system

Abstract

The system according to this embodiment aims to provide an optimal meal plan tailored to each dog's individual health condition, age, and activity level. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an advice unit, and a delivery unit. The reception unit receives information from the owner regarding the dog's health condition, age, and activity level. The analysis unit analyzes the information received by the reception unit and generates a suitable meal plan for the dog. The advice unit allows veterinarians and nutrition specialists to customize the meal plan generated by the analysis unit. The delivery unit regularly delivers food based on the customized meal plan provided by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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, there was a problem that it was difficult to provide an optimal diet plan according to the individual health condition, age, and activity level of a dog.

[0005] The system according to the embodiment aims to provide an optimal diet plan according to the individual health condition, age, and activity level of a dog.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an advice unit, and a delivery unit. The reception unit receives information from the owner regarding the dog's health condition, age, and activity level. The analysis unit analyzes the information received by the reception unit and generates a suitable diet plan for the dog. The advice unit allows veterinarians and nutrition specialists to customize the diet plan generated by the analysis unit. The delivery unit regularly delivers food based on the customized diet plan provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal diet plan tailored to each dog's individual health condition, age, and activity level. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 health management system according to an embodiment of the present invention is a system that provides a meal plan optimized according to the individual health condition, age, and activity level of each dog. This system provides nutritionally balanced meals and is customized to address the specific needs of dogs, such as allergies, weight management, and specific health problems. Subscribers can select the best food for their dog through individual consultations with veterinarians and nutrition specialists and have it delivered to their home regularly. This service makes it easier for pet owners to maintain their pet's health. For example, owners input information such as their dog's health condition, age, and activity level. For example, if the dog has allergies, that information is also entered. This information is analyzed by AI, and an optimal meal plan for the dog is generated. Next, the generated meal plan is reviewed through individual consultations with veterinarians and nutrition specialists. The specialists provide further detailed advice based on the plan generated by the AI. For example, customizations such as adding special nutrients for dogs with specific health problems are made. Finally, food is delivered to the home regularly based on the customized meal plan. Owners receive the regularly delivered food and provide it to their dogs. This makes it easier for owners to maintain their dog's health. This service can continuously monitor a dog's health and update its diet plan as needed. For example, if a dog gains weight, the diet plan can be adjusted to manage its weight. Regular health checks also allow the service to understand the dog's health status and provide an optimal diet plan. In this way, by providing an optimal diet plan tailored to each dog's individual health condition and needs, owners can more easily maintain their pet's health. Thus, the pet health management system provides an optimal diet plan based on the dog's health condition, age, and activity level, making it easier for owners to maintain their pet's health.

[0029] The pet health management system according to this embodiment comprises a reception unit, an analysis unit, an advice unit, and a delivery unit. The reception unit receives information from the owner regarding the dog's health status, age, and activity level. This information may include, but is not limited to, the dog's weight, body temperature, exercise level, and walking frequency. The reception unit, for example, stores the information entered by the owner in a database and transmits it to the analysis unit. The analysis unit uses AI to analyze the information received by the reception unit and generates a suitable meal plan for the dog. The analysis unit generates a nutritionally balanced meal plan based on, for example, the dog's health status, age, and activity level. The analysis unit can use AI to generate an optimal meal plan according to the dog's health status. For example, the analysis unit uses an AI model that takes data such as the dog's weight, body temperature, and exercise level as input and outputs an optimal meal plan to generate a meal plan. The advice unit allows veterinarians and nutrition specialists to customize the meal plan based on the meal plan generated by the analysis unit. The advice unit can customize the plan for dogs with specific health problems, for example, by adding special nutrients. The advice unit allows veterinarians and nutrition specialists to provide more detailed advice based on the AI-generated meal plan. For example, the advice unit can customize the meal plan to exclude specific ingredients, taking into account the dog's allergy information. The delivery unit regularly delivers food based on the customized meal plan provided by the advice unit. For example, the delivery unit selects the necessary food based on the customized meal plan and delivers it regularly to the owner's home. The delivery unit can manage the food delivery schedule and notify the owner. For example, the delivery unit can notify the owner in advance of the food delivery date and prepare for delivery. This allows the pet health management system according to the embodiment to provide an optimal meal plan tailored to the dog's health condition, age, and activity level, making it easier for owners to maintain their pet's health.

[0030] The monitoring unit can continuously monitor the dog's health and update the feeding plan as needed. For example, the monitoring unit periodically collects data such as the dog's weight, body temperature, and activity level to monitor its health. For instance, it periodically measures the dog's weight and records any changes in weight. It can also periodically measure the dog's body temperature and record any changes. Furthermore, it can measure the dog's activity level using sensors and record the amount of exercise and the frequency of walks. For example, the monitoring unit can monitor the dog's activity level in real time using a sensor attached to the dog's collar. This allows the monitoring unit to continuously understand the dog's health and update the feeding plan as needed. For example, if the dog's weight increases, the monitoring unit adjusts the feeding plan to manage its weight. Also, if the dog's body temperature is abnormally high, the monitoring unit can adjust the feeding plan and take measures to improve its health. This allows the monitoring unit to continuously optimize the feeding plan according to the dog's health. Some or all of the above processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input data such as the dog's weight, body temperature, and activity level into the AI, which can then perform an analysis of the dog's health status.

[0031] The reception desk can analyze the owner's past input history and provide the optimal input interface. For example, the reception desk can store information previously entered by the owner in a database and analyze that data. For example, the reception desk can automatically display information that the owner has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the owner has used in the past. Furthermore, the reception desk can predict and suggest information to be used during specific time periods based on the owner's past input history. For example, the reception desk can prioritize displaying information related to a specific time period based on information the owner has entered during that time period in the past. This allows the reception desk to provide the optimal input interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input interface.

[0032] The reception unit can dynamically change input fields based on the dog's current health status and activity level when receiving information. For example, the reception unit can collect data on the dog's health status and activity level in real time and dynamically change input fields based on that data. For example, if the dog's health status is deteriorating, the reception unit can prompt for the input of detailed health information. Also, if the dog's activity level is high, the reception unit can display additional input fields regarding exercise. Furthermore, if the dog is older, the reception unit can add input fields regarding specific health problems. For example, if the dog is older, the reception unit can add input fields regarding joint health. This allows the reception unit to provide input fields that are appropriate for the dog's health status and activity level. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the dog's health status and activity level data collected in real time into the AI ​​and have the AI ​​perform the dynamic changes to the input fields.

[0033] The reception desk can prioritize obtaining highly relevant information based on the owner's geographical location when information is received. For example, the reception desk can obtain the owner's geographical location using GPS data or location services. For example, if the owner lives in a specific area, the reception desk can prioritize obtaining health information related to that area. Also, if the owner is traveling, the reception desk can prioritize obtaining information based on the climate and environment of the travel destination. Furthermore, if the owner is planning to move, the reception desk can also prioritize obtaining information related to the new area. For example, the reception desk can obtain and provide health information related to the owner's new area in advance. This allows the reception desk to provide highly relevant information based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input geographical location data into AI and have the AI ​​perform the acquisition of highly relevant information.

[0034] The reception desk can analyze the owner's social media activity and obtain relevant information upon receiving information. For example, the reception desk can analyze the owner's social media activity and obtain relevant information. For example, the reception desk can obtain information related to the dog's health condition based on information shared by the owner on social media. The reception desk can also refer to advice from experts that the owner follows on social media. Furthermore, the reception desk can obtain relevant health information based on information from groups the owner participates in on social media. For example, the reception desk can analyze information from pet care groups the owner participates in and provide relevant health information. In this way, the reception desk can provide relevant information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input social media activity data into AI and have the AI ​​perform the acquisition of relevant information.

[0035] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health condition during the analysis. For example, the analysis unit collects data on the dog's health condition and adjusts the level of detail of the analysis based on that data. For example, if the dog's health condition is deteriorating, the analysis unit will perform a detailed analysis. Conversely, if the dog's health condition is good, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can focus its analysis on specific health problems of the dog. For example, the analysis unit can evaluate the importance of the health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail of the analysis. This allows the analysis unit to provide the optimal level of detail of the analysis according to the dog's health condition. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the dog's health condition data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0036] The analysis unit can apply different analysis algorithms depending on the dog's category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the analysis unit can apply an analysis algorithm related to growth. Also, in the case of a senior dog, the analysis unit can apply an analysis algorithm related to aging. Furthermore, the analysis unit can apply analysis algorithms related to breed-specific health problems for specific breeds. For example, the analysis unit can perform an analysis on genetic health problems for a specific breed. This allows the analysis unit to provide the optimal analysis algorithm according to the dog's category. 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 dog category data into AI and have the AI ​​select an appropriate analysis algorithm.

[0037] The analysis unit can determine the priority of analysis based on when the dog's information was submitted. For example, the analysis unit can record the submission date of the dog's health information in a database and determine the priority of analysis based on that data. For example, the analysis unit can prioritize analysis of dog health information that has been recently submitted. Conversely, the analysis unit can postpone analysis of dog health information that is old. Furthermore, the analysis unit can also prioritize the analysis of information submitted within a specific period if the information was submitted within that period. For example, the analysis unit can prioritize analysis based on health information submitted within a specific period. This allows the analysis unit to provide an optimal analysis priority based on the information submission date. 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 submission date data into AI and have the AI ​​determine the analysis priority.

[0038] The analysis unit can adjust the order of analysis based on the relevance of the dogs during the analysis. For example, the analysis unit evaluates relevance based on data such as the dogs' health status and activity level, and adjusts the order of analysis. For example, if a dog's health status is poor, the analysis unit will prioritize the analysis. Conversely, if a dog's health status is good, the analysis unit can postpone the analysis. Furthermore, the analysis unit can focus the analysis on specific health problems of the dogs. For example, the analysis unit evaluates relevance based on data such as the dogs' weight, body temperature, and activity level, and adjusts the order of analysis. This allows the analysis unit to provide an optimal analysis order based on the dogs' relevance. 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 relevance data into AI and have the AI ​​perform the adjustment of the analysis order.

[0039] The advice unit can adjust the level of detail in its advice based on the importance of the dog's health condition. For example, the advice unit can collect data on the dog's health condition and adjust the level of detail based on that data. For instance, if the dog's health condition is deteriorating, the advice unit can provide detailed advice. Conversely, if the dog's health condition is good, the advice unit can provide simplified advice. Furthermore, the advice unit can provide focused advice on specific health problems of the dog. For example, the advice unit can assess the importance of the dog's health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail in its advice. This allows the advice unit to provide the optimal level of detail in its advice according to the dog's health condition. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input the dog's health condition data into an AI and have the AI ​​adjust the level of detail in its advice.

[0040] The advice unit can apply different advice algorithms depending on the dog's category when providing advice. For example, the advice unit can apply different advice algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the advice unit can apply an advice algorithm related to growth. Also, in the case of a senior dog, the advice unit can apply an advice algorithm related to aging. Furthermore, the advice unit can apply advice algorithms related to breed-specific health problems for specific breeds. For example, the advice unit can provide advice on genetic health problems for specific breeds. This allows the advice unit to provide the optimal advice algorithm according to the dog's category. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input dog category data into AI and have the AI ​​select an appropriate advice algorithm.

[0041] The advice unit can determine the priority of advice based on when the dog's information is submitted. For example, the advice unit can record the submission date of the dog's health information in a database and determine the priority of advice based on that data. For example, the advice unit can prioritize advice if the dog's health information has been recently submitted. Conversely, the advice unit can postpone providing advice if the dog's health information is old. Furthermore, the advice unit can also prioritize advice based on information submitted within a specific period. For example, the advice unit can prioritize advice based on health information submitted within a specific period. This allows the advice unit to provide the optimal advice priority based on when the information was submitted. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input submission date data into AI and have the AI ​​determine the priority of advice.

[0042] The advice unit can adjust the order of advice based on the dog's relevance when providing advice. For example, the advice unit evaluates relevance based on data such as the dog's health status and activity level, and adjusts the order of advice. For example, if the dog's health status is deteriorating, the advice unit will provide advice preferentially. Conversely, if the dog's health status is good, the advice unit can provide advice later. Furthermore, the advice unit can provide focused advice on specific health problems of the dog. For example, the advice unit evaluates relevance based on data such as the dog's weight, body temperature, and activity level, and adjusts the order of advice. This allows the advice unit to provide an optimal order of advice based on the dog's relevance. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input relevance data into AI and have the AI ​​perform the adjustment of the order of advice.

[0043] The delivery department can adjust the level of detail in delivery based on the importance of the dog's health condition. For example, the delivery department can collect data on the dog's health condition and adjust the level of detail based on that data. For example, if the dog's health condition is deteriorating, the delivery department can provide detailed delivery information. Conversely, if the dog's health condition is good, the delivery department can provide simplified delivery information. Furthermore, the delivery department can provide focused delivery information for specific health problems of the dog. For example, the delivery department can assess the importance of the dog's health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail accordingly. This allows the delivery department to provide the optimal level of detail for each dog's health condition. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input dog health condition data into AI and have the AI ​​perform the adjustment of the level of detail in delivery.

[0044] The delivery unit can apply different delivery algorithms depending on the dog's category during delivery. For example, the delivery unit can apply different delivery algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the delivery unit can apply a delivery algorithm related to growth. Also, in the case of a senior dog, the delivery unit can apply a delivery algorithm related to aging. Furthermore, the delivery unit can apply a delivery algorithm related to breed-specific health problems for specific breeds. For example, the delivery unit can perform deliveries related to genetic health problems for specific breeds. This allows the delivery unit to provide the optimal delivery algorithm according to the dog's category. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input dog category data into AI and have the AI ​​select an appropriate delivery algorithm.

[0045] The delivery department can adjust the delivery order based on when the dog's information was submitted. For example, the delivery department can record the submission date of the dog's health information in a database and adjust the delivery order based on that data. For example, the delivery department can prioritize deliveries if the dog's health information has been recently submitted. Conversely, the delivery department can postpone deliveries if the dog's health information is old. Furthermore, the delivery department can also prioritize deliveries of dog health information submitted within a specific period. For example, the delivery department can prioritize deliveries based on health information submitted within a specific period. This allows the delivery department to provide an optimal delivery order based on when the information was submitted. Some or all of the above processing in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input submission date data into AI and have the AI ​​perform the adjustment of the delivery order.

[0046] The delivery department can adjust the delivery order based on the relevance of the dogs during delivery. The delivery department can evaluate relevance and adjust the delivery order based on data such as the dogs' health status and activity level. For example, the delivery department can prioritize deliveries of dogs whose health is deteriorating. Conversely, it can postpone deliveries of dogs whose health is good. Furthermore, the delivery department can also focus deliveries on dogs with specific health problems. For example, the delivery department can evaluate relevance and adjust the delivery order based on data such as the dogs' weight, body temperature, and activity level. This allows the delivery department to provide an optimal delivery order based on the dogs' relevance. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input relevance data into AI and have the AI ​​perform the adjustment of the delivery order.

[0047] The monitoring unit can adjust the level of detail of monitoring based on the importance of the dog's health condition during monitoring. For example, the monitoring unit collects data on the dog's health condition and adjusts the level of detail of monitoring based on that data. For example, if the dog's health condition is deteriorating, the monitoring unit will perform detailed monitoring. Conversely, if the dog's health condition is good, the monitoring unit can perform simplified monitoring. Furthermore, the monitoring unit can also focus monitoring on specific health problems of the dog. For example, the monitoring unit can evaluate the importance of the health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail of monitoring. This allows the monitoring unit to provide the optimal level of detail of monitoring according to the dog's health condition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the dog's health condition data into AI and have the AI ​​perform the adjustment of the level of detail of monitoring.

[0048] The monitoring unit can apply different monitoring algorithms depending on the dog's category during monitoring. For example, the monitoring unit can apply different monitoring algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the monitoring unit can apply a monitoring algorithm related to growth. In the case of a senior dog, the monitoring unit can apply a monitoring algorithm related to aging. Furthermore, the monitoring unit can apply a monitoring algorithm related to breed-specific health problems for specific breeds. For example, the monitoring unit can perform monitoring related to genetic health problems for specific breeds. This allows the monitoring unit to provide the optimal monitoring algorithm according to the dog's category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input dog category data into AI and have the AI ​​select an appropriate monitoring algorithm.

[0049] The monitoring unit can adjust the monitoring order based on when the dog's information was submitted. For example, the monitoring unit can record the submission date of the dog's health information in a database and adjust the monitoring order based on that data. For example, the monitoring unit can prioritize monitoring if the dog's health information has been recently submitted. Conversely, the monitoring unit can postpone monitoring if the dog's health information is old. Furthermore, the monitoring unit can prioritize monitoring information submitted within a specific period if that period was observed. For example, the monitoring unit can prioritize monitoring based on health information submitted within a specific period. This allows the monitoring unit to provide an optimal monitoring order based on the information submission date. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input submission date data into AI and have the AI ​​perform the adjustment of the monitoring order.

[0050] The monitoring unit can adjust the order of monitoring based on the relevance of the dogs during monitoring. For example, the monitoring unit evaluates relevance based on data such as the dogs' health status and activity level, and adjusts the order of monitoring. For example, if a dog's health status is deteriorating, the monitoring unit will prioritize monitoring. Conversely, if a dog's health status is good, the monitoring unit can postpone monitoring. Furthermore, the monitoring unit can focus monitoring on specific health problems of the dogs. For example, the monitoring unit evaluates relevance based on data such as the dogs' weight, body temperature, and activity level, and adjusts the order of monitoring. This allows the monitoring unit to provide an optimal monitoring order based on the dogs' relevance. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevance data into AI and have the AI ​​perform the adjustment of the monitoring order.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze the owner's past purchase history and propose the optimal meal plan. For example, the reception desk stores the types and frequency of food purchased by the owner in the past in a database and customizes the meal plan based on that data. The reception desk can also collect ratings of food purchased by the owner in the past and prioritize suggesting highly-rated foods. Furthermore, the reception desk can analyze the ingredient information of food purchased by the owner in the past and suggest foods that address allergies or specific health problems. This allows the reception desk to provide the optimal meal plan based on the owner's past purchase history.

[0053] The monitoring unit can analyze a dog's behavioral patterns and detect abnormal behavior. For example, it collects data such as a dog's walking patterns, food intake, and sleep duration, and compares them to normal behavioral patterns. It can also analyze the frequency of a dog's barking and movements to detect signs of stress or anxiety. Furthermore, it can detect sudden changes in a dog's activity level, enabling early detection of health abnormalities. In this way, the monitoring unit can detect abnormal behavior based on a dog's behavioral patterns and support early health management.

[0054] The reception desk can analyze the owner's past input history and provide optimal reminders. For example, based on information the owner has previously entered, the reception desk can remind owners about regular health checks and changes in their pet's diet. It can also automatically remind owners of information they tend to forget in the past. Furthermore, the reception desk can provide reminders at the optimal time, tailored to the owner's schedule. This allows the reception desk to provide optimal reminders based on the owner's past input history.

[0055] The reception system can dynamically change input fields based on the dog's current health status and activity level when information is received. For example, if the dog's health is deteriorating, the reception system will prompt for detailed health information. If the dog's activity level is high, the reception system can display additional input fields regarding exercise. Furthermore, if the dog is older, the reception system can add input fields related to specific health problems. This allows the reception system to provide input fields tailored to the dog's health status and activity level.

[0056] The reception desk can prioritize retrieving highly relevant information based on the owner's geographical location when information is received. For example, if the owner lives in a specific area, the reception desk will prioritize retrieving health information related to that area. If the owner is traveling, the reception desk can prioritize retrieving information based on the climate and environment of their destination. Furthermore, if the owner is planning to move, the reception desk can prioritize retrieving information related to their new area. This allows the reception desk to provide highly relevant information based on geographical location.

[0057] The reception desk can analyze the owner's social media activity when receiving information and obtain relevant information. For example, the reception desk can obtain information related to the dog's health condition based on information shared by the owner on social media. The reception desk can also refer to advice from experts the owner follows on social media. Furthermore, the reception desk can obtain relevant health information based on information from groups the owner participates in on social media. This allows the reception desk to provide relevant information based on social media activity.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health condition during the analysis. For example, if the dog's health condition is deteriorating, the analysis unit will perform a detailed analysis. Conversely, if the dog's health condition is good, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can focus its analysis on specific health problems in the dog. In this way, the analysis unit can provide the optimal level of detail of analysis according to the dog's health condition.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives information from owners about their dog's health, age, and activity level. This information includes, for example, the dog's weight, body temperature, exercise level, and walking frequency. The reception desk stores the information entered by the owner in a database and sends it to the analysis department. Step 2: The analysis unit uses AI to analyze the information received by the reception unit and generate a suitable meal plan for the dog. The analysis unit generates a nutritionally balanced meal plan based on the dog's health condition, age, and activity level. For example, it uses an AI model that takes data such as the dog's weight, body temperature, and exercise level as input and outputs the optimal meal plan. Step 3: The advice department customizes the meal plan generated by the analysis department, with the help of veterinarians and nutrition specialists. For example, they may add special nutrients for dogs with specific health problems. The advice department also customizes the meal plan to exclude certain ingredients, taking into account the dog's allergy information. Step 4: The delivery department delivers food regularly based on the customized meal plan developed by the advisory department. The delivery department selects the necessary food based on the customized meal plan and delivers it regularly to the owner's home. The delivery department can manage the food delivery schedule and notify the owner.

[0061] (Example of form 2) The pet health management system according to an embodiment of the present invention is a system that provides a meal plan optimized according to the individual health condition, age, and activity level of each dog. This system provides nutritionally balanced meals and is customized to address the specific needs of dogs, such as allergies, weight management, and specific health problems. Subscribers can select the best food for their dog through individual consultations with veterinarians and nutrition specialists and have it delivered to their home regularly. This service makes it easier for pet owners to maintain their pet's health. For example, owners input information such as their dog's health condition, age, and activity level. For example, if the dog has allergies, that information is also entered. This information is analyzed by AI, and an optimal meal plan for the dog is generated. Next, the generated meal plan is reviewed through individual consultations with veterinarians and nutrition specialists. The specialists provide further detailed advice based on the plan generated by the AI. For example, customizations such as adding special nutrients for dogs with specific health problems are made. Finally, food is delivered to the home regularly based on the customized meal plan. Owners receive the regularly delivered food and provide it to their dogs. This makes it easier for owners to maintain their dog's health. This service can continuously monitor a dog's health and update its diet plan as needed. For example, if a dog gains weight, the diet plan can be adjusted to manage its weight. Regular health checks also allow the service to understand the dog's health status and provide an optimal diet plan. In this way, by providing an optimal diet plan tailored to each dog's individual health condition and needs, owners can more easily maintain their pet's health. Thus, the pet health management system provides an optimal diet plan based on the dog's health condition, age, and activity level, making it easier for owners to maintain their pet's health.

[0062] The pet health management system according to this embodiment comprises a reception unit, an analysis unit, an advice unit, and a delivery unit. The reception unit receives information from the owner regarding the dog's health status, age, and activity level. This information may include, but is not limited to, the dog's weight, body temperature, exercise level, and walking frequency. The reception unit, for example, stores the information entered by the owner in a database and transmits it to the analysis unit. The analysis unit uses AI to analyze the information received by the reception unit and generates a suitable meal plan for the dog. The analysis unit generates a nutritionally balanced meal plan based on, for example, the dog's health status, age, and activity level. The analysis unit can use AI to generate an optimal meal plan according to the dog's health status. For example, the analysis unit uses an AI model that takes data such as the dog's weight, body temperature, and exercise level as input and outputs an optimal meal plan to generate a meal plan. The advice unit allows veterinarians and nutrition specialists to customize the meal plan based on the meal plan generated by the analysis unit. The advice unit can customize the plan for dogs with specific health problems, for example, by adding special nutrients. The advice unit allows veterinarians and nutrition specialists to provide more detailed advice based on the AI-generated meal plan. For example, the advice unit can customize the meal plan to exclude specific ingredients, taking into account the dog's allergy information. The delivery unit regularly delivers food based on the customized meal plan provided by the advice unit. For example, the delivery unit selects the necessary food based on the customized meal plan and delivers it regularly to the owner's home. The delivery unit can manage the food delivery schedule and notify the owner. For example, the delivery unit can notify the owner in advance of the food delivery date and prepare for delivery. This allows the pet health management system according to the embodiment to provide an optimal meal plan tailored to the dog's health condition, age, and activity level, making it easier for owners to maintain their pet's health.

[0063] The monitoring unit can continuously monitor the dog's health and update the feeding plan as needed. For example, the monitoring unit periodically collects data such as the dog's weight, body temperature, and activity level to monitor its health. For instance, it periodically measures the dog's weight and records any changes in weight. It can also periodically measure the dog's body temperature and record any changes. Furthermore, it can measure the dog's activity level using sensors and record the amount of exercise and the frequency of walks. For example, the monitoring unit can monitor the dog's activity level in real time using a sensor attached to the dog's collar. This allows the monitoring unit to continuously understand the dog's health and update the feeding plan as needed. For example, if the dog's weight increases, the monitoring unit adjusts the feeding plan to manage its weight. Also, if the dog's body temperature is abnormally high, the monitoring unit can adjust the feeding plan and take measures to improve its health. This allows the monitoring unit to continuously optimize the feeding plan according to the dog's health. Some or all of the above processing in the monitoring unit may be performed using AI, or without AI. For example, the monitoring unit can input data such as the dog's weight, body temperature, and activity level into the AI, which can then perform an analysis of the dog's health status.

[0064] The reception unit can estimate the owner's emotions and adjust the information input method based on the estimated emotions. For example, the reception unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and adjust the input method. The reception unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the input method. The reception unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and adjust the input method. This allows the reception unit to provide the optimal information input method according to the owner's emotions. For example, if the owner is stressed, the reception unit can provide a simple interface and minimize the input procedure. Furthermore, if the owner is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. If the owner is in a hurry, the reception desk can prioritize voice input to allow for quick information entry. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input image data of the owner captured by a camera into a generative AI and have the generative AI perform emotion estimation of the owner.

[0065] The reception desk can analyze the owner's past input history and provide the optimal input interface. For example, the reception desk can store information previously entered by the owner in a database and analyze that data. For example, the reception desk can automatically display information that the owner has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the owner has used in the past. Furthermore, the reception desk can predict and suggest information to be used during specific time periods based on the owner's past input history. For example, the reception desk can prioritize displaying information related to a specific time period based on information the owner has entered during that time period in the past. This allows the reception desk to provide the optimal input interface based on past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input interface.

[0066] The reception unit can dynamically change input fields based on the dog's current health status and activity level when receiving information. For example, the reception unit can collect data on the dog's health status and activity level in real time and dynamically change input fields based on that data. For example, if the dog's health status is deteriorating, the reception unit can prompt for the input of detailed health information. Also, if the dog's activity level is high, the reception unit can display additional input fields regarding exercise. Furthermore, if the dog is older, the reception unit can add input fields regarding specific health problems. For example, if the dog is older, the reception unit can add input fields regarding joint health. This allows the reception unit to provide input fields that are appropriate for the dog's health status and activity level. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the dog's health status and activity level data collected in real time into the AI ​​and have the AI ​​perform the dynamic changes to the input fields.

[0067] The reception unit can estimate the owner's emotions and determine the priority of inputs based on the estimated emotions. For example, the reception unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and determine the priority of inputs. The reception unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of inputs. The reception unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and determine the priority of inputs. This allows the reception unit to provide the optimal input priority according to the owner's emotions. For example, if the owner is stressed, the reception unit will prioritize inputting the most important information. If the owner is relaxed, the reception unit can prompt for inputting more detailed information. Furthermore, if the owner is in a hurry, the reception desk can prioritize inputting only the minimum necessary information. Emotion estimation is achieved using an emotion estimation function, for example, with 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input image data of the owner taken by a camera into a generative AI and have the generative AI perform an estimation of the owner's emotions.

[0068] The reception desk can prioritize obtaining highly relevant information based on the owner's geographical location when information is received. For example, the reception desk can obtain the owner's geographical location using GPS data or location services. For example, if the owner lives in a specific area, the reception desk can prioritize obtaining health information related to that area. Also, if the owner is traveling, the reception desk can prioritize obtaining information based on the climate and environment of the travel destination. Furthermore, if the owner is planning to move, the reception desk can also prioritize obtaining information related to the new area. For example, the reception desk can obtain and provide health information related to the owner's new area in advance. This allows the reception desk to provide highly relevant information based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input geographical location data into AI and have the AI ​​perform the acquisition of highly relevant information.

[0069] The reception desk can analyze the owner's social media activity and obtain relevant information upon receiving information. For example, the reception desk can analyze the owner's social media activity and obtain relevant information. For example, the reception desk can obtain information related to the dog's health condition based on information shared by the owner on social media. The reception desk can also refer to advice from experts that the owner follows on social media. Furthermore, the reception desk can obtain relevant health information based on information from groups the owner participates in on social media. For example, the reception desk can analyze information from pet care groups the owner participates in and provide relevant health information. In this way, the reception desk can provide relevant information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input social media activity data into AI and have the AI ​​perform the acquisition of relevant information.

[0070] The analysis unit can estimate the owner's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the presentation of the analysis. The analysis unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the presentation of the analysis. The analysis unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the presentation of the analysis. This allows the analysis unit to provide optimal analysis results tailored to the owner's emotions. For example, if the owner is tense, the analysis unit provides simple and easy-to-understand analysis results. If the owner is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the owner is in a hurry, the analysis unit can provide concise analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health condition during the analysis. For example, the analysis unit collects data on the dog's health condition and adjusts the level of detail of the analysis based on that data. For example, if the dog's health condition is deteriorating, the analysis unit will perform a detailed analysis. Conversely, if the dog's health condition is good, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can focus its analysis on specific health problems of the dog. For example, the analysis unit can evaluate the importance of the health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail of the analysis. This allows the analysis unit to provide the optimal level of detail of the analysis according to the dog's health condition. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the dog's health condition data into AI and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the dog's category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the analysis unit can apply an analysis algorithm related to growth. Also, in the case of a senior dog, the analysis unit can apply an analysis algorithm related to aging. Furthermore, the analysis unit can apply analysis algorithms related to breed-specific health problems for specific breeds. For example, the analysis unit can perform an analysis on genetic health problems for a specific breed. This allows the analysis unit to provide the optimal analysis algorithm according to the dog's category. 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 dog category data into AI and have the AI ​​select an appropriate analysis algorithm.

[0073] The analysis unit can estimate the owner's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions and adjust the length of the analysis. The analysis unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the analysis. The analysis unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the analysis. This allows the analysis unit to provide an optimal length of analysis result according to the owner's emotions. For example, if the owner is in a hurry, the analysis unit will provide a short, concise analysis result. If the owner is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the owner is excited, the analysis unit can provide analysis results with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 without AI. For example, the analysis unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0074] The analysis unit can determine the priority of analysis based on when the dog's information was submitted. For example, the analysis unit can record the submission date of the dog's health information in a database and determine the priority of analysis based on that data. For example, the analysis unit can prioritize analysis of dog health information that has been recently submitted. Conversely, the analysis unit can postpone analysis of dog health information that is old. Furthermore, the analysis unit can also prioritize the analysis of information submitted within a specific period if the information was submitted within that period. For example, the analysis unit can prioritize analysis based on health information submitted within a specific period. This allows the analysis unit to provide an optimal analysis priority based on the information submission date. 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 submission date data into AI and have the AI ​​determine the analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the dogs during the analysis. For example, the analysis unit evaluates relevance based on data such as the dogs' health status and activity level, and adjusts the order of analysis. For example, if a dog's health status is poor, the analysis unit will prioritize the analysis. Conversely, if a dog's health status is good, the analysis unit can postpone the analysis. Furthermore, the analysis unit can focus the analysis on specific health problems of the dogs. For example, the analysis unit evaluates relevance based on data such as the dogs' weight, body temperature, and activity level, and adjusts the order of analysis. This allows the analysis unit to provide an optimal analysis order based on the dogs' relevance. 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 relevance data into AI and have the AI ​​perform the adjustment of the analysis order.

[0076] The advice unit can estimate the owner's emotions and adjust the way it presents advice based on those emotions. For example, the advice unit can capture the owner's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and adjust the way it presents advice. The advice unit can also record the owner's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice, calculate an emotion score, and adjust the way it presents advice. Furthermore, the advice unit can collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on fluctuations in heart rate and adjust the way it presents advice. This allows the advice unit to provide optimal advice tailored to the owner's emotions. For example, if the owner is stressed, the advice unit can provide simple and easily understandable advice. If the owner is relaxed, the advice unit can provide detailed advice. Furthermore, if the owner is in a hurry, the advice unit can provide concise advice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input image data of the owner taken by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0077] The advice unit can adjust the level of detail in its advice based on the importance of the dog's health condition. For example, the advice unit can collect data on the dog's health condition and adjust the level of detail based on that data. For instance, if the dog's health condition is deteriorating, the advice unit can provide detailed advice. Conversely, if the dog's health condition is good, the advice unit can provide simplified advice. Furthermore, the advice unit can provide focused advice on specific health problems of the dog. For example, the advice unit can assess the importance of the dog's health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail in its advice. This allows the advice unit to provide the optimal level of detail in its advice according to the dog's health condition. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input the dog's health condition data into an AI and have the AI ​​adjust the level of detail in its advice.

[0078] The advice unit can apply different advice algorithms depending on the dog's category when providing advice. For example, the advice unit can apply different advice algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the advice unit can apply an advice algorithm related to growth. Also, in the case of a senior dog, the advice unit can apply an advice algorithm related to aging. Furthermore, the advice unit can apply advice algorithms related to breed-specific health problems for specific breeds. For example, the advice unit can provide advice on genetic health problems for specific breeds. This allows the advice unit to provide the optimal advice algorithm according to the dog's category. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input dog category data into AI and have the AI ​​select an appropriate advice algorithm.

[0079] The advice unit can estimate the owner's emotions and adjust the length of the advice based on the estimated emotions. For example, the advice unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the advice unit can calculate an emotion score based on changes in facial expressions and adjust the length of the advice. The advice unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the advice unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the advice. The advice unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the advice unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the advice. This allows the advice unit to provide the optimal length of advice according to the owner's emotions. For example, if the owner is in a hurry, the advice unit will provide short, concise advice. If the owner is relaxed, the advice unit can provide detailed advice. Furthermore, if the owner is excited, the advice unit can provide advice with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input image data of the owner taken by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0080] The advice unit can determine the priority of advice based on when the dog's information is submitted. For example, the advice unit can record the submission date of the dog's health information in a database and determine the priority of advice based on that data. For example, the advice unit can prioritize advice if the dog's health information has been recently submitted. Conversely, the advice unit can postpone providing advice if the dog's health information is old. Furthermore, the advice unit can also prioritize advice based on information submitted within a specific period. For example, the advice unit can prioritize advice based on health information submitted within a specific period. This allows the advice unit to provide the optimal advice priority based on when the information was submitted. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input submission date data into AI and have the AI ​​determine the priority of advice.

[0081] The advice unit can adjust the order of advice based on the dog's relevance when providing advice. For example, the advice unit evaluates relevance based on data such as the dog's health status and activity level, and adjusts the order of advice. For example, if the dog's health status is deteriorating, the advice unit will provide advice preferentially. Conversely, if the dog's health status is good, the advice unit can provide advice later. Furthermore, the advice unit can provide focused advice on specific health problems of the dog. For example, the advice unit evaluates relevance based on data such as the dog's weight, body temperature, and activity level, and adjusts the order of advice. This allows the advice unit to provide an optimal order of advice based on the dog's relevance. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input relevance data into AI and have the AI ​​perform the adjustment of the order of advice.

[0082] The delivery unit can estimate the owner's emotions and adjust the delivery timing based on those emotions. For example, the delivery unit can capture the owner's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the delivery unit can calculate an emotion score based on changes in facial expressions and adjust the delivery timing. The delivery unit can also record the owner's voice and estimate their emotions using voice analysis technology. For example, the delivery unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the delivery timing. Furthermore, the delivery unit can collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the delivery unit can calculate an emotion score based on fluctuations in heart rate and adjust the delivery timing. This allows the delivery unit to provide the optimal delivery timing according to the owner's emotions. For example, if the owner is stressed, the delivery unit will make an expedited delivery. Conversely, if the owner is relaxed, the delivery unit can maintain the normal delivery schedule. Furthermore, if the owner is in a hurry, the delivery department can prioritize same-day delivery. 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 delivery department may be performed using AI, or not using AI. For example, the delivery department can input image data of the owner taken by a camera into a generative AI and have the generative AI perform the estimation of the owner's emotions.

[0083] The delivery department can adjust the level of detail in delivery based on the importance of the dog's health condition. For example, the delivery department can collect data on the dog's health condition and adjust the level of detail based on that data. For example, if the dog's health condition is deteriorating, the delivery department can provide detailed delivery information. Conversely, if the dog's health condition is good, the delivery department can provide simplified delivery information. Furthermore, the delivery department can provide focused delivery information for specific health problems of the dog. For example, the delivery department can assess the importance of the dog's health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail accordingly. This allows the delivery department to provide the optimal level of detail for each dog's health condition. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input dog health condition data into AI and have the AI ​​perform the adjustment of the level of detail in delivery.

[0084] The delivery unit can apply different delivery algorithms depending on the dog's category during delivery. For example, the delivery unit can apply different delivery algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the delivery unit can apply a delivery algorithm related to growth. Also, in the case of a senior dog, the delivery unit can apply a delivery algorithm related to aging. Furthermore, the delivery unit can apply a delivery algorithm related to breed-specific health problems for specific breeds. For example, the delivery unit can perform deliveries related to genetic health problems for specific breeds. This allows the delivery unit to provide the optimal delivery algorithm according to the dog's category. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input dog category data into AI and have the AI ​​select an appropriate delivery algorithm.

[0085] The delivery unit can estimate the owner's emotions and determine delivery priorities based on those estimated emotions. For example, the delivery unit can capture the owner's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the delivery unit can calculate an emotion score based on changes in facial expressions and determine delivery priorities. The delivery unit can also record the owner's voice and estimate their emotions using voice analysis technology. For example, the delivery unit can analyze the tone and speed of the voice, calculate an emotion score, and determine delivery priorities. Furthermore, the delivery unit can collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the delivery unit can calculate an emotion score based on fluctuations in heart rate and determine delivery priorities. This allows the delivery unit to provide optimal delivery priorities according to the owner's emotions. For example, if the owner is stressed, the delivery unit will prioritize the delivery. Conversely, if the owner is relaxed, the delivery unit can maintain the normal delivery schedule. Furthermore, if the owner is in a hurry, the delivery department can prioritize same-day delivery. 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 delivery department may be performed using AI, or not using AI. For example, the delivery department can input image data of the owner taken by a camera into a generative AI and have the generative AI perform the estimation of the owner's emotions.

[0086] The delivery department can adjust the delivery order based on when the dog's information was submitted. For example, the delivery department can record the submission date of the dog's health information in a database and adjust the delivery order based on that data. For example, the delivery department can prioritize deliveries if the dog's health information has been recently submitted. Conversely, the delivery department can postpone deliveries if the dog's health information is old. Furthermore, the delivery department can also prioritize deliveries of dog health information submitted within a specific period. For example, the delivery department can prioritize deliveries based on health information submitted within a specific period. This allows the delivery department to provide an optimal delivery order based on when the information was submitted. Some or all of the above processing in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input submission date data into AI and have the AI ​​perform the adjustment of the delivery order.

[0087] The delivery department can adjust the delivery order based on the relevance of the dogs during delivery. The delivery department can evaluate relevance and adjust the delivery order based on data such as the dogs' health status and activity level. For example, the delivery department can prioritize deliveries of dogs whose health is deteriorating. Conversely, it can postpone deliveries of dogs whose health is good. Furthermore, the delivery department can also focus deliveries on dogs with specific health problems. For example, the delivery department can evaluate relevance and adjust the delivery order based on data such as the dogs' weight, body temperature, and activity level. This allows the delivery department to provide an optimal delivery order based on the dogs' relevance. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input relevance data into AI and have the AI ​​perform the adjustment of the delivery order.

[0088] The monitoring unit can estimate the owner's emotions and adjust the monitoring method based on the estimated emotions. For example, the monitoring unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions and adjust the monitoring method. The monitoring unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the monitoring method. The monitoring unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on fluctuations in heart rate and adjust the monitoring method. As a result, the monitoring unit can provide an optimal monitoring method according to the owner's emotions. For example, if the owner is tense, the monitoring unit can provide a simple and highly visible monitoring method. If the owner is relaxed, the monitoring unit can provide a detailed monitoring method. Furthermore, if the owner is in a hurry, the monitoring unit can provide a concise monitoring method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0089] The monitoring unit can adjust the level of detail of monitoring based on the importance of the dog's health condition during monitoring. For example, the monitoring unit collects data on the dog's health condition and adjusts the level of detail of monitoring based on that data. For example, if the dog's health condition is deteriorating, the monitoring unit will perform detailed monitoring. Conversely, if the dog's health condition is good, the monitoring unit can perform simplified monitoring. Furthermore, the monitoring unit can also focus monitoring on specific health problems of the dog. For example, the monitoring unit can evaluate the importance of the health condition based on data such as the dog's weight, body temperature, and activity level, and adjust the level of detail of monitoring. This allows the monitoring unit to provide the optimal level of detail of monitoring according to the dog's health condition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the dog's health condition data into AI and have the AI ​​perform the adjustment of the level of detail of monitoring.

[0090] The monitoring unit can apply different monitoring algorithms depending on the dog's category during monitoring. For example, the monitoring unit can apply different monitoring algorithms depending on the dog's category (puppy, senior dog, specific breed, etc.). For example, in the case of a puppy, the monitoring unit can apply a monitoring algorithm related to growth. In the case of a senior dog, the monitoring unit can apply a monitoring algorithm related to aging. Furthermore, the monitoring unit can apply a monitoring algorithm related to breed-specific health problems for specific breeds. For example, the monitoring unit can perform monitoring related to genetic health problems for specific breeds. This allows the monitoring unit to provide the optimal monitoring algorithm according to the dog's category. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input dog category data into AI and have the AI ​​select an appropriate monitoring algorithm.

[0091] The monitoring unit can estimate the owner's emotions and determine monitoring priorities based on the estimated emotions. For example, the monitoring unit can capture the owner's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions and determine monitoring priorities. The monitoring unit can also record the owner's voice and estimate emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice, calculate an emotion score, and determine monitoring priorities. The monitoring unit can also collect the owner's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on fluctuations in heart rate and determine monitoring priorities. This allows the monitoring unit to provide optimal monitoring priorities according to the owner's emotions. For example, if the owner is stressed, the monitoring unit will prioritize monitoring. If the owner is relaxed, the monitoring unit can maintain the normal monitoring schedule. Furthermore, if the owner is in a hurry, the monitoring unit can perform monitoring quickly. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input image data of the owner captured by a camera into the generative AI and have the generative AI perform the estimation of the owner's emotions.

[0092] The monitoring unit can adjust the monitoring order based on when the dog's information was submitted. For example, the monitoring unit can record the submission date of the dog's health information in a database and adjust the monitoring order based on that data. For example, the monitoring unit can prioritize monitoring if the dog's health information has been recently submitted. Conversely, the monitoring unit can postpone monitoring if the dog's health information is old. Furthermore, the monitoring unit can prioritize monitoring information submitted within a specific period if that period was observed. For example, the monitoring unit can prioritize monitoring based on health information submitted within a specific period. This allows the monitoring unit to provide an optimal monitoring order based on the information submission date. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input submission date data into AI and have the AI ​​perform the adjustment of the monitoring order.

[0093] The monitoring unit can adjust the order of monitoring based on the relevance of the dogs during monitoring. For example, the monitoring unit evaluates relevance based on data such as the dogs' health status and activity level, and adjusts the order of monitoring. For example, if a dog's health status is deteriorating, the monitoring unit will prioritize monitoring. Conversely, if a dog's health status is good, the monitoring unit can postpone monitoring. Furthermore, the monitoring unit can focus monitoring on specific health problems of the dogs. For example, the monitoring unit evaluates relevance based on data such as the dogs' weight, body temperature, and activity level, and adjusts the order of monitoring. This allows the monitoring unit to provide an optimal monitoring order based on the dogs' relevance. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevance data into AI and have the AI ​​perform the adjustment of the monitoring order. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, advice unit, and delivery unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives information from the owner. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a suitable meal plan for the dog using AI. The advice unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and allows veterinarians and nutrition specialists to customize the plan. The delivery unit is implemented by, for example, the control unit 46A of the smart device 14 and delivers food regularly based on the customized meal plan. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, advice unit, and delivery unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives information from the owner. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a suitable meal plan for the dog using AI. The advice unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and is customized by veterinarians and nutrition specialists. The delivery unit is implemented, for example, by the control unit 46A of the smart glasses 214 and delivers food regularly based on the customized meal plan. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, advice unit, and delivery unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives information from the owner. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a suitable meal plan for the dog using AI. The advice unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and allows veterinarians and nutrition specialists to customize the plan. The delivery unit is implemented by, for example, the control unit 46A of the headset terminal 314 and delivers food regularly based on the customized meal plan. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, advice unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives information from the owner. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and generates a suitable meal plan for the dog using AI. The advice unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and is customized by veterinarians and nutrition specialists. The delivery unit is implemented by, for example, the control unit 46A of the robot 414 and delivers food regularly based on the customized meal plan.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze the owner's past purchase history and propose the optimal meal plan. For example, the reception desk stores the types and frequency of food purchased by the owner in the past in a database and customizes the meal plan based on that data. The reception desk can also collect ratings of food purchased by the owner in the past and prioritize suggesting highly-rated foods. Furthermore, the reception desk can analyze the ingredient information of food purchased by the owner in the past and suggest foods that address allergies or specific health problems. This allows the reception desk to provide the optimal meal plan based on the owner's past purchase history.

[0096] The monitoring unit can analyze a dog's behavioral patterns and detect abnormal behavior. For example, it collects data such as a dog's walking patterns, food intake, and sleep duration, and compares them to normal behavioral patterns. It can also analyze the frequency of a dog's barking and movements to detect signs of stress or anxiety. Furthermore, it can detect sudden changes in a dog's activity level, enabling early detection of health abnormalities. In this way, the monitoring unit can detect abnormal behavior based on a dog's behavioral patterns and support early health management.

[0097] The reception desk can estimate the owner's emotions and adjust its customer support response based on those estimates. For example, if the owner is stressed, the reception desk can provide quick and concise support. If the owner is relaxed, it can provide detailed explanations and additional advice. Furthermore, if the owner is anxious, it can provide empathetic support that provides reassurance. In this way, the reception desk can provide optimal customer support tailored to the owner's emotions.

[0098] The reception desk can analyze the owner's past input history and provide optimal reminders. For example, based on information the owner has previously entered, the reception desk can remind owners about regular health checks and changes in their pet's diet. It can also automatically remind owners of information they tend to forget in the past. Furthermore, the reception desk can provide reminders at the optimal time, tailored to the owner's schedule. This allows the reception desk to provide optimal reminders based on the owner's past input history.

[0099] The reception system can dynamically change input fields based on the dog's current health status and activity level when information is received. For example, if the dog's health is deteriorating, the reception system will prompt for detailed health information. If the dog's activity level is high, the reception system can display additional input fields regarding exercise. Furthermore, if the dog is older, the reception system can add input fields related to specific health problems. This allows the reception system to provide input fields tailored to the dog's health status and activity level.

[0100] The reception system can estimate the owner's emotions and determine input priorities based on those estimates. For example, if the owner is stressed, the reception system will prioritize inputting the most important information. If the owner is relaxed, it can encourage inputting more detailed information. If the owner is in a hurry, it can prioritize inputting only the essential information. In this way, the reception system can provide the optimal input priority according to the owner's emotions.

[0101] The reception desk can prioritize retrieving highly relevant information based on the owner's geographical location when information is received. For example, if the owner lives in a specific area, the reception desk will prioritize retrieving health information related to that area. If the owner is traveling, the reception desk can prioritize retrieving information based on the climate and environment of their destination. Furthermore, if the owner is planning to move, the reception desk can prioritize retrieving information related to their new area. This allows the reception desk to provide highly relevant information based on geographical location.

[0102] The reception desk can analyze the owner's social media activity when receiving information and obtain relevant information. For example, the reception desk can obtain information related to the dog's health condition based on information shared by the owner on social media. The reception desk can also refer to advice from experts the owner follows on social media. Furthermore, the reception desk can obtain relevant health information based on information from groups the owner participates in on social media. This allows the reception desk to provide relevant information based on social media activity.

[0103] The analysis unit can estimate the owner's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the owner is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the owner is relaxed, it can provide detailed analysis results. If the owner is in a hurry, it can provide concise analysis results. In this way, the analysis unit can provide optimal analysis results that match the owner's emotions.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the dog's health condition during the analysis. For example, if the dog's health condition is deteriorating, the analysis unit will perform a detailed analysis. Conversely, if the dog's health condition is good, the analysis unit can perform a simplified analysis. Furthermore, the analysis unit can focus its analysis on specific health problems in the dog. In this way, the analysis unit can provide the optimal level of detail of analysis according to the dog's health condition.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives information from owners about their dog's health, age, and activity level. This information includes, for example, the dog's weight, body temperature, exercise level, and walking frequency. The reception desk stores the information entered by the owner in a database and sends it to the analysis department. Step 2: The analysis unit uses AI to analyze the information received by the reception unit and generate a suitable meal plan for the dog. The analysis unit generates a nutritionally balanced meal plan based on the dog's health condition, age, and activity level. For example, it uses an AI model that takes data such as the dog's weight, body temperature, and exercise level as input and outputs the optimal meal plan. Step 3: The advice department customizes the meal plan generated by the analysis department, with the help of veterinarians and nutrition specialists. For example, they may add special nutrients for dogs with specific health problems. The advice department also customizes the meal plan to exclude certain ingredients, taking into account the dog's allergy information. Step 4: The delivery department delivers food regularly based on the customized meal plan developed by the advisory department. The delivery department selects the necessary food based on the customized meal plan and delivers it regularly to the owner's home. The delivery department can manage the food delivery schedule and notify the owner.

[0107] 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.

[0108] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0109] 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.

[0110] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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. 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.

[0125] 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.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 width of a typical healthy person's field of vision).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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. 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.

[0141] 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.

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

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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. 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.

[0158] 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.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of Symbols]

[0179] 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 reception desk that receives information from owners about their dog's health, age, and activity level, An analysis unit analyzes the information received by the reception unit and generates a suitable meal plan for dogs, Based on the meal plan generated by the aforementioned analysis unit, an advice unit is provided by a veterinarian or nutrition specialist to customize the plan. A delivery unit that regularly delivers food based on a meal plan customized by the aforementioned advice unit, Equipped with A system characterized by the following features.

2. It also includes a monitoring unit to continuously monitor the dog's health and update the meal plan as needed. The system according to feature 1.

3. The aforementioned reception unit is The system estimates the owner's emotions and adjusts the information input method based on the estimated emotions. The system according to feature 1.

4. The aforementioned reception unit is It analyzes the owner's past input history and provides the optimal input interface. The system according to feature 1.

5. The aforementioned reception unit is When receiving information, the input fields are dynamically changed based on the dog's current health status and activity level. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the owner's emotions and determines the priority of inputs based on the estimated owner's emotions. The system according to feature 1.

7. The aforementioned reception unit is When information is received, the system prioritizes retrieving highly relevant information based on the owner's geographical location. The system according to feature 1.

8. The aforementioned reception unit is When information is received, the system analyzes the owner's social media activity and retrieves relevant information. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the owner's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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