the pet is relaxed, the analysis unit can perform a detailed analysis. For example, if the pet is excited, the analysis unit can also perform a concise analysis. For example, if the pet is stressed, the analysis unit can shorten the analysis. By adjusting the length of the analysis based on the pet's emotions, the analysis results can be provided in a way that is easy for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input pet emotion data into AI and have the AI ​​adjust the length of the analysis.

[0049] The analysis unit can determine the priority of analysis based on the timing of gene information collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected gene information. The analysis unit may also postpone the analysis of older gene information. For example, the analysis unit may prioritize the analysis of gene information collected within a specific period. This allows for efficient analysis by determining the priority of analysis based on the timing of gene information collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input gene information collection timing data into the AI ​​and have the AI ​​determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the genetic information during the analysis. For example, the analysis unit may prioritize the analysis of important genetic information. For example, the analysis unit may postpone the analysis of less relevant genetic information. For example, the analysis unit may prioritize the analysis of genetic information related to a specific category. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the genetic information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic relevance data into AI and have AI perform the adjustment of the analysis order.

[0051] The generation unit can estimate the pet's emotions and adjust the care plan generation method based on the estimated emotions. For example, if the pet is relaxed, the generation unit can generate a detailed care plan. If the pet is excited, for example, the generation unit can also generate a concise care plan. If the pet is stressed, for example, the generation unit can also present the care plan in a visually easy-to-understand manner. This allows the system to provide the optimal care plan for the pet by adjusting the care plan generation method based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input pet emotion data into the generation AI and have the generation AI adjust the care plan generation method.

[0052] The generation unit can analyze the pet's past health data to select the optimal care plan when generating a care plan. For example, the generation unit can select the optimal care plan based on the pet's past health check data. The generation unit can also consider the pet's past medical history and avoid certain care plans. For example, the generation unit can select a care plan to avoid allergic reactions based on the pet's past allergy information. In this way, by selecting the optimal care plan based on past health data, the pet's health management can be optimized. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the pet's past health data into the generation AI and have the generation AI select the optimal care plan.

[0053] The generation unit can customize the elements of a care plan based on the pet's current living situation when generating a care plan. For example, if the pet lives indoors, the generation unit will generate a care plan that includes indoor exercise. If the pet lives outdoors, the generation unit can also generate a care plan that includes outdoor exercise. If the pet has specific dietary restrictions, the generation unit can also generate a care plan based on those restrictions. This allows for the provision of optimal care for the pet by customizing the care plan based on the current living situation. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the pet's current living situation into the generation AI and have the generation AI customize the elements of the care plan.

[0054] The generation unit can estimate the pet's emotions and determine the priority of care plans based on the estimated emotions. For example, if the pet is relaxed, the generation unit may prioritize providing a detailed care plan. If the pet is excited, for example, the generation unit may also prioritize providing a concise care plan. If the pet is stressed, for example, the generation unit may also present the care plan in a visually easy-to-understand manner. This allows for the provision of optimal care for the pet by prioritizing care plans based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit can input pet emotion data into the generative AI and have the generative AI determine the priority of care plans.

[0055] The generation unit can select the optimal care plan by considering the pet's geographical location information when generating a care plan. For example, if the pet lives in a specific area, the generation unit can generate a care plan suitable for that area. For example, if the pet is traveling, the generation unit can also generate a care plan suitable for the environment of the travel destination. For example, if the pet moves, the generation unit can also generate a care plan suitable for the new area. By selecting a care plan while considering geographical location information, it is possible to provide care that takes into account area-specific risks. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the pet's geographical location information data into the generation AI and have the generation AI select the optimal care plan.

[0056] The generation unit can analyze the pet owner's social media activity when generating a care plan and propose methods for the care plan. For example, if the owner shares information about the pet's health on social media, the generation unit can generate a care plan based on that information. For example, if the owner shares information about the pet's activities on social media, the generation unit can also generate a care plan based on that information. For example, if the owner shares information about the pet's meals on social media, the generation unit can also generate a care plan based on that information. In this way, by analyzing the owner's social media activity, it is possible to provide a care plan that is based on the pet's living situation. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the owner's social media activity into the generation AI and have the generation AI propose methods for the care plan.

[0057] The service provider can estimate the pet's emotions and adjust the method of providing the care plan based on the estimated emotions. For example, if the pet is relaxed, the service provider can provide a detailed care plan. If the pet is excited, the service provider can also provide a concise care plan. If the pet is stressed, the service provider can also present the care plan in a visually easy-to-understand manner. This allows the service provider to provide the optimal care plan for the pet by adjusting the method of provision based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into AI and have the AI ​​adjust the method of provision.

[0058] The service provider can select the optimal service method by referring to the pet's past care history when providing a care plan. For example, the service provider can select the optimal service method based on the pet's past care history. The service provider can also avoid certain care methods based on the pet's past care history. For example, the service provider can analyze the pet's past care history and select the most effective service method. This allows the service provider to provide the best possible care plan for the pet by selecting the optimal service method based on past care history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet's past care history data into AI and have the AI ​​select the optimal service method.

[0059] The service provider can estimate the pet's emotions and adjust the care plan delivery procedure based on the estimated emotions. For example, if the pet is relaxed, the service provider can provide a detailed delivery procedure. If the pet is excited, the service provider can also provide a concise delivery procedure. If the pet is stressed, the service provider can also visually represent the delivery procedure in an easy-to-understand way. This allows the service provider to provide the optimal care plan for the pet by adjusting the delivery procedure based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into AI and have the AI ​​perform the adjustment of the delivery procedure.

[0060] The service provider can select the optimal service delivery method when providing a care plan, taking into account the pet owner's device information. For example, if the owner is using a smartphone, the service provider can provide a service delivery method adapted to the screen size. If the owner is using a tablet, the service provider can also provide a service delivery method optimized for a larger screen. If the owner is using a smartwatch, the service provider can also provide a concise and highly visible service delivery method. By selecting a service delivery method that takes the owner's device information into account, the service provider can provide the best possible care plan for the pet. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the owner's device information into the AI ​​and have the AI ​​select the optimal service delivery method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The pet care system can further collect pet behavioral data and provide behavioral modification plans based on the analysis results. For example, the collection unit collects data using sensors to monitor the pet's activity level and sleep patterns. The analysis unit analyzes the collected behavioral data to identify the pet's behavioral patterns and abnormal behaviors. The generation unit generates training plans and environmental adjustment plans to improve the pet's behavior based on the analysis results. The provision unit provides the generated behavioral modification plans to the pet owner to support the improvement of the pet's behavior. This allows for the early detection of pet behavioral problems and the implementation of appropriate countermeasures.

[0063] The pet care system can further collect pet dietary data and provide a meal plan based on the analysis results. For example, the collection unit collects data using a device to record the pet's food content and intake. The analysis unit analyzes the collected dietary data to identify the pet's nutritional balance and dietary imbalances. The generation unit generates an optimal meal plan for the pet based on the analysis results. The delivery unit provides the generated meal plan to the owner, supporting the pet's health management. This allows for efficient management of the pet's diet and helps maintain its health.

[0064] The pet care system can further collect pet health data and provide a health monitoring plan based on the analysis results. For example, the collection unit collects data using devices to monitor the pet's body temperature and heart rate. The analysis unit analyzes the collected health data and evaluates the pet's health status. The generation unit generates a plan for continuously monitoring the pet's health status based on the analysis results. The delivery unit provides the generated health monitoring plan to the owner, supporting pet health management. This allows owners to constantly monitor their pet's health status and detect abnormalities early.

[0065] The pet care system can further collect data on the pet's living environment and provide environmental improvement plans based on the analysis results. For example, the collection unit collects data using sensors to monitor the pet's living environment (temperature, humidity, noise level, etc.). The analysis unit analyzes the collected environmental data and identifies problems in the pet's living environment. The generation unit generates a plan to improve the pet's living environment based on the analysis results. The provision unit provides the generated environmental improvement plan to the pet owner and offers advice on optimizing the pet's living environment. This can improve the pet's living environment and enhance its health and well-being.

[0066] The pet care system can further collect social data about pets and provide socialization plans based on the analysis results. For example, the collection unit collects data using devices designed to collect data on the pet's interactions with other animals and people. The analysis unit analyzes the collected social data to identify the pet's social skills and any problems. The generation unit generates a plan to promote the pet's socialization based on the analysis results. The provision unit provides the generated socialization plan to the owner and offers advice on how to improve the pet's social skills. This helps improve the pet's social skills and build good relationships with other animals and people.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects genetic information from pets. The collection unit collects genetic samples from sources such as pet saliva, hair, and blood, and sends them to an analysis laboratory. Step 2: The analysis unit analyzes the genetic information collected by the collection unit. For example, the analysis unit analyzes the genetic information to identify the pet's health status and genetic risks. Step 3: The generation unit generates a care plan based on the results analyzed by the analysis unit. For example, the generation unit generates a customized food, exercise, and care plan that is optimal for the pet based on the analysis results. Step 4: The provider provides the care plan generated by the generator. The provider can, for example, provide the generated care plan to the pet owner, either as a digital platform or as a printed document.

[0069] (Example of form 2) The pet care system according to an embodiment of the present invention is a system that utilizes generative AI to perform genetic testing on pets and provide personalized care plans. The pet care system analyzes the pet's genetic information and, based on the results, generates an optimal customized food, exercise, and care plan to support pet health management and early detection of diseases. The aim is to maximize the happiness and bond between pets and their owners. For example, the pet care system collects the pet's genetic information. For example, it collects genetic samples from the pet's saliva or hair and sends them to an analysis laboratory. The analysis laboratory analyzes the genetic information to identify the pet's health status and genetic risks. Next, the pet care system uses generative AI to generate an optimal customized food, exercise, and care plan for the pet based on the analysis results. For example, if there is a specific genetic risk, it proposes a special diet and exercise plan to mitigate that risk. It also provides a care plan tailored to the pet's age, weight, and activity level. The generated care plan is provided to the owner. The owner manages the pet's health according to the customized food, exercise, and care plan proposed by the generative AI. For example, the owner maintains the pet's health by providing food rich in specific nutrients or ensuring appropriate exercise. This service makes pet health management easier and enables early detection of diseases. Pet owners can deepen their bond with their pets by constantly monitoring their health and providing appropriate care. Furthermore, the pet care industry can offer more advanced services by providing genetic testing and care plans utilizing generative AI. For example, veterinarians and breeders in the pet care industry can use the genetic information and care plans provided by generative AI to manage pets' health. This allows them to accurately understand the pet's health status and provide appropriate treatment and care. In this way, providing pet genetic testing and personalized care plans using generative AI aims to support pet health management and early detection of diseases, maximizing the happiness and bond between pets and their owners. As a result, the pet care system can support pet health management and early detection of diseases.

[0070] The pet care system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a supply unit. The collection unit collects the pet's genetic information. The collection unit collects genetic samples from, for example, the pet's saliva or hair. The collection unit, for example, collects a pet's saliva sample and sends it to an analysis laboratory. The collection unit can also collect a pet's hair sample and send it to an analysis laboratory. The collection unit can also collect a pet's blood sample and send it to an analysis laboratory. The analysis unit analyzes the genetic information collected by the collection unit. The analysis unit, for example, analyzes the genetic information to identify the pet's health status and genetic risks. The analysis unit can also, for example, analyze the genetic information to evaluate the pet's health status. The analysis unit can also, for example, analyze the genetic information to identify the pet's genetic risks. The generation unit generates a care plan based on the results analyzed by the analysis unit. The generation unit generates, for example, customized food, exercise, and a care plan that is optimal for the pet based on the analysis results. The generation unit can, for example, suggest special diet and exercise plans to mitigate specific genetic risks. The generation unit can also generate care plans tailored to the pet's age, weight, and activity level. The provision unit provides the care plans generated by the generation unit. The provision unit provides the generated care plans to the pet owners. The provision unit can also provide the generated care plans through a digital platform. The provision unit can also provide the generated care plans as printed materials. Thus, the pet care system according to this embodiment can support pet health management and early detection of diseases.

[0071] The collection unit collects genetic information from pets. Specifically, it collects genetic samples from pets' saliva, hair, blood, etc. For example, when collecting a pet's saliva sample, a special cotton swab is used to collect saliva from the mouth, which is then placed in a sealed container and sent to the analysis laboratory. Similarly, when collecting a pet's hair sample, shed hair or hair collected during brushing is used, and this too can be placed in a sealed container and sent to the analysis laboratory. Furthermore, when collecting a pet's blood sample, a veterinarian collects the blood and sends it to the analysis laboratory using appropriate storage methods. These sample collections are carried out to obtain accurate genetic information while minimizing the burden on the pet. The collection unit centrally manages these samples, allowing the analysis unit to access them quickly. The collection unit also ensures appropriate storage methods and transportation means to maintain sample quality, thereby improving the accuracy of the analysis results. In this way, the collection unit can efficiently and accurately collect pets' genetic information and improve the reliability of the entire system.

[0072] The analysis unit analyzes the genetic information collected by the data collection unit. Specifically, it analyzes the genetic information to identify the pet's health status and genetic risks. Using the latest genetic analysis technology, the analysis unit analyzes the pet's gene sequence in detail and detects specific genetic markers. This allows for the assessment of the pet's genetic health risks and susceptibility to specific diseases. For example, based on the pet's genetic information, the analysis unit can identify risks such as obesity, diabetes, and heart disease, enabling early intervention. Furthermore, based on the pet's genetic information, the analysis unit can identify optimal nutrients and exercise levels to help maintain the pet's health. In addition, based on the genetic information, the analysis unit can evaluate the pet's behavioral characteristics and personality traits and suggest appropriate care methods to the owner. As a result, the analysis unit can gain a detailed understanding of the pet's health status and genetic risks, and provide useful information to the owner.

[0073] The generation unit generates a care plan based on the results analyzed by the analysis unit. Specifically, it generates a customized food, exercise, and care plan that is optimal for the pet based on the analysis results. For example, if there is a specific genetic risk, it can suggest a special diet and exercise plan to mitigate that risk. The generation unit generates a care plan that is appropriate for the pet's age, weight, and activity level, supporting the maintenance of the pet's health. For example, for pets at high risk of obesity, it can suggest a low-calorie customized food and a moderate exercise plan. Also, for pets at risk of heart disease, it can suggest an exercise plan that does not strain the heart and a diet plan that includes specific nutrients. Furthermore, the generation unit can generate a care plan that takes into account the pet's behavioral characteristics and personality tendencies, and can also suggest appropriate care methods to the owner. In this way, the generation unit can provide an optimal care plan that is appropriate for the pet's health condition and genetic risks, supporting the maintenance of the pet's health.

[0074] The service provider provides the care plans generated by the generation service provider. Specifically, it provides the generated care plans to pet owners. The service provider can provide care plans through a digital platform. For example, pet owners can review and implement their pet's care plan through a dedicated application or website. The service provider can also provide the generated care plans in printed form. For example, a detailed care plan can be provided to pet owners as a booklet or leaflet to assist with daily care. Furthermore, the service provider can monitor the implementation status of the care plan and collect feedback as needed. For example, pet owners can report the results of implementing the care plan, and the service provider can revise and improve the care plan based on that. This allows the service provider to provide pet owners with appropriate care plans and support the management of their pets' health. In addition, through communication with pet owners, the service provider can continuously evaluate the pet's health condition and the effectiveness of the care plan, and provide optimal care. This allows the service provider to comprehensively support the management of pets' health and provide pet owners with peace of mind.

[0075] The collection unit can collect genetic samples from pet saliva, hair, etc. For example, the collection unit can collect a pet saliva sample. The collection unit can also collect a pet hair sample, for example. The collection unit can also collect a pet blood sample, for example. By collecting genetic samples from pet saliva, hair, etc., genetic information can be obtained. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect a pet saliva sample, input that sample into the AI, and have the AI ​​perform genetic information analysis.

[0076] The analysis unit can analyze the collected genetic information to identify the pet's health status and genetic risks. For example, the analysis unit can analyze the genetic information to evaluate the pet's health status. For example, the analysis unit can analyze the genetic information to identify the pet's genetic risks. For example, the analysis unit can analyze the genetic information to evaluate the pet's health status in detail. In this way, by analyzing the genetic information, the pet's health status and genetic risks can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected genetic information into AI and have the AI ​​perform the identification of the pet's health status and genetic risks.

[0077] The generation unit can generate customized food, exercise, and care plans optimized for pets based on the analysis results. For example, the generation unit can generate customized food optimized for pets based on the analysis results. The generation unit can also generate exercise plans optimized for pets based on the analysis results. The generation unit can also generate care plans optimized for pets based on the analysis results. This allows for support of pet health management by generating optimal care plans based on the analysis results. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the analysis results into the generation AI and have the generation AI execute the generation of customized food, exercise, and care plans optimized for pets.

[0078] The service provider can provide the generated care plan to the pet owner. The service provider can, for example, provide the generated care plan through a digital platform. The service provider can also, for example, provide the generated care plan as a printed document. The service provider can also, for example, provide the generated care plan via email. This allows the service provider to support pet health management by providing the generated care plan to the pet owner. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated care plan into an AI and have the AI ​​execute a method for providing it to the pet owner.

[0079] The generation unit can generate care plans tailored to the pet's age, weight, and activity level. For example, the generation unit can generate a care plan based on the pet's age. The generation unit can also generate a care plan based on the pet's weight. The generation unit can also generate a care plan based on the pet's activity level. This allows for the provision of individually optimized care by generating care plans tailored to the pet's age, weight, and activity level. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the pet's age, weight, and activity level into the generation AI and have the generation AI generate the care plan.

[0080] The collection unit can estimate the pet's emotions and adjust the timing of gene sample collection based on the estimated emotions. For example, the collection unit can set the timing to collect a saliva sample when the pet is relaxed. The collection unit can also adjust the timing to avoid collecting a hair sample if the pet is excited. For example, if the pet is stressed, the collection unit can temporarily suspend collection and wait until the pet calms down. By adjusting the collection timing based on the pet's emotions, stress can be reduced and accurate samples can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input pet emotion data into an AI and have the AI ​​adjust the collection timing.

[0081] The data collection unit can analyze the pet's past health data and select the optimal sample collection method. For example, the data collection unit can select the most suitable sample collection method based on the pet's past health check data. The data collection unit can also avoid certain sample collection methods by considering the pet's past medical history. For example, the data collection unit can select a collection method to avoid allergic reactions based on the pet's past allergy information. By selecting the optimal collection method based on past health data, more accurate genetic information can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the pet's past health data into AI and have the AI ​​select the optimal sample collection method.

[0082] The collection unit can filter genetic samples based on the pet's current health status and living environment. For example, if the pet is healthy, the collection unit uses a standard sample collection method. If the pet is ill, for example, the collection unit can also use a special sample collection method. If the pet's living environment changes, for example, the collection unit can select a sample collection method suitable for the new environment. This allows for the collection of appropriate samples by filtering based on the current health status and living environment. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the pet's current health status and living environment into the AI ​​and have the AI ​​perform the filtering.

[0083] The collection unit can estimate the pet's emotions and determine the priority of samples to collect based on the estimated emotions. For example, if the pet is relaxed, the collection unit may prioritize collecting saliva samples. If the pet is excited, the collection unit may also prioritize collecting hair samples. If the pet is stressed, the collection unit may temporarily suspend collection and wait until the pet calms down. This reduces stress and ensures accurate sample collection by prioritizing samples based on the pet's emotions. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input pet emotion data into AI and have the AI ​​determine the priority of samples.

[0084] The collection unit can prioritize the collection of highly relevant samples by considering the pet's geographical location when collecting genetic samples. For example, if the pet lives in a specific region, the collection unit will prioritize the collection of genetic samples related to that region. For example, if the pet is traveling, the collection unit can also collect samples suitable for the environment of the travel destination. For example, if the pet has moved, the collection unit can also prioritize the collection of samples related to the new region. By collecting samples while considering geographical location, it is possible to obtain genetic information that takes into account region-specific risks. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the pet's geographical location information into the AI ​​and have the AI ​​perform the priority collection of highly relevant samples.

[0085] The collection unit can analyze the social media activity of pet owners when collecting genetic samples and collect relevant samples. For example, if an owner shares information about their pet's health on social media, the collection unit can collect samples based on that information. For example, if an owner shares information about their pet's activities on social media, the collection unit can also collect samples based on that information. For example, if an owner shares information about their pet's diet on social media, the collection unit can also collect samples based on that information. In this way, by analyzing the owner's social media activity, it is possible to collect samples based on the pet's living situation. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the owner's social media activity into AI and have the AI ​​perform the collection of relevant samples.

[0086] The analysis unit can estimate the pet's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the pet is relaxed, the analysis unit can provide detailed analysis results. For example, if the pet is excited, the analysis unit can also provide concise analysis results. For example, if the pet is stressed, the analysis unit can also present the analysis results in a visually easy-to-understand manner. By adjusting the presentation of the analysis based on the pet's emotions, the analysis unit can provide results that are easy for the owner to understand. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic information during the analysis. For example, the analysis unit performs a detailed analysis for important genetic information. For example, the analysis unit can perform a simplified analysis for less important genetic information. For example, the analysis unit can perform additional analysis for highly important genetic information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the genetic information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic information importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the genetic information during analysis. For example, the analysis unit applies a specific analysis algorithm to health-related genetic information. The analysis unit can also apply a different analysis algorithm to behavior-related genetic information. The analysis unit can also apply yet another analysis algorithm to reproductive-related genetic information. By applying different analysis algorithms depending on the category of genetic information, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic information category data into the AI ​​and have the AI ​​execute the application of different analysis algorithms.

[0089] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the pet is relaxed, the analysis unit can perform a detailed analysis. For example, if the pet is excited, the analysis unit can also perform a concise analysis. For example, if the pet is stressed, the analysis unit can shorten the analysis. By adjusting the length of the analysis based on the pet's emotions, the analysis results can be provided in a way that is easy for the owner to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input pet emotion data into AI and have the AI ​​adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of gene information collection during the analysis. For example, the analysis unit may prioritize the analysis of recently collected gene information. The analysis unit may also postpone the analysis of older gene information. For example, the analysis unit may prioritize the analysis of gene information collected within a specific period. This allows for efficient analysis by determining the priority of analysis based on the timing of gene information collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input gene information collection timing data into the AI ​​and have the AI ​​determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the genetic information during the analysis. For example, the analysis unit may prioritize the analysis of important genetic information. For example, the analysis unit may postpone the analysis of less relevant genetic information. For example, the analysis unit may prioritize the analysis of genetic information related to a specific category. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the genetic information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input genetic relevance data into AI and have AI perform the adjustment of the analysis order.

[0092] The generation unit can estimate the pet's emotions and adjust the care plan generation method based on the estimated emotions. For example, if the pet is relaxed, the generation unit can generate a detailed care plan. If the pet is excited, for example, the generation unit can also generate a concise care plan. If the pet is stressed, for example, the generation unit can also present the care plan in a visually easy-to-understand manner. This allows the system to provide the optimal care plan for the pet by adjusting the care plan generation method based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input pet emotion data into the generation AI and have the generation AI adjust the care plan generation method.

[0093] The generation unit can analyze the pet's past health data to select the optimal care plan when generating a care plan. For example, the generation unit can select the optimal care plan based on the pet's past health check data. The generation unit can also consider the pet's past medical history and avoid certain care plans. For example, the generation unit can select a care plan to avoid allergic reactions based on the pet's past allergy information. In this way, by selecting the optimal care plan based on past health data, the pet's health management can be optimized. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the pet's past health data into the generation AI and have the generation AI select the optimal care plan.

[0094] The generation unit can customize the elements of a care plan based on the pet's current living situation when generating a care plan. For example, if the pet lives indoors, the generation unit will generate a care plan that includes indoor exercise. If the pet lives outdoors, the generation unit can also generate a care plan that includes outdoor exercise. If the pet has specific dietary restrictions, the generation unit can also generate a care plan based on those restrictions. This allows for the provision of optimal care for the pet by customizing the care plan based on the current living situation. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the pet's current living situation into the generation AI and have the generation AI customize the elements of the care plan.

[0095] The generation unit can estimate the pet's emotions and determine the priority of care plans based on the estimated emotions. For example, if the pet is relaxed, the generation unit may prioritize providing a detailed care plan. If the pet is excited, for example, the generation unit may also prioritize providing a concise care plan. If the pet is stressed, for example, the generation unit may also present the care plan in a visually easy-to-understand manner. This allows for the provision of optimal care for the pet by prioritizing care plans based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generative AI. For example, the generation unit can input pet emotion data into the generative AI and have the generative AI determine the priority of care plans.

[0096] The generation unit can select the optimal care plan by considering the pet's geographical location information when generating a care plan. For example, if the pet lives in a specific area, the generation unit can generate a care plan suitable for that area. For example, if the pet is traveling, the generation unit can also generate a care plan suitable for the environment of the travel destination. For example, if the pet moves, the generation unit can also generate a care plan suitable for the new area. By selecting a care plan while considering geographical location information, it is possible to provide care that takes into account area-specific risks. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input the pet's geographical location information data into the generation AI and have the generation AI select the optimal care plan.

[0097] The generation unit can analyze the pet owner's social media activity when generating a care plan and propose methods for the care plan. For example, if the owner shares information about the pet's health on social media, the generation unit can generate a care plan based on that information. For example, if the owner shares information about the pet's activities on social media, the generation unit can also generate a care plan based on that information. For example, if the owner shares information about the pet's meals on social media, the generation unit can also generate a care plan based on that information. In this way, by analyzing the owner's social media activity, it is possible to provide a care plan that is based on the pet's living situation. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the owner's social media activity into the generation AI and have the generation AI propose methods for the care plan.

[0098] The service provider can estimate the pet's emotions and adjust the method of providing the care plan based on the estimated emotions. For example, if the pet is relaxed, the service provider can provide a detailed care plan. If the pet is excited, the service provider can also provide a concise care plan. If the pet is stressed, the service provider can also present the care plan in a visually easy-to-understand manner. This allows the service provider to provide the optimal care plan for the pet by adjusting the method of provision based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into AI and have the AI ​​adjust the method of provision.

[0099] The service provider can select the optimal service method by referring to the pet's past care history when providing a care plan. For example, the service provider can select the optimal service method based on the pet's past care history. The service provider can also avoid certain care methods based on the pet's past care history. For example, the service provider can analyze the pet's past care history and select the most effective service method. This allows the service provider to provide the best possible care plan for the pet by selecting the optimal service method based on past care history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the pet's past care history data into AI and have the AI ​​select the optimal service method.

[0100] The service provider can estimate the pet's emotions and adjust the care plan delivery procedure based on the estimated emotions. For example, if the pet is relaxed, the service provider can provide a detailed delivery procedure. If the pet is excited, the service provider can also provide a concise delivery procedure. If the pet is stressed, the service provider can also visually represent the delivery procedure in an easy-to-understand way. This allows the service provider to provide the optimal care plan for the pet by adjusting the delivery procedure based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not. For example, the service provider can input pet emotion data into AI and have the AI ​​perform the adjustment of the delivery procedure.

[0101] The service provider can select the optimal service delivery method when providing a care plan, taking into account the pet owner's device information. For example, if the owner is using a smartphone, the service provider can provide a service delivery method adapted to the screen size. If the owner is using a tablet, the service provider can also provide a service delivery method optimized for a larger screen. If the owner is using a smartwatch, the service provider can also provide a concise and highly visible service delivery method. By selecting a service delivery method that takes the owner's device information into account, the service provider can provide the best possible care plan for the pet. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the owner's device information into the AI ​​and have the AI ​​select the optimal service delivery method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The pet care system can further collect pet behavioral data and provide behavioral modification plans based on the analysis results. For example, the collection unit collects data using sensors to monitor the pet's activity level and sleep patterns. The analysis unit analyzes the collected behavioral data to identify the pet's behavioral patterns and abnormal behaviors. The generation unit generates training plans and environmental adjustment plans to improve the pet's behavior based on the analysis results. The provision unit provides the generated behavioral modification plans to the pet owner to support the improvement of the pet's behavior. This allows for the early detection of pet behavioral problems and the implementation of appropriate countermeasures.

[0104] The pet care system can further estimate the pet's emotions and adjust the care plan based on those emotions. For example, the analysis unit analyzes the pet's facial expressions and tone of voice to estimate its emotions. The generation unit generates a care plan that will help the pet relax based on the estimated emotions. The provision unit provides the generated care plan to the owner and offers advice on how to reduce the pet's stress. In this way, the system can improve the pet's well-being by providing care that is tailored to the pet's emotional state.

[0105] The pet care system can further collect pet dietary data and provide a meal plan based on the analysis results. For example, the collection unit collects data using a device to record the pet's food content and intake. The analysis unit analyzes the collected dietary data to identify the pet's nutritional balance and dietary imbalances. The generation unit generates an optimal meal plan for the pet based on the analysis results. The delivery unit provides the generated meal plan to the owner, supporting the pet's health management. This allows for efficient management of the pet's diet and helps maintain its health.

[0106] The pet care system can further estimate the pet's emotions and adjust the exercise plan based on those emotions. For example, the analysis unit analyzes the pet's behavior and facial expressions to estimate its emotions. The generation unit generates an exercise plan that the pet will enjoy based on the estimated emotions. The provision unit provides the generated exercise plan to the owner and offers advice on how to address the pet's lack of exercise. By providing an exercise plan that matches the pet's emotional state, the system can reduce the pet's stress and maintain its health.

[0107] The pet care system can further collect pet health data and provide a health monitoring plan based on the analysis results. For example, the collection unit collects data using devices to monitor the pet's body temperature and heart rate. The analysis unit analyzes the collected health data and evaluates the pet's health status. The generation unit generates a plan for continuously monitoring the pet's health status based on the analysis results. The delivery unit provides the generated health monitoring plan to the owner, supporting pet health management. This allows owners to constantly monitor their pet's health status and detect abnormalities early.

[0108] The pet care system can further estimate the pet's emotions and adjust the health monitoring plan based on those estimated emotions. For example, the analysis unit analyzes the pet's behavior and facial expressions to estimate its emotions. The generation unit generates a health monitoring plan that helps the pet relax based on the estimated emotions. The provision unit provides the generated health monitoring plan to the owner and offers advice on how to reduce the pet's stress. In this way, by performing health monitoring according to the pet's emotional state, the pet's well-being can be improved.

[0109] The pet care system can further collect data on the pet's living environment and provide environmental improvement plans based on the analysis results. For example, the collection unit collects data using sensors to monitor the pet's living environment (temperature, humidity, noise level, etc.). The analysis unit analyzes the collected environmental data and identifies problems in the pet's living environment. The generation unit generates a plan to improve the pet's living environment based on the analysis results. The provision unit provides the generated environmental improvement plan to the pet owner and offers advice on optimizing the pet's living environment. This can improve the pet's living environment and enhance its health and well-being.

[0110] The pet care system can further estimate the pet's emotions and adjust the environmental improvement plan based on those emotions. For example, the analysis unit analyzes the pet's behavior and facial expressions to estimate its emotions. The generation unit generates an environmental improvement plan that will help the pet relax, based on the estimated emotions. The provision unit provides the generated environmental improvement plan to the owner and offers advice on how to reduce the pet's stress. In this way, by making environmental improvements that correspond to the pet's emotional state, the pet's happiness can be improved.

[0111] The pet care system can further collect social data about pets and provide socialization plans based on the analysis results. For example, the collection unit collects data using devices designed to collect data on the pet's interactions with other animals and people. The analysis unit analyzes the collected social data to identify the pet's social skills and any problems. The generation unit generates a plan to promote the pet's socialization based on the analysis results. The provision unit provides the generated socialization plan to the owner and offers advice on how to improve the pet's social skills. This helps improve the pet's social skills and build good relationships with other animals and people.

[0112] The pet care system can further estimate the pet's emotions and adjust the socialization plan based on those estimated emotions. For example, the analysis unit analyzes the pet's behavior and facial expressions to estimate its emotions. The generation unit generates a socialization plan that helps the pet relax based on the estimated emotions. The provision unit provides the generated socialization plan to the owner and offers advice on how to reduce the pet's stress. In this way, by providing a socialization plan that is tailored to the pet's emotional state, the pet's well-being can be improved.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The collection unit collects genetic information from pets. The collection unit collects genetic samples from sources such as pet saliva, hair, and blood, and sends them to an analysis laboratory. Step 2: The analysis unit analyzes the genetic information collected by the collection unit. For example, the analysis unit analyzes the genetic information to identify the pet's health status and genetic risks. Step 3: The generation unit generates a care plan based on the results analyzed by the analysis unit. For example, the generation unit generates a customized food, exercise, and care plan that is optimal for the pet based on the analysis results. Step 4: The provider provides the care plan generated by the generator. The provider can, for example, provide the generated care plan to the pet owner, either as a digital platform or as a printed document.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects a genetic sample of the pet using the camera 42 and microphone 38B of the smart device 14 and transmits it to an analysis laboratory via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the genetic information to identify the pet's health status and genetic risks. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a customized food, exercise, and care plan optimized for the pet based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the generated care plan to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects a genetic sample of the pet using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to an analysis laboratory via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the genetic information to identify the pet's health status and genetic risks. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a customized food, exercise, and care plan optimized for the pet based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the generated care plan to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects a genetic sample of the pet using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the analysis laboratory via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the genetic information to identify the pet's health status and genetic risks. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a customized food, exercise, and care plan optimized for the pet based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides the generated care plan to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects a genetic sample of the pet using the camera 42 and microphone 238 of the robot 414 and transmits it to the analysis laboratory via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the genetic information to identify the pet's health status and genetic risks. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates a customized food, exercise, and care plan optimized for the pet based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated care plan to the owner. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A collection unit that collects genetic information about pets, An analysis unit analyzes the genetic information collected by the aforementioned collection unit, A generation unit that generates a care plan based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the care plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect genetic samples from pet saliva, hair, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected genetic information is analyzed to identify the pet's health status and genetic risks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, we generate a customized food, exercise, and care plan optimized for your pet. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated care plan to the pet owner. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate a care plan tailored to your pet's age, weight, and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the pet's emotions and adjust the timing of gene sample collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the pet's past health data to select the optimal sample collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting genetic samples, filtering is performed based on the pet's current health status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the pet's emotions and determine the priority of samples to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting genetic samples, the geographical location of the pet is taken into consideration to prioritize the collection of highly relevant samples. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting genetic samples, we analyze the social media activity of pet owners and collect relevant samples. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the pet's emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the pet's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the genetic information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the pet's emotions and adjust the care plan generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a care plan, the system analyzes the pet's past health data to select the most suitable care plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a care plan, customize the care plan's methods based on the pet's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the pet's emotions and prioritizes care plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a care plan, the optimal care plan is selected by considering the pet's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating care plans, we analyze the social media activity of pet owners and propose methods for developing those plans. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the pet's emotions and adjust the care plan delivery method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a care plan, we will refer to the pet's past care history to select the most appropriate method of care. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the pet's emotions and adjusts the care plan delivery procedures based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing a care plan, the optimal delivery method will be selected considering the pet owner's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects genetic information about pets, An analysis unit analyzes the genetic information collected by the aforementioned collection unit, A generation unit that generates a care plan based on the results of the analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the care plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect genetic samples from pet saliva, hair, etc. The system according to feature 1.

3. The aforementioned analysis unit, The collected genetic information is analyzed to identify the pet's health status and genetic risks. The system according to feature 1.

4. The generating unit is Based on the analysis results, we generate a customized food, exercise, and care plan optimized for your pet. The system according to feature 1.

5. The aforementioned supply unit is, Provide the generated care plan to the pet owner. The system according to feature 1.

6. The generating unit is Generate a care plan tailored to your pet's age, weight, and activity level. The system according to feature 1.

7. The aforementioned collection unit is We estimate the pet's emotions and adjust the timing of gene sample collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the pet's past health data to select the optimal sample collection method. The system according to feature 1.

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