system

Abstract

We provide the system. [Solution] A means of receiving natural language data related to a pet's health problems, Information processing means for analyzing the aforementioned natural language data to estimate the health status of a pet, A means for generating first aid instructions based on estimated health status, A means for presenting the user with instructions for the aforementioned first aid, A means to identify the nearest medical institution and assist with making an appointment, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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] Pets cannot directly complain about their own health conditions, and it is difficult for pet owners to quickly detect poor health and take appropriate measures. In particular, at night or outside the business hours of animal hospitals, pet owners have limited immediate response means to protect the health of their pets and often feel psychological stress. Under such circumstances, there is a need for a support system that enables pet owners to quickly and accurately take emergency measures and send their pets to appropriate medical institutions for treatment.

Means for Solving the Problems

[0005] This invention provides an information processing means that receives information related to a pet's poor health from a user in natural language, analyzes that data, and estimates the pet's health condition. Based on the estimated health condition, it generates emergency treatment and presents instructions to the user, thereby supporting the owner's quick response. It also provides a system that includes means for identifying the nearest medical facility and supporting reservations. This system allows pet owners to receive appropriate medical care while reducing their anxiety.

[0006] "Natural language data" refers to textual information expressed by users in normal conversational or written form, specifically descriptions of a pet's symptoms or condition.

[0007] "Information processing means" refers to a device or program that analyzes received natural language data and performs computational processing to estimate the health status of a pet.

[0008] "First aid instructions" are specific guidelines that provide users with recommended temporary treatments and care methods based on their pet's health condition.

[0009] A "medical institution" is a facility that can provide health checkups and treatment for pets, and includes animal hospitals and clinics.

[0010] "Means of supporting reservations" refers to functions and methods for making reservations for medical treatment at the nearest selected medical institution. [Brief explanation of the drawing]

[0011] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] 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.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0023] 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.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] The 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.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention is a system that helps pet owners respond quickly and accurately when they notice their pet is unwell. This system is implemented via terminals, servers, and a network.

[0033] First, users use devices such as smartphones or computers to input natural language data about their pet's health. For example, a user might input something like, "My cat is lethargic and not eating."

[0034] The device sends this input to the server via the communication network. The server analyzes the received text data using natural language processing (NLP) techniques to extract important keywords and phrases. Based on the extracted information, the server uses a machine learning model to estimate the pet's health status. In this estimation, characteristic information such as the pet's age, sex, and medical history is also taken into consideration.

[0035] Based on the health status estimated by the server, recommended first aid measures are determined. For example, if dehydration is likely, "give more water" will be suggested as first aid. These first aid instructions are sent from the server to the terminal and displayed to the user. The user can then use this information to administer first aid to their pet.

[0036] Furthermore, this system has the functionality to identify nearby veterinary hospitals and present their location and contact information to the user. If the user wishes to make an appointment, the system has the functionality to assist the hospital with the booking process. This allows users to visit a medical institution quickly and easily.

[0037] In this way, the present invention provides a practical solution for achieving both the health of pets and the psychological well-being of users.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users use devices such as smartphones or computers to input detailed natural language data about their pet's health. This data describes the symptoms the pet is exhibiting. For example, "The dog has lost its appetite and has diarrhea."

[0041] Step 2:

[0042] The terminal formats the data entered by the user into a digital format and creates an API request to send it to the server over the network.

[0043] Step 3:

[0044] The server receives the transmitted input data. Next, it uses a natural language processing (NLP) engine to extract important keywords and related phrases from the input data. This extracts the information necessary for analysis.

[0045] Step 4:

[0046] The server uses extracted keywords and pet characteristic information (age, sex, medical history, etc.) provided by the user in advance to run an AI model and estimate the pet's health status. This estimation also includes checking whether similar symptoms exist in the past database.

[0047] Step 5:

[0048] The server uses AI to select the most appropriate first aid based on the estimated health status. The content of the first aid is determined taking into account factors such as the pet's species and its usual health condition.

[0049] Step 6:

[0050] The server compiles information, including diagnostic results and details of first aid, and sends it to the terminal. This information includes immediate action to take for the pet.

[0051] Step 7:

[0052] The terminal displays information received from the server on the user interface, assisting the user in taking appropriate action. This includes reassuring explanations and specific procedures.

[0053] Step 8:

[0054] If the user wishes to seek medical attention as a further measure, the device will provide information on the nearest animal hospital. If a reservation support function is available, the device will send a reservation request to the selected hospital via the server.

[0055] This series of steps enables users to quickly and appropriately manage their pet's health and receive support for diagnosis and treatment at a medical facility when necessary.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] There is a need for a system that can help pet owners quickly and accurately identify when their pets are unwell and support them in providing appropriate first aid or visiting medical facilities. However, currently, accurately diagnosing a pet's illness requires specialized knowledge, and there is a problem in that rapid response is difficult, especially in emergencies. Therefore, there is a need to develop a user-friendly and effective support system that allows pet owners to easily assess their pet's health and take appropriate measures.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for acquiring natural language data relating to the health status of a pet, means for information processing means to analyze the natural language data using natural language processing technology and extract important keywords, and means for estimating the health status of the pet using a machine learning model based on the extracted important keywords. This allows pet owners to quickly recognize when their pet is unwell, receive instructions for appropriate first aid, and easily make reservations at medical facilities if necessary.

[0061] "Health status" refers to the physical and physiological condition of a pet, and is determined from its normal activity, eating habits, and other behaviors.

[0062] "Natural language data" refers to information expressed in natural language entered by the user, and is text data that describes the symptoms and behavior of pets.

[0063] "Information processing means" refers to systems and processes for analyzing received data, and methods for interpreting data using natural language processing techniques and machine learning models.

[0064] "Key terms" refer to words and phrases extracted from natural language data that are particularly meaningful in estimating a pet's health status.

[0065] A "machine learning model" is an algorithm or method that makes predictions and classifications based on past data. It learns patterns from the data and is used to estimate the health status of pets.

[0066] "First aid" refers to initial measures that can be taken quickly when a pet becomes ill, and are implemented before professional diagnosis or treatment.

[0067] A "medical facility" is a specialized institution that provides health checkups and treatment for pets, and includes facilities such as animal hospitals and clinics.

[0068] "Appointment booking" refers to the process of setting a date and time to visit a medical facility, and is carried out through a system as needed.

[0069] This invention provides a support system that enables pet owners to quickly recognize when their pets are unwell and take appropriate action. This system operates via terminals, servers, and a network, and utilizes natural language processing technology and machine learning.

[0070] First, users use devices such as smartphones or computers to input information about their pet's health in natural language. This input is expected to include specific symptoms and behaviors. For example, a user might input something like, "My cat has lost its appetite and is sleeping frequently."

[0071] The terminal transmits the collected data to the server via the network. The server analyzes the received natural language data using natural language processing libraries (e.g., spaCy or TENSORFLOW®). This analysis extracts important keywords and phrases.

[0072] The server uses this extracted information to estimate the pet's health status using a machine learning model (e.g., a model using Scikit-learn or PyTorch). This process also takes into account the pet's characteristics, such as age, sex, and medical history.

[0073] Based on the estimation results, the server generates appropriate first-aid instructions and sends that information to the terminal. The terminal displays the received instructions on the screen and prompts the user to take specific action.

[0074] Furthermore, the server uses location information to identify nearby medical facilities and, if necessary, assists with the booking process at medical institutions. This allows users to effectively manage their pets' health and receive appropriate medical services quickly.

[0075] When using this system, the system displays prompts such as, "Please enter information about your pet's health. For example, 'My dog ​​has been lethargic since this morning,'" to support the input of necessary data. This allows pet owners to easily record their pet's condition, enabling detailed analysis on the server side.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users input natural language data about their pet's health using a device. Specifically, they input text such as, "My dog ​​has been lethargic and has no appetite since this morning," through the device's input interface. This allows the user's observational information to be collected as digital data on the device.

[0079] Step 2:

[0080] The terminal receives natural language data entered by the user and prepares to send it to the server. The entered string data is converted into packets according to the network protocol and securely transferred to the server via the communication network.

[0081] Step 3:

[0082] The server analyzes the received text data using natural language processing techniques. First, it tokenizes the received data and extracts important keywords and phrases using a natural language processing library (e.g., spaCy) to analyze the language structure. The input is natural language text data, and the output is the important words and phrases extracted as features.

[0083] Step 4:

[0084] The server uses a machine learning model to estimate the pet's health status based on extracted key keywords. This prediction takes into account pre-stored characteristic information such as the pet's age, sex, and medical history. The input consists of key keywords and characteristic information, and the output is the estimated health status of the pet. Specifically, a classification model is executed using libraries such as Scikit-learn.

[0085] Step 5:

[0086] The server determines appropriate first aid based on the estimated health status. For example, it generates specific instructions such as "give more water." The input is the estimated health status, and the output is the first aid instruction.

[0087] Step 6:

[0088] The server sends first aid instructions to the terminal. The terminal displays these instructions on the user's screen. The user can then use this information to administer first aid to their pet. This allows the user to quickly and effectively take appropriate action.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] There is a need for a system that not only provides information to enable quick and appropriate first aid when a pet becomes ill, but also immediately suggests necessary related products and allows for quick purchase procedures. Such a system is expected to reduce the burden of maintaining pet health and increase convenience for pet owners.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes a device for receiving natural language data related to a pet's poor health, an information processing device for analyzing the natural language data and predicting the pet's health condition, a device for creating first aid instructions based on the predicted health condition, a device for suggesting relevant products according to the results of the analysis of the pet's health condition, and a device for enabling the purchase of suggested products through electronic payment. This enables the rapid provision of first aid instructions and the suggestion and purchase of relevant products.

[0094] "Natural language data related to pet health problems" refers to data in which pet owners express their observations and concerns about their pets' health in natural language.

[0095] An "information processing device" is a device that analyzes input data, understands its meaning and intent based on that data, and determines the next processing step.

[0096] "Predicting health status" means estimating a pet's health status using a specialized algorithm based on its current physical condition and past information.

[0097] "First aid instructions" are directives that specifically outline the immediate actions and responses necessary to maintain or restore a pet's health.

[0098] A "device that suggests related products" is a device that allows users to easily select and present appropriate products and services according to their pet's health condition and problems.

[0099] A "device that enables purchase via electronic payment" is a device that allows the proposed product to be purchased quickly through a specific online payment system.

[0100] The system for implementing this invention consists of a user's terminal, a server, and an associated network. The user uses a terminal such as a smartphone or computer to input natural language data regarding their pet's health condition. This data is transmitted from the terminal to the server via a communication network.

[0101] The server first analyzes the received natural language data using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and Hugging Face Transformers are used to extract important keywords and phrases from the data. Based on the extracted information, the server uses machine learning algorithms to predict the pet's health status. This prediction uses machine learning frameworks such as TensorFlow and PyTorch, and also takes into account characteristic information such as the pet's age, sex, and medical history.

[0102] Once a health status prediction is made, the server generates first-aid instructions based on the results and sends them to the user's terminal. Following these instructions, the user can perform first aid on their pet. Furthermore, the server suggests and presents relevant products based on the pet's health condition. This relevant product information is obtained using shopping APIs such as Amazon and Rakuten. Users can purchase these products through electronic payment systems, such as Stripe and PayPal.

[0103] As a concrete example of its use, suppose a user inputs "My cat is drinking water frequently." In this case, the server will use the input data to assess the potential risk of diabetes and suggest purchasing diabetes-friendly food or testing kits.

[0104] Examples of prompts for a generative AI model include the following:

[0105] "Please suggest products I should buy to improve my pet's health. The situation is as follows: 'My cat is drinking water frequently.'"

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] Users input natural language data about their pet's health issues using a smartphone or computer. This data reflects the user's observations and concerns (e.g., "My cat is drinking water frequently"). The input data is stored on the device and transmitted to a server via the network.

[0109] Step 2:

[0110] The server analyzes the received natural language data using natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers). This analysis extracts important keywords and phrases from the text. Based on the input data, it identifies elements related to specific health conditions. The analysis results provide information that suggests potential health problems in pets.

[0111] Step 3:

[0112] The server applies machine learning algorithms (e.g., TensorFlow, PyTorch) to predict the health status using the extracted information. Pet characteristics (age, sex, medical history, etc.) are also considered. Based on the input information, the health status is output as a numerical value or diagnostic result. This output is then used to construct instructions for first aid.

[0113] Step 4:

[0114] The server generates first-aid instructions based on the predicted health condition. These instructions (e.g., "Give more fluids") are sent to the user's device, allowing the user to immediately take specific action.

[0115] Step 5:

[0116] The server retrieves the most suitable related products based on the pet's health condition via shopping APIs (e.g., Amazon, Rakuten Market). Product information, such as test kits or specific pet food, is suggested. The user can view the suggested products on their device.

[0117] Step 6:

[0118] When a user wishes to make a purchase, the process of purchasing the goods through an electronic payment system (e.g., Stripe, PayPal) is initiated. The server processes the payment information and guides the user through the steps to complete the transaction. In this way, the purchase process is completed smoothly.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] This invention is a system that combines natural language data related to a pet's health problems with an emotion engine that recognizes the user's emotions. When a user inputs their pet's symptoms, the system can detect the user's emotional state from the words and context, and dynamically adapt the style of emergency treatment and information presentation accordingly.

[0121] To use the system, users first input natural language data about their pet's health using a terminal. This data is in the form of a description of how the pet is feeling unwell. The input includes the pet's name, symptoms, and any visible abnormalities.

[0122] The terminal sends the input data to the server, which uses natural language processing technology to extract important symptom keywords from the data. In addition, an emotion engine picks up emotional hints from the user's input and analyzes the user's emotions, such as whether they are tense or calm.

[0123] Based on the analysis results, the server uses an AI model to estimate the pet's health status. This process also takes into account the pet's pre-registered characteristics (age, sex, medical history, etc.).

[0124] Based on estimations, optimal first-aid instructions are generated, utilizing the results of the emotion engine's analysis. For example, if the user is feeling very anxious, the server will provide instructions with more detailed and reassuring explanations. These first-aid instructions and information on recommended medical facilities are presented to the user on their device.

[0125] If the user wishes to seek further medical attention, the device will identify the nearest veterinary hospital and provide assistance with making an appointment. The emotion engine also adjusts the guidance based on the user's current emotional state, providing a smooth and reassuring experience.

[0126] This system enables flexible adaptation of the user interface, which was difficult with conventional systems, resulting in more personalized support. Users will be able to use the system with peace of mind and confidence, in addition to managing their pets' health.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users input information about their pet's symptoms and condition into the device using natural language. For example, they might provide specific details such as, "My cat has lost its appetite and is lethargic."

[0130] Step 2:

[0131] The terminal prepares to send the input natural language data to the server via the communication network. During this process, the data is converted to an appropriate format and sent to the server as an API request.

[0132] Step 3:

[0133] The server receives natural language data from the terminal. The server then uses a natural language processing engine to analyze the data and detect important keywords and phrases related to the pet's symptoms.

[0134] Step 4:

[0135] Simultaneously, the server uses an emotion engine to detect the user's emotions from natural language data. This allows it to determine the user's emotional state, such as whether they are anxious, calm, or restless.

[0136] Step 5:

[0137] The server uses an AI model to estimate the pet's health status based on the analyzed data and the user's emotional state. Pet characteristics (e.g., age, sex, medical history) are taken into consideration during this process.

[0138] Step 6:

[0139] Based on the estimated health status, the server generates first-aid instructions. Using the results of the emotion engine, the instructions are adjusted to the user's emotional state. For example, if the user is anxious, more detailed explanations are added to provide greater reassurance.

[0140] Step 7:

[0141] The server transmits first-aid instructions and medical condition information to the terminal. The terminal displays this information on its user interface, clearly presenting it to the user.

[0142] Step 8:

[0143] When a user wishes to visit a medical institution, the device communicates this to the server. The server identifies the nearest animal hospital, provides the user with that information, and also offers a reservation assistance function.

[0144] Through this series of processes, the system aims to support pet health management and the user's emotional well-being.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In today's world, where prompt and appropriate responses to pet illnesses are essential, it's crucial to ensure that pet owners can cope with the situation with peace of mind, even when they are emotionally unstable. However, conventional systems fail to provide information that takes the owner's emotional state into account, and they do not fully utilize individual pet information, making it difficult to provide appropriate first aid.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes a device that receives natural language data related to the pet's poor health, an information processing device that extracts symptom keywords and analyzes the user's emotional state, and a computing device that estimates the pet's health condition based on the extracted keywords and emotional data. This enables personalized first aid instructions that are tailored to the user's emotional state.

[0150] "Natural language data" refers to textual data from users describing the current health status of their pets, including information indicating symptoms or abnormalities in the pets.

[0151] "Symptom keywords" are important words and phrases related to a pet's health condition, extracted from natural language data.

[0152] "User emotional state" refers to the mental state or emotional tendencies expressed when a user inputs natural language data.

[0153] An "information processing device" is a device or software used to analyze natural language data, extract symptom keywords, and analyze the emotional state of a user.

[0154] A "computational device" refers to a device or infrastructure with computing capabilities for estimating a pet's health status based on extracted symptom keywords and emotional data.

[0155] "First aid instructions" refer to information about the actions that should be taken immediately in response to a pet's illness, based on its estimated health condition.

[0156] A "medical facility" refers to a veterinary facility or hospital that provides medical care for pets.

[0157] This invention is a system aimed at quickly assessing a pet's health condition and providing owners with appropriate first aid and peace of mind.

[0158] The terminal first has a means of receiving natural language data entered by the user. This includes information processing devices such as smartphones and personal computers, and the user uses the terminal to input the pet's symptoms and related information. An example of the input content might be in the format of "My cat Milk is lethargic and has no appetite."

[0159] The terminal sends this natural language data to the server. The server, acting as an information processing device, first uses spaCy, a natural language processing library, to extract keywords related to the symptoms. It also uses NLTK for emotion analysis to analyze the user's emotional state. This allows the server to understand whether the user is anxious or calm.

[0160] Based on this information, the server uses the GPT-3® generative AI model to estimate the pet's health status, taking into account the extracted keywords and emotional state. The calculated results also incorporate the pet's pre-registration information, resulting in a more accurate estimate of the pet's health status.

[0161] The server generates first-aid instructions based on the estimated results and presents them to the user via the terminal. For example, if the user is feeling very anxious, it can add detailed and reassuring explanations such as, "Don't panic, warm up some milk, and let the baby rest for a while."

[0162] In addition, if a user wishes to visit a medical institution, the device will use their current location to identify the nearest medical facility and assist with making an appointment. The suggested medical facilities will also be presented in a way that takes the user's feelings into consideration.

[0163] This system overcomes the limitations of conventional information provision systems, enabling personalized, safe, and reliable medical support for users.

[0164] A concrete example of a prompt might be the input, "My dog, Coco, has been coughing since this morning. What should I do?" Based on this prompt, the generative AI model provides appropriate first aid and explanations.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user provides natural language input about their pet's health condition to the device. The device receives input such as, "My cat, Milk, is lethargic and has no appetite." This input is processed as the starting data for the entire system.

[0168] Step 2:

[0169] The terminal sends the received natural language input data to the server. This transmission takes place over the internet using secure communication methods. The next process begins as soon as the server receives the data.

[0170] Step 3:

[0171] The server analyzes the received natural language data using an information processing device. Using a natural language processing library (e.g., spaCy), symptom keywords such as "feeling unwell" and "loss of appetite" are extracted from the input text. The input for this process is the user's natural language data, and the output is a list of extracted keywords.

[0172] Step 4:

[0173] The server uses an emotion analysis engine to analyze the user's emotional state from their input. For example, a natural language processing library (such as NLTK) is used to extract emotions like "anxiety" and "worry." At this stage, the input is the user's natural language text, and the output is an index of their emotional state.

[0174] Step 5:

[0175] The server uses the extracted symptom keywords and emotional states as a dataset to estimate the pet's health status using a generative AI model (e.g., GPT-3). The input here is symptom keywords and emotional indicators, and the output is information about the estimated health status. This process allows for the planning of first aid measures appropriate to the pet's current condition.

[0176] Step 6:

[0177] The server generates first-aid instructions based on the estimation results and sends them to the terminal. The generated instructions include smooth and reassuring explanations that take the user's emotions into consideration. The input is information about the estimated health status, and the output is first-aid instructions presented to the user.

[0178] Step 7:

[0179] When a user wishes to visit a medical institution, the terminal identifies the nearest medical facility based on their current location. The server supports this by providing information to facilitate the booking process. The input here is the user's current location, and the output is information about the recommended nearest medical facility.

[0180] This process allows users to receive emotionally reassuring support and be guided to appropriate first aid and medical facilities.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] Providing appropriate solutions for pet health management while considering the owner's feelings is challenging. Furthermore, the lack of optimized food recommendations and purchasing procedures tailored to the pet's health condition highlights the need to improve the user experience. When a pet is unwell, owners often experience anxiety, and information and support are needed to alleviate this anxiety. Additionally, a system is desired that appropriately recommends the most suitable food for each pet's health condition and facilitates its purchase.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for receiving natural language data related to the pet's poor health, means for analyzing the user's emotional state and adjusting the information presentation style according to the emotional state, and means for recommending the most suitable food according to the pet's health condition. This makes it possible to provide appropriate first aid measures regarding pet health management, and to optimize the recommendation and purchase process of pet food, while taking into account the owner's emotions.

[0186] "Natural language data related to pet health problems" refers to information that pet owners have written themselves using language about unusual symptoms or signs that may affect their pet's health.

[0187] "Information processing means" refers to a computational function or software and hardware environment for analyzing received data and estimating the current health status of a pet.

[0188] "Means for generating first aid instructions" refers to a process or device for automatically creating and presenting appropriate initial responses to an estimated health condition.

[0189] "Means of presenting to the user" refers to a mechanism including a display device or audio output device for conveying generated information or instructions to the user visually or aurally.

[0190] "A means of identifying the nearest medical facility and assisting with reservations" refers to a process that uses information about the pet's health to determine the geographically closest animal hospital or other medical facility and provides an interface for making appointments.

[0191] "A means of analyzing a user's emotional state and adjusting the information presentation style according to that emotional state" refers to a system that evaluates the user's psychological state from the input data and dynamically changes the format and tone of information provision based on the results.

[0192] "A method for recommending the optimal food according to a pet's health condition" refers to a process that automatically selects and suggests foods and nutrients that are suitable for a pet's current health condition.

[0193] "Means of optimizing the purchase process" refers to a process that simplifies and makes purchase-related operations easier to use, based on the user's emotional state and other factors.

[0194] The system for implementing this invention mainly consists of a server and a user terminal. The user inputs natural language data about the pet's health using a smartphone application. This includes the pet's symptoms, changes in appearance, and changes in appetite. The data received by the terminal is transmitted to the server.

[0195] The server analyzes the input data using natural language processing software. For example, the Google Cloud Natural Language API is used. As a result of the analysis, keywords related to important symptoms are extracted, and basic data is generated to predict the pet's health status. In this process, pre-registered characteristic information about the pet is also taken into consideration. Generative AI models can be used in this process, with TensorFlow models being one example.

[0196] Furthermore, the server uses an emotion analysis engine to analyze the user's emotional state. This engine includes, for example, IBM Watson® Tone Analyzer. Based on the analysis results, it determines how anxious or relaxed the user is and applies this information to subsequent processes.

[0197] Based on a prediction of the pet's health condition, appropriate first-aid instructions are generated, and recommended pet food and supplements are selected according to the pet's health status. This information is presented in detail and in an appropriate tone on the user's device. If the user is feeling very anxious, more detailed explanations and advice are added to provide reassurance.

[0198] Furthermore, when a user initiates a purchase, the server optimizes the process to ensure a smooth flow. For example, if a pet has a decreased appetite, the server automatically recommends foods that stimulate appetite and supplements that aid digestion, providing a process that allows for easy purchase.

[0199] An example of a prompt message for a generative AI model would be: "This user is concerned about their pet's health. Based on the entered symptom information and acquired emotion data, please recommend pet food. The current emotion state is 'calm'."

[0200] This method allows users to manage their pets' health more easily and with greater peace of mind, while also providing them with information on the most suitable food for their pets.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user operates a smartphone application and inputs natural language data about their pet's health. This data includes the pet's symptoms and any changes in its appearance. This data is then transmitted to a server by the device.

[0204] Step 2:

[0205] The server analyzes the received natural language data using tools such as the Google Cloud Natural Language API. The input is text data describing the pet's symptoms, and the output is the extraction of keywords indicating important symptoms. This analysis processes and interprets the data, generating basic information for evaluating the pet's health status.

[0206] Step 3:

[0207] The server uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is the user's emotional expressions associated with natural language data, and the output is an evaluation of the user's emotional state. This process determines whether the user is anxious or calm and applies that information to subsequent processes.

[0208] Step 4:

[0209] The server uses a generative AI model to predict the pet's health status, taking into account the pet's pre-registered characteristic information. The input is the set of symptom keywords and characteristic information extracted in the previous step, and the output is predicted pet health status data. In this process, the AI ​​model estimates the health status through calculations of the data.

[0210] Step 5:

[0211] The server generates first-aid instructions and pet food recommendations based on the predicted health status. The inputs are the predicted health status and the user's emotional state, while the output is a specific first-aid document and a list of recommended pet foods to present to the user. The recommendations are tailored to take the user's emotions into consideration and to provide reassurance.

[0212] Step 6:

[0213] The terminal displays first-aid instructions and pet food recommendations sent from the server to the user. Input consists of instruction documents and recommendation lists from the server, while output is information presented in visual or auditory formats. The information is provided to the user in a user-friendly and easy-to-understand format.

[0214] Step 7:

[0215] When a user indicates their intention to purchase pet food, the server assists in facilitating the purchase process. The input is the user's purchase request information, and the output is an optimized purchase process. This allows users to complete their purchase easily and with peace of mind.

[0216] 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.

[0217] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0223] 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.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0225] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] 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.

[0227] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The 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.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention is a system that helps pet owners respond quickly and accurately when they notice their pet is unwell. This system is implemented via terminals, servers, and a network.

[0233] First, users use devices such as smartphones or computers to input natural language data about their pet's health. For example, a user might input something like, "My cat is lethargic and not eating."

[0234] The device sends this input to the server via the communication network. The server analyzes the received text data using natural language processing (NLP) techniques to extract important keywords and phrases. Based on the extracted information, the server uses a machine learning model to estimate the pet's health status. In this estimation, characteristic information such as the pet's age, sex, and medical history is also taken into consideration.

[0235] Based on the health status estimated by the server, recommended first aid measures are determined. For example, if dehydration is likely, "give more water" will be suggested as first aid. These first aid instructions are sent from the server to the terminal and displayed to the user. The user can then use this information to administer first aid to their pet.

[0236] Furthermore, this system has the functionality to identify nearby veterinary hospitals and present their location and contact information to the user. If the user wishes to make an appointment, the system has the functionality to assist the hospital with the booking process. This allows users to visit a medical institution quickly and easily.

[0237] In this way, the present invention provides a practical solution for achieving both the health of pets and the psychological well-being of users.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] Users use devices such as smartphones or computers to input detailed natural language data about their pet's health. This data describes the symptoms the pet is exhibiting. For example, "The dog has lost its appetite and has diarrhea."

[0241] Step 2:

[0242] The terminal formats the data entered by the user into a digital format and creates an API request to send it to the server over the network.

[0243] Step 3:

[0244] The server receives the transmitted input data. Next, it uses a natural language processing (NLP) engine to extract important keywords and related phrases from the input data. This extracts the information necessary for analysis.

[0245] Step 4:

[0246] The server uses extracted keywords and pet characteristic information (age, sex, medical history, etc.) provided by the user in advance to run an AI model and estimate the pet's health status. This estimation also includes checking whether similar symptoms exist in the past database.

[0247] Step 5:

[0248] The server uses AI to select the most appropriate first aid based on the estimated health status. The content of the first aid is determined taking into account factors such as the pet's species and its usual health condition.

[0249] Step 6:

[0250] The server compiles information, including diagnostic results and details of first aid, and sends it to the terminal. This information includes immediate action to take for the pet.

[0251] Step 7:

[0252] The terminal displays information received from the server on the user interface, assisting the user in taking appropriate action. This includes reassuring explanations and specific procedures.

[0253] Step 8:

[0254] If the user wishes to seek medical attention as a further measure, the device will provide information on the nearest animal hospital. If a reservation support function is available, the device will send a reservation request to the selected hospital via the server.

[0255] This series of steps enables users to quickly and appropriately manage their pet's health and receive support for diagnosis and treatment at a medical facility when necessary.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] There is a need for a system that can help pet owners quickly and accurately identify when their pets are unwell and support them in providing appropriate first aid or visiting medical facilities. However, currently, accurately diagnosing a pet's illness requires specialized knowledge, and there is a problem in that rapid response is difficult, especially in emergencies. Therefore, there is a need to develop a user-friendly and effective support system that allows pet owners to easily assess their pet's health and take appropriate measures.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes means for acquiring natural language data relating to the health status of a pet, means for information processing means to analyze the natural language data using natural language processing technology and extract important keywords, and means for estimating the health status of the pet using a machine learning model based on the extracted important keywords. This allows pet owners to quickly recognize when their pet is unwell, receive instructions for appropriate first aid, and easily make reservations at medical facilities if necessary.

[0261] "Health status" refers to the physical and physiological condition of a pet, and is determined from its normal activity, eating habits, and other behaviors.

[0262] "Natural language data" refers to information expressed in natural language entered by the user, and is text data that describes the symptoms and behavior of pets.

[0263] "Information processing means" refers to systems and processes for analyzing received data, and methods for interpreting data using natural language processing techniques and machine learning models.

[0264] "Key terms" refer to words and phrases extracted from natural language data that are particularly meaningful in estimating a pet's health status.

[0265] A "machine learning model" is an algorithm or method that makes predictions and classifications based on past data. It learns patterns from the data and is used to estimate the health status of pets.

[0266] "First aid" refers to initial measures that can be taken quickly when a pet becomes ill, and are implemented before professional diagnosis or treatment.

[0267] A "medical facility" is a specialized institution that provides health checkups and treatment for pets, and includes facilities such as animal hospitals and clinics.

[0268] "Appointment booking" refers to the process of setting a date and time to visit a medical facility, and is carried out through a system as needed.

[0269] This invention provides a support system that enables pet owners to quickly recognize when their pets are unwell and take appropriate action. This system operates via terminals, servers, and a network, and utilizes natural language processing technology and machine learning.

[0270] First, users use devices such as smartphones or computers to input information about their pet's health in natural language. This input is expected to include specific symptoms and behaviors. For example, a user might input something like, "My cat has lost its appetite and is sleeping frequently."

[0271] The terminal sends the collected data to the server via the network. The server analyzes the received natural language data using natural language processing libraries (e.g., spaCy or TensorFlow). This analysis extracts important keywords and phrases.

[0272] The server uses this extracted information to estimate the pet's health status using a machine learning model (e.g., a model using Scikit-learn or PyTorch). This process also takes into account the pet's characteristics, such as age, sex, and medical history.

[0273] Based on the estimation results, the server generates appropriate first-aid instructions and sends that information to the terminal. The terminal displays the received instructions on the screen and prompts the user to take specific action.

[0274] Furthermore, the server uses location information to identify nearby medical facilities and, if necessary, assists with the booking process at medical institutions. This allows users to effectively manage their pets' health and receive appropriate medical services quickly.

[0275] When using this system, the system displays prompts such as, "Please enter information about your pet's health. For example, 'My dog ​​has been lethargic since this morning,'" to support the input of necessary data. This allows pet owners to easily record their pet's condition, enabling detailed analysis on the server side.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The user uses the terminal to input natural language data regarding the health status of the pet. Specifically, through the input interface of the terminal, the user inputs text such as "The dog has been listless and has no appetite since this morning." As a result, the user's observation information is aggregated on the terminal as digital data.

[0279] Step 2:

[0280] The terminal receives the natural language data input by the user and prepares to send it to the server. The input string data is converted into packets according to the network protocol and securely transferred to the server through the communication network.

[0281] Step 3:

[0282] The server analyzes the received text data using natural language processing techniques. First, the received data is tokenized, and important keywords and phrases are extracted using a natural language processing library (e.g., spaCy) to analyze the structure of the language. The input is natural language text data, and the output is the important phrases extracted as features.

[0283] Step 4:

[0284] The server estimates the health status of the pet using a machine learning model based on the extracted important phrases. At this time, a prediction is made considering the characteristic information such as the pet's age, gender, and medical history stored in advance. The input is the important phrases and characteristic information, and the output is the estimation result of the pet's health status. Specifically, a classification model is executed using a library such as Scikit-learn.

[0285] Step 5:

[0286] The server determines appropriate emergency measures based on the estimated health condition. For example, it generates specific instructions such as "Provide more water". The input is the estimated result of the health condition, and the output is the instruction for emergency measures.

[0287] Step 6:

[0288] The server sends an instruction for emergency measures to the terminal. The terminal displays it on the screen for the user. Based on this information, the user can take emergency measures for the pet. This enables the user to execute appropriate countermeasures quickly and effectively.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] There is a need for a means that can not only provide information for quickly and appropriately taking emergency measures when a pet's physical condition deteriorates, but also immediately propose the required related products and enable a quick purchase procedure. Such a system is expected to reduce the burden related to maintaining the health of the pet and enhance the convenience for the owner.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes a device that receives natural language data related to a pet's physical condition deterioration, an information processing device that analyzes the natural language data to predict the pet's health condition, a device that creates an instruction for emergency measures based on the predicted health condition, a device that proposes related products according to the result of analyzing the pet's health condition, and a device that enables the purchase of the proposed products through electronic payment. This enables the quick provision of instructions for emergency measures and the proposal and purchase of related products.

[0294] "Natural language data related to pet health problems" refers to data in which pet owners express their observations and concerns about their pets' health in natural language.

[0295] An "information processing device" is a device that analyzes input data, understands its meaning and intent based on that data, and determines the next processing step.

[0296] "Predicting health status" means estimating a pet's health status using a specialized algorithm based on its current physical condition and past information.

[0297] "First aid instructions" are directives that specifically outline the immediate actions and responses necessary to maintain or restore a pet's health.

[0298] A "device that suggests related products" is a device that allows users to easily select and present appropriate products and services according to their pet's health condition and problems.

[0299] A "device that enables purchase via electronic payment" is a device that allows the proposed product to be purchased quickly through a specific online payment system.

[0300] The system for implementing this invention consists of a user's terminal, a server, and an associated network. The user uses a terminal such as a smartphone or computer to input natural language data regarding their pet's health condition. This data is transmitted from the terminal to the server via a communication network.

[0301] The server first analyzes the received natural language data using a natural language processing (NLP) library. Specifically, libraries such as spaCy or Hugging Face Transformers are used to extract important keywords and phrases from the data. Based on the extracted information, the server predicts the pet's health status using machine learning algorithms. For this prediction, machine learning frameworks such as TensorFlow or PyTorch are used, and characteristic information such as the pet's age, gender, and medical history is also considered.

[0302] Once the prediction of the health status is made, the server generates instructions for emergency treatment based on the result and sends them to the user's terminal. According to these instructions, the user can perform emergency treatment on the pet. Furthermore, the server proposes relevant products according to the pet's health status and presents them to the user. At this time, the relevant product information is obtained using shopping APIs such as Amazon or Rakuten. The user can purchase these products through an electronic payment system. Electronic payment services such as Stripe or PayPal are used.

[0303] As a specific example of use, assume that the user inputs "The cat is drinking water frequently". In this case, based on the input data, the server evaluates the potential risk of diabetes and proposes the purchase of a hood or test kit for diabetes.

[0304] Examples of prompt texts for the generative AI model may include the following.

[0305] "Please propose products to purchase to improve the health status of the pet. The situation is as follows: 'The cat is drinking water frequently'"

[0306] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0307] Step 1:

[0308] Users input natural language data about their pet's health issues using a smartphone or computer. This data reflects the user's observations and concerns (e.g., "My cat is drinking water frequently"). The input data is stored on the device and transmitted to a server via the network.

[0309] Step 2:

[0310] The server analyzes the received natural language data using natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers). This analysis extracts important keywords and phrases from the text. Based on the input data, it identifies elements related to specific health conditions. The analysis results provide information that suggests potential health problems in pets.

[0311] Step 3:

[0312] The server applies machine learning algorithms (e.g., TensorFlow, PyTorch) to predict the health status using the extracted information. Pet characteristics (age, sex, medical history, etc.) are also considered. Based on the input information, the health status is output as a numerical value or diagnostic result. This output is then used to construct instructions for first aid.

[0313] Step 4:

[0314] The server generates first-aid instructions based on the predicted health condition. These instructions (e.g., "Give more fluids") are sent to the user's device, allowing the user to immediately take specific action.

[0315] Step 5:

[0316] The server retrieves the most suitable related products based on the pet's health condition via shopping APIs (e.g., Amazon, Rakuten Market). Product information, such as test kits or specific pet food, is suggested. The user can view the suggested products on their device.

[0317] Step 6:

[0318] When a user wishes to make a purchase, the process of purchasing the goods through an electronic payment system (e.g., Stripe, PayPal) is initiated. The server processes the payment information and guides the user through the steps to complete the transaction. In this way, the purchase process is completed smoothly.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention is a system that combines natural language data related to a pet's health problems with an emotion engine that recognizes the user's emotions. When a user inputs their pet's symptoms, the system can detect the user's emotional state from the words and context, and dynamically adapt the style of emergency treatment and information presentation accordingly.

[0321] To use the system, users first input natural language data about their pet's health using a terminal. This data is in the form of a description of how the pet is feeling unwell. The input includes the pet's name, symptoms, and any visible abnormalities.

[0322] The terminal sends the input data to the server, which uses natural language processing technology to extract important symptom keywords from the data. In addition, an emotion engine picks up emotional hints from the user's input and analyzes the user's emotions, such as whether they are tense or calm.

[0323] Based on the analysis results, the server uses an AI model to estimate the pet's health status. This process also takes into account the pet's pre-registered characteristics (age, sex, medical history, etc.).

[0324] Based on estimations, optimal first-aid instructions are generated, utilizing the results of the emotion engine's analysis. For example, if the user is feeling very anxious, the server will provide instructions with more detailed and reassuring explanations. These first-aid instructions and information on recommended medical facilities are presented to the user on their device.

[0325] If the user wishes to seek further medical attention, the device will identify the nearest veterinary hospital and provide assistance with making an appointment. The emotion engine also adjusts the guidance based on the user's current emotional state, providing a smooth and reassuring experience.

[0326] This system enables flexible adaptation of the user interface, which was difficult with conventional systems, resulting in more personalized support. Users will be able to use the system with peace of mind and confidence, in addition to managing their pets' health.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] Users input information about their pet's symptoms and condition into the device using natural language. For example, they might provide specific details such as, "My cat has lost its appetite and is lethargic."

[0330] Step 2:

[0331] The terminal prepares to send the input natural language data to the server via the communication network. During this process, the data is converted to an appropriate format and sent to the server as an API request.

[0332] Step 3:

[0333] The server receives natural language data from the terminal. The server then uses a natural language processing engine to analyze the data and detect important keywords and phrases related to the pet's symptoms.

[0334] Step 4:

[0335] Simultaneously, the server uses an emotion engine to detect the user's emotions from natural language data. This allows it to determine the user's emotional state, such as whether they are anxious, calm, or restless.

[0336] Step 5:

[0337] The server uses an AI model to estimate the pet's health status based on the analyzed data and the user's emotional state. Pet characteristics (e.g., age, sex, medical history) are taken into consideration during this process.

[0338] Step 6:

[0339] Based on the estimated health status, the server generates first-aid instructions. Using the results of the emotion engine, the instructions are adjusted to the user's emotional state. For example, if the user is anxious, more detailed explanations are added to provide greater reassurance.

[0340] Step 7:

[0341] The server transmits first-aid instructions and medical condition information to the terminal. The terminal displays this information on its user interface, clearly presenting it to the user.

[0342] Step 8:

[0343] When a user wishes to visit a medical institution, the device communicates this to the server. The server identifies the nearest animal hospital, provides the user with that information, and also offers a reservation assistance function.

[0344] Through this series of processes, the system aims to support pet health management and the user's emotional well-being.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] In today's world, where prompt and appropriate responses to pet illnesses are essential, it's crucial to ensure that pet owners can cope with the situation with peace of mind, even when they are emotionally unstable. However, conventional systems fail to provide information that takes the owner's emotional state into account, and they do not fully utilize individual pet information, making it difficult to provide appropriate first aid.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes a device that receives natural language data related to the pet's poor health, an information processing device that extracts symptom keywords and analyzes the user's emotional state, and a computing device that estimates the pet's health condition based on the extracted keywords and emotional data. This enables personalized first aid instructions that are tailored to the user's emotional state.

[0350] "Natural language data" refers to textual data from users describing the current health status of their pets, including information indicating symptoms or abnormalities in the pets.

[0351] "Symptom keywords" are important words and phrases related to a pet's health condition, extracted from natural language data.

[0352] "User emotional state" refers to the mental state or emotional tendencies expressed when a user inputs natural language data.

[0353] An "information processing device" is a device or software used to analyze natural language data, extract symptom keywords, and analyze the emotional state of a user.

[0354] A "computational device" refers to a device or infrastructure with computing capabilities for estimating a pet's health status based on extracted symptom keywords and emotional data.

[0355] "First aid instructions" refer to information about the actions that should be taken immediately in response to a pet's illness, based on its estimated health condition.

[0356] A "medical facility" refers to a veterinary facility or hospital that provides medical care for pets.

[0357] This invention is a system aimed at quickly assessing a pet's health condition and providing owners with appropriate first aid and peace of mind.

[0358] The terminal first has a means of receiving natural language data entered by the user. This includes information processing devices such as smartphones and personal computers, and the user uses the terminal to input the pet's symptoms and related information. An example of the input content might be in the format of "My cat Milk is lethargic and has no appetite."

[0359] The terminal sends this natural language data to the server. The server, acting as an information processing device, first uses spaCy, a natural language processing library, to extract keywords related to the symptoms. It also uses NLTK for emotion analysis to analyze the user's emotional state. This allows the server to understand whether the user is anxious or calm.

[0360] Based on this information, the server uses the GPT-3 generative AI model to estimate the pet's health status, taking into account the extracted keywords and emotional state. The calculated results also incorporate the pet's pre-registration information, resulting in a more accurate estimate of the pet's health status.

[0361] The server generates first-aid instructions based on the estimated results and presents them to the user via the terminal. For example, if the user is feeling very anxious, it can add detailed and reassuring explanations such as, "Don't panic, warm up some milk, and let the baby rest for a while."

[0362] In addition, if a user wishes to visit a medical institution, the device will use their current location to identify the nearest medical facility and assist with making an appointment. The suggested medical facilities will also be presented in a way that takes the user's feelings into consideration.

[0363] This system overcomes the limitations of conventional information provision systems, enabling personalized, safe, and reliable medical support for users.

[0364] A concrete example of a prompt might be the input, "My dog, Coco, has been coughing since this morning. What should I do?" Based on this prompt, the generative AI model provides appropriate first aid and explanations.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The user provides natural language input about their pet's health condition to the device. The device receives input such as, "My cat, Milk, is lethargic and has no appetite." This input is processed as the starting data for the entire system.

[0368] Step 2:

[0369] The terminal sends the received natural language input data to the server. This transmission takes place over the internet using secure communication methods. The next process begins as soon as the server receives the data.

[0370] Step 3:

[0371] The server analyzes the received natural language data using an information processing device. Using a natural language processing library (e.g., spaCy), symptom keywords such as "feeling unwell" and "loss of appetite" are extracted from the input text. The input for this process is the user's natural language data, and the output is a list of extracted keywords.

[0372] Step 4:

[0373] The server uses an emotion analysis engine to analyze the user's emotional state from their input. For example, a natural language processing library (such as NLTK) is used to extract emotions like "anxiety" and "worry." At this stage, the input is the user's natural language text, and the output is an index of their emotional state.

[0374] Step 5:

[0375] The server uses the extracted symptom keywords and emotional states as a dataset to estimate the pet's health status using a generative AI model (e.g., GPT-3). The input here is symptom keywords and emotional indicators, and the output is information about the estimated health status. This process allows for the planning of first aid measures appropriate to the pet's current condition.

[0376] Step 6:

[0377] The server generates first-aid instructions based on the estimation results and sends them to the terminal. The generated instructions include smooth and reassuring explanations that take the user's emotions into consideration. The input is information about the estimated health status, and the output is first-aid instructions presented to the user.

[0378] Step 7:

[0379] When a user wishes to visit a medical institution, the terminal identifies the nearest medical facility based on their current location. The server supports this by providing information to facilitate the booking process. The input here is the user's current location, and the output is information about the recommended nearest medical facility.

[0380] This process allows users to receive emotionally reassuring support and be guided to appropriate first aid and medical facilities.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] Providing appropriate solutions for pet health management while considering the owner's feelings is challenging. Furthermore, the lack of optimized food recommendations and purchasing procedures tailored to the pet's health condition highlights the need to improve the user experience. When a pet is unwell, owners often experience anxiety, and information and support are needed to alleviate this anxiety. Additionally, a system is desired that appropriately recommends the most suitable food for each pet's health condition and facilitates its purchase.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for receiving natural language data related to the pet's poor health, means for analyzing the user's emotional state and adjusting the information presentation style according to the emotional state, and means for recommending the most suitable food according to the pet's health condition. This makes it possible to provide appropriate first aid measures regarding pet health management, and to optimize the recommendation and purchase process of pet food, while taking into account the owner's emotions.

[0386] "Natural language data related to pet health problems" refers to information that pet owners have written themselves using language about unusual symptoms or signs that may affect their pet's health.

[0387] "Information processing means" refers to a computational function or software and hardware environment for analyzing received data and estimating the current health status of a pet.

[0388] "Means for generating first aid instructions" refers to a process or device for automatically creating and presenting appropriate initial responses to an estimated health condition.

[0389] "Means of presenting to the user" refers to a mechanism including a display device or audio output device for conveying generated information or instructions to the user visually or aurally.

[0390] "A means of identifying the nearest medical facility and assisting with reservations" refers to a process that uses information about the pet's health to determine the geographically closest animal hospital or other medical facility and provides an interface for making appointments.

[0391] "A means of analyzing a user's emotional state and adjusting the information presentation style according to that emotional state" refers to a system that evaluates the user's psychological state from the input data and dynamically changes the format and tone of information provision based on the results.

[0392] "A method for recommending the optimal food according to a pet's health condition" refers to a process that automatically selects and suggests foods and nutrients that are suitable for a pet's current health condition.

[0393] "Means of optimizing the purchase process" refers to a process that simplifies and makes purchase-related operations easier to use, based on the user's emotional state and other factors.

[0394] The system for implementing this invention mainly consists of a server and a user terminal. The user inputs natural language data about the pet's health using a smartphone application. This includes the pet's symptoms, changes in appearance, and changes in appetite. The data received by the terminal is transmitted to the server.

[0395] The server analyzes the input data using natural language processing software. For example, the Google Cloud Natural Language API is used. As a result of the analysis, keywords related to important symptoms are extracted, and basic data is generated to predict the pet's health status. In this process, pre-registered characteristic information about the pet is also taken into consideration. Generative AI models can be used in this process, with TensorFlow models being one example.

[0396] Furthermore, the server uses an emotion analysis engine to analyze the user's emotional state. This engine includes, for example, IBM Watson Tone Analyzer. Based on the analysis results, it determines how anxious or relaxed the user is and applies this information to subsequent processes.

[0397] Based on a prediction of the pet's health condition, appropriate first-aid instructions are generated, and recommended pet food and supplements are selected according to the pet's health status. This information is presented in detail and in an appropriate tone on the user's device. If the user is feeling very anxious, more detailed explanations and advice are added to provide reassurance.

[0398] Furthermore, when a user initiates a purchase, the server optimizes the process to ensure a smooth flow. For example, if a pet has a decreased appetite, the server automatically recommends foods that stimulate appetite and supplements that aid digestion, providing a process that allows for easy purchase.

[0399] An example of a prompt message for a generative AI model would be: "This user is concerned about their pet's health. Based on the entered symptom information and acquired emotion data, please recommend pet food. The current emotion state is 'calm'."

[0400] This method allows users to manage their pets' health more easily and with greater peace of mind, while also providing them with information on the most suitable food for their pets.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The user operates a smartphone application and inputs natural language data about their pet's health. This data includes the pet's symptoms and any changes in its appearance. This data is then transmitted to a server by the device.

[0404] Step 2:

[0405] The server analyzes the received natural language data using tools such as the Google Cloud Natural Language API. The input is text data describing the pet's symptoms, and the output is the extraction of keywords indicating important symptoms. This analysis processes and interprets the data, generating basic information for evaluating the pet's health status.

[0406] Step 3:

[0407] The server uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is the user's emotional expressions associated with natural language data, and the output is an evaluation of the user's emotional state. This process determines whether the user is anxious or calm and applies that information to subsequent processes.

[0408] Step 4:

[0409] The server uses a generative AI model to predict the pet's health status, taking into account the pet's pre-registered characteristic information. The input is the set of symptom keywords and characteristic information extracted in the previous step, and the output is predicted pet health status data. In this process, the AI ​​model estimates the health status through calculations of the data.

[0410] Step 5:

[0411] The server generates first-aid instructions and pet food recommendations based on the predicted health status. The inputs are the predicted health status and the user's emotional state, while the output is a specific first-aid document and a list of recommended pet foods to present to the user. The recommendations are tailored to take the user's emotions into consideration and to provide reassurance.

[0412] Step 6:

[0413] The terminal displays first-aid instructions and pet food recommendations sent from the server to the user. Input consists of instruction documents and recommendation lists from the server, while output is information presented in visual or auditory formats. The information is provided to the user in a user-friendly and easy-to-understand format.

[0414] Step 7:

[0415] When a user indicates their intention to purchase pet food, the server assists in facilitating the purchase process. The input is the user's purchase request information, and the output is an optimized purchase process. This allows users to complete their purchase easily and with peace of mind.

[0416] 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.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0418] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0423] 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.

[0424] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0425] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0426] 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.

[0427] 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.

[0428] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0429] The 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.

[0430] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0431] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0432] This invention is a system that helps pet owners respond quickly and accurately when they notice their pet is unwell. This system is implemented via terminals, servers, and a network.

[0433] First, users use devices such as smartphones or computers to input natural language data about their pet's health. For example, a user might input something like, "My cat is lethargic and not eating."

[0434] The device sends this input to the server via the communication network. The server analyzes the received text data using natural language processing (NLP) techniques to extract important keywords and phrases. Based on the extracted information, the server uses a machine learning model to estimate the pet's health status. In this estimation, characteristic information such as the pet's age, sex, and medical history is also taken into consideration.

[0435] Based on the health status estimated by the server, recommended first aid measures are determined. For example, if dehydration is likely, "give more water" will be suggested as first aid. These first aid instructions are sent from the server to the terminal and displayed to the user. The user can then use this information to administer first aid to their pet.

[0436] Furthermore, this system has the functionality to identify nearby veterinary hospitals and present their location and contact information to the user. If the user wishes to make an appointment, the system has the functionality to assist the hospital with the booking process. This allows users to visit a medical institution quickly and easily.

[0437] In this way, the present invention provides a practical solution for achieving both the health of pets and the psychological well-being of users.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] Users use devices such as smartphones or computers to input detailed natural language data about their pet's health. This data describes the symptoms the pet is exhibiting. For example, "The dog has lost its appetite and has diarrhea."

[0441] Step 2:

[0442] The terminal formats the data entered by the user into a digital format and creates an API request to send it to the server over the network.

[0443] Step 3:

[0444] The server receives the transmitted input data. Next, it uses a natural language processing (NLP) engine to extract important keywords and related phrases from the input data. This extracts the information necessary for analysis.

[0445] Step 4:

[0446] The server uses extracted keywords and pet characteristic information (age, sex, medical history, etc.) provided by the user in advance to run an AI model and estimate the pet's health status. This estimation also includes checking whether similar symptoms exist in the past database.

[0447] Step 5:

[0448] The server uses AI to select the most appropriate first aid based on the estimated health status. The content of the first aid is determined taking into account factors such as the pet's species and its usual health condition.

[0449] Step 6:

[0450] The server compiles information, including diagnostic results and details of first aid, and sends it to the terminal. This information includes immediate action to take for the pet.

[0451] Step 7:

[0452] The terminal displays information received from the server on the user interface, assisting the user in taking appropriate action. This includes reassuring explanations and specific procedures.

[0453] Step 8:

[0454] If the user wishes to seek medical attention as a further measure, the device will provide information on the nearest animal hospital. If a reservation support function is available, the device will send a reservation request to the selected hospital via the server.

[0455] This series of steps enables users to quickly and appropriately manage their pet's health and receive support for diagnosis and treatment at a medical facility when necessary.

[0456] (Example 1)

[0457] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] There is a need for a system that can help pet owners quickly and accurately identify when their pets are unwell and support them in providing appropriate first aid or visiting medical facilities. However, currently, accurately diagnosing a pet's illness requires specialized knowledge, and there is a problem in that rapid response is difficult, especially in emergencies. Therefore, there is a need to develop a user-friendly and effective support system that allows pet owners to easily assess their pet's health and take appropriate measures.

[0459] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0460] In this invention, the server includes means for acquiring natural language data relating to the health status of a pet, means for information processing means to analyze the natural language data using natural language processing technology and extract important keywords, and means for estimating the health status of the pet using a machine learning model based on the extracted important keywords. This allows pet owners to quickly recognize when their pet is unwell, receive instructions for appropriate first aid, and easily make reservations at medical facilities if necessary.

[0461] "Health status" refers to the physical and physiological condition of a pet, and is determined from its normal activity, eating habits, and other behaviors.

[0462] "Natural language data" refers to information expressed in natural language entered by the user, and is text data that describes the symptoms and behavior of pets.

[0463] "Information processing means" refers to systems and processes for analyzing received data, and methods for interpreting data using natural language processing techniques and machine learning models.

[0464] "Key terms" refer to words and phrases extracted from natural language data that are particularly meaningful in estimating a pet's health status.

[0465] A "machine learning model" is an algorithm or method that makes predictions and classifications based on past data. It learns patterns from the data and is used to estimate the health status of pets.

[0466] "First aid" refers to initial measures that can be taken quickly when a pet becomes ill, and are implemented before professional diagnosis or treatment.

[0467] A "medical facility" is a specialized institution that provides health checkups and treatment for pets, and includes facilities such as animal hospitals and clinics.

[0468] "Appointment booking" refers to the process of setting a date and time to visit a medical facility, and is carried out through a system as needed.

[0469] This invention provides a support system that enables pet owners to quickly recognize when their pets are unwell and take appropriate action. This system operates via terminals, servers, and a network, and utilizes natural language processing technology and machine learning.

[0470] First, users use devices such as smartphones or computers to input information about their pet's health in natural language. This input is expected to include specific symptoms and behaviors. For example, a user might input something like, "My cat has lost its appetite and is sleeping frequently."

[0471] The terminal sends the collected data to the server via the network. The server analyzes the received natural language data using natural language processing libraries (e.g., spaCy or TensorFlow). This analysis extracts important keywords and phrases.

[0472] The server uses this extracted information to estimate the pet's health status using a machine learning model (e.g., a model using Scikit-learn or PyTorch). This process also takes into account the pet's characteristics, such as age, sex, and medical history.

[0473] Based on the estimation results, the server generates appropriate first-aid instructions and sends that information to the terminal. The terminal displays the received instructions on the screen and prompts the user to take specific action.

[0474] Furthermore, the server uses location information to identify nearby medical facilities and, if necessary, assists with the booking process at medical institutions. This allows users to effectively manage their pets' health and receive appropriate medical services quickly.

[0475] When using this system, the system displays prompts such as, "Please enter information about your pet's health. For example, 'My dog ​​has been lethargic since this morning,'" to support the input of necessary data. This allows pet owners to easily record their pet's condition, enabling detailed analysis on the server side.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] Users input natural language data about their pet's health using a device. Specifically, they input text such as, "My dog ​​has been lethargic and has no appetite since this morning," through the device's input interface. This allows the user's observational information to be collected as digital data on the device.

[0479] Step 2:

[0480] The terminal receives natural language data entered by the user and prepares to send it to the server. The entered string data is converted into packets according to the network protocol and securely transferred to the server via the communication network.

[0481] Step 3:

[0482] The server analyzes the received text data using natural language processing techniques. First, it tokenizes the received data and extracts important keywords and phrases using a natural language processing library (e.g., spaCy) to analyze the language structure. The input is natural language text data, and the output is the important words and phrases extracted as features.

[0483] Step 4:

[0484] The server uses a machine learning model to estimate the pet's health status based on extracted key keywords. This prediction takes into account pre-stored characteristic information such as the pet's age, sex, and medical history. The input consists of key keywords and characteristic information, and the output is the estimated health status of the pet. Specifically, a classification model is executed using libraries such as Scikit-learn.

[0485] Step 5:

[0486] The server determines appropriate first aid based on the estimated health status. For example, it generates specific instructions such as "give more water." The input is the estimated health status, and the output is the first aid instruction.

[0487] Step 6:

[0488] The server sends first aid instructions to the terminal. The terminal displays these instructions on the user's screen. The user can then use this information to administer first aid to their pet. This allows the user to quickly and effectively take appropriate action.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] There is a need for a system that not only provides information to enable quick and appropriate first aid when a pet becomes ill, but also immediately suggests necessary related products and allows for quick purchase procedures. Such a system is expected to reduce the burden of maintaining pet health and increase convenience for pet owners.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the server includes a device for receiving natural language data related to a pet's poor health, an information processing device for analyzing the natural language data and predicting the pet's health condition, a device for creating first aid instructions based on the predicted health condition, a device for suggesting relevant products according to the results of the analysis of the pet's health condition, and a device for enabling the purchase of suggested products through electronic payment. This enables the rapid provision of first aid instructions and the suggestion and purchase of relevant products.

[0494] "Natural language data related to pet health problems" refers to data in which pet owners express their observations and concerns about their pets' health in natural language.

[0495] An "information processing device" is a device that analyzes input data, understands its meaning and intent based on that data, and determines the next processing step.

[0496] "Predicting health status" means estimating a pet's health status using a specialized algorithm based on its current physical condition and past information.

[0497] "First aid instructions" are directives that specifically outline the immediate actions and responses necessary to maintain or restore a pet's health.

[0498] A "device that suggests related products" is a device that allows users to easily select and present appropriate products and services according to their pet's health condition and problems.

[0499] A "device that enables purchase via electronic payment" is a device that allows the proposed product to be purchased quickly through a specific online payment system.

[0500] The system for implementing this invention consists of a user's terminal, a server, and an associated network. The user uses a terminal such as a smartphone or computer to input natural language data regarding their pet's health condition. This data is transmitted from the terminal to the server via a communication network.

[0501] The server first analyzes the received natural language data using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and Hugging Face Transformers are used to extract important keywords and phrases from the data. Based on the extracted information, the server uses machine learning algorithms to predict the pet's health status. This prediction uses machine learning frameworks such as TensorFlow and PyTorch, and also takes into account characteristic information such as the pet's age, sex, and medical history.

[0502] Once a health status prediction is made, the server generates first-aid instructions based on the results and sends them to the user's terminal. Following these instructions, the user can perform first aid on their pet. Furthermore, the server suggests and presents relevant products based on the pet's health condition. This relevant product information is obtained using shopping APIs such as Amazon and Rakuten. Users can purchase these products through electronic payment systems, such as Stripe and PayPal.

[0503] As a concrete example of its use, suppose a user inputs "My cat is drinking water frequently." In this case, the server will use the input data to assess the potential risk of diabetes and suggest purchasing diabetes-friendly food or testing kits.

[0504] Examples of prompts for a generative AI model include the following:

[0505] "Please suggest products I should buy to improve my pet's health. The situation is as follows: 'My cat is drinking water frequently.'"

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] Users input natural language data about their pet's health issues using a smartphone or computer. This data reflects the user's observations and concerns (e.g., "My cat is drinking water frequently"). The input data is stored on the device and transmitted to a server via the network.

[0509] Step 2:

[0510] The server analyzes the received natural language data using natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers). This analysis extracts important keywords and phrases from the text. Based on the input data, it identifies elements related to specific health conditions. The analysis results provide information that suggests potential health problems in pets.

[0511] Step 3:

[0512] The server applies machine learning algorithms (e.g., TensorFlow, PyTorch) to predict the health status using the extracted information. Pet characteristics (age, sex, medical history, etc.) are also considered. Based on the input information, the health status is output as a numerical value or diagnostic result. This output is then used to construct instructions for first aid.

[0513] Step 4:

[0514] The server generates first-aid instructions based on the predicted health condition. These instructions (e.g., "Give more fluids") are sent to the user's device, allowing the user to immediately take specific action.

[0515] Step 5:

[0516] The server retrieves the most suitable related products based on the pet's health condition via shopping APIs (e.g., Amazon, Rakuten Market). Product information, such as test kits or specific pet food, is suggested. The user can view the suggested products on their device.

[0517] Step 6:

[0518] When a user wishes to make a purchase, the process of purchasing the goods through an electronic payment system (e.g., Stripe, PayPal) is initiated. The server processes the payment information and guides the user through the steps to complete the transaction. In this way, the purchase process is completed smoothly.

[0519] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0520] This invention is a system that combines natural language data related to a pet's health problems with an emotion engine that recognizes the user's emotions. When a user inputs their pet's symptoms, the system can detect the user's emotional state from the words and context, and dynamically adapt the style of emergency treatment and information presentation accordingly.

[0521] To use the system, users first input natural language data about their pet's health using a terminal. This data is in the form of a description of how the pet is feeling unwell. The input includes the pet's name, symptoms, and any visible abnormalities.

[0522] The terminal sends the input data to the server, which uses natural language processing technology to extract important symptom keywords from the data. In addition, an emotion engine picks up emotional hints from the user's input and analyzes the user's emotions, such as whether they are tense or calm.

[0523] Based on the analysis results, the server uses an AI model to estimate the pet's health status. This process also takes into account the pet's pre-registered characteristics (age, sex, medical history, etc.).

[0524] Based on estimations, optimal first-aid instructions are generated, utilizing the results of the emotion engine's analysis. For example, if the user is feeling very anxious, the server will provide instructions with more detailed and reassuring explanations. These first-aid instructions and information on recommended medical facilities are presented to the user on their device.

[0525] If the user wishes to seek further medical attention, the device will identify the nearest veterinary hospital and provide assistance with making an appointment. The emotion engine also adjusts the guidance based on the user's current emotional state, providing a smooth and reassuring experience.

[0526] This system enables flexible adaptation of the user interface, which was difficult with conventional systems, resulting in more personalized support. Users will be able to use the system with peace of mind and confidence, in addition to managing their pets' health.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] Users input information about their pet's symptoms and condition into the device using natural language. For example, they might provide specific details such as, "My cat has lost its appetite and is lethargic."

[0530] Step 2:

[0531] The terminal prepares to send the input natural language data to the server via the communication network. During this process, the data is converted to an appropriate format and sent to the server as an API request.

[0532] Step 3:

[0533] The server receives natural language data from the terminal. The server then uses a natural language processing engine to analyze the data and detect important keywords and phrases related to the pet's symptoms.

[0534] Step 4:

[0535] Simultaneously, the server uses an emotion engine to detect the user's emotions from natural language data. This allows it to determine the user's emotional state, such as whether they are anxious, calm, or restless.

[0536] Step 5:

[0537] The server uses an AI model to estimate the pet's health status based on the analyzed data and the user's emotional state. Pet characteristics (e.g., age, sex, medical history) are taken into consideration during this process.

[0538] Step 6:

[0539] Based on the estimated health status, the server generates first-aid instructions. Using the results of the emotion engine, the instructions are adjusted to the user's emotional state. For example, if the user is anxious, more detailed explanations are added to provide greater reassurance.

[0540] Step 7:

[0541] The server transmits first-aid instructions and medical condition information to the terminal. The terminal displays this information on its user interface, clearly presenting it to the user.

[0542] Step 8:

[0543] When a user wishes to visit a medical institution, the device communicates this to the server. The server identifies the nearest animal hospital, provides the user with that information, and also offers a reservation assistance function.

[0544] Through this series of processes, the system aims to support pet health management and the user's emotional well-being.

[0545] (Example 2)

[0546] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] In today's world, where prompt and appropriate responses to pet illnesses are essential, it's crucial to ensure that pet owners can cope with the situation with peace of mind, even when they are emotionally unstable. However, conventional systems fail to provide information that takes the owner's emotional state into account, and they do not fully utilize individual pet information, making it difficult to provide appropriate first aid.

[0548] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0549] In this invention, the server includes a device that receives natural language data related to the pet's poor health, an information processing device that extracts symptom keywords and analyzes the user's emotional state, and a computing device that estimates the pet's health condition based on the extracted keywords and emotional data. This enables personalized first aid instructions that are tailored to the user's emotional state.

[0550] "Natural language data" refers to textual data from users describing the current health status of their pets, including information indicating symptoms or abnormalities in the pets.

[0551] "Symptom keywords" are important words and phrases related to a pet's health condition, extracted from natural language data.

[0552] "User emotional state" refers to the mental state or emotional tendencies expressed when a user inputs natural language data.

[0553] An "information processing device" is a device or software used to analyze natural language data, extract symptom keywords, and analyze the emotional state of a user.

[0554] A "computational device" refers to a device or infrastructure with computing capabilities for estimating a pet's health status based on extracted symptom keywords and emotional data.

[0555] "First aid instructions" refer to information about the actions that should be taken immediately in response to a pet's illness, based on its estimated health condition.

[0556] A "medical facility" refers to a veterinary facility or hospital that provides medical care for pets.

[0557] This invention is a system aimed at quickly assessing a pet's health condition and providing owners with appropriate first aid and peace of mind.

[0558] The terminal first has a means of receiving natural language data entered by the user. This includes information processing devices such as smartphones and personal computers, and the user uses the terminal to input the pet's symptoms and related information. An example of the input content might be in the format of "My cat Milk is lethargic and has no appetite."

[0559] The terminal sends this natural language data to the server. The server, acting as an information processing device, first uses spaCy, a natural language processing library, to extract keywords related to the symptoms. It also uses NLTK for emotion analysis to analyze the user's emotional state. This allows the server to understand whether the user is anxious or calm.

[0560] Based on this information, the server uses the GPT-3 generative AI model to estimate the pet's health status, taking into account the extracted keywords and emotional state. The calculated results also incorporate the pet's pre-registration information, resulting in a more accurate estimate of the pet's health status.

[0561] The server generates first-aid instructions based on the estimated results and presents them to the user via the terminal. For example, if the user is feeling very anxious, it can add detailed and reassuring explanations such as, "Don't panic, warm up some milk, and let the baby rest for a while."

[0562] In addition, if a user wishes to visit a medical institution, the device will use their current location to identify the nearest medical facility and assist with making an appointment. The suggested medical facilities will also be presented in a way that takes the user's feelings into consideration.

[0563] This system overcomes the limitations of conventional information provision systems, enabling personalized, safe, and reliable medical support for users.

[0564] A concrete example of a prompt might be the input, "My dog, Coco, has been coughing since this morning. What should I do?" Based on this prompt, the generative AI model provides appropriate first aid and explanations.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user provides natural language input about their pet's health condition to the device. The device receives input such as, "My cat, Milk, is lethargic and has no appetite." This input is processed as the starting data for the entire system.

[0568] Step 2:

[0569] The terminal sends the received natural language input data to the server. This transmission takes place over the internet using secure communication methods. The next process begins as soon as the server receives the data.

[0570] Step 3:

[0571] The server analyzes the received natural language data using an information processing device. Using a natural language processing library (e.g., spaCy), symptom keywords such as "feeling unwell" and "loss of appetite" are extracted from the input text. The input for this process is the user's natural language data, and the output is a list of extracted keywords.

[0572] Step 4:

[0573] The server uses an emotion analysis engine to analyze the user's emotional state from their input. For example, a natural language processing library (such as NLTK) is used to extract emotions like "anxiety" and "worry." At this stage, the input is the user's natural language text, and the output is an index of their emotional state.

[0574] Step 5:

[0575] The server uses the extracted symptom keywords and emotional states as a dataset to estimate the pet's health status using a generative AI model (e.g., GPT-3). The input here is symptom keywords and emotional indicators, and the output is information about the estimated health status. This process allows for the planning of first aid measures appropriate to the pet's current condition.

[0576] Step 6:

[0577] The server generates first-aid instructions based on the estimation results and sends them to the terminal. The generated instructions include smooth and reassuring explanations that take the user's emotions into consideration. The input is information about the estimated health status, and the output is first-aid instructions presented to the user.

[0578] Step 7:

[0579] When a user wishes to visit a medical institution, the terminal identifies the nearest medical facility based on their current location. The server supports this by providing information to facilitate the booking process. The input here is the user's current location, and the output is information about the recommended nearest medical facility.

[0580] This process allows users to receive emotionally reassuring support and be guided to appropriate first aid and medical facilities.

[0581] (Application Example 2)

[0582] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0583] Providing appropriate solutions for pet health management while considering the owner's feelings is challenging. Furthermore, the lack of optimized food recommendations and purchasing procedures tailored to the pet's health condition highlights the need to improve the user experience. When a pet is unwell, owners often experience anxiety, and information and support are needed to alleviate this anxiety. Additionally, a system is desired that appropriately recommends the most suitable food for each pet's health condition and facilitates its purchase.

[0584] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0585] In this invention, the server includes means for receiving natural language data related to the pet's poor health, means for analyzing the user's emotional state and adjusting the information presentation style according to the emotional state, and means for recommending the most suitable food according to the pet's health condition. This makes it possible to provide appropriate first aid measures regarding pet health management, and to optimize the recommendation and purchase process of pet food, while taking into account the owner's emotions.

[0586] "Natural language data related to pet health problems" refers to information that pet owners have written themselves using language about unusual symptoms or signs that may affect their pet's health.

[0587] "Information processing means" refers to a computational function or software and hardware environment for analyzing received data and estimating the current health status of a pet.

[0588] "Means for generating first aid instructions" refers to a process or device for automatically creating and presenting appropriate initial responses to an estimated health condition.

[0589] "Means of presenting to the user" refers to a mechanism including a display device or audio output device for conveying generated information or instructions to the user visually or aurally.

[0590] "A means of identifying the nearest medical facility and assisting with reservations" refers to a process that uses information about the pet's health to determine the geographically closest animal hospital or other medical facility and provides an interface for making appointments.

[0591] "A means of analyzing a user's emotional state and adjusting the information presentation style according to that emotional state" refers to a system that evaluates the user's psychological state from the input data and dynamically changes the format and tone of information provision based on the results.

[0592] "A method for recommending the optimal food according to a pet's health condition" refers to a process that automatically selects and suggests foods and nutrients that are suitable for a pet's current health condition.

[0593] "Means of optimizing the purchase process" refers to a process that simplifies and makes purchase-related operations easier to use, based on the user's emotional state and other factors.

[0594] The system for implementing this invention mainly consists of a server and a user terminal. The user inputs natural language data about the pet's health using a smartphone application. This includes the pet's symptoms, changes in appearance, and changes in appetite. The data received by the terminal is transmitted to the server.

[0595] The server analyzes the input data using natural language processing software. For example, the Google Cloud Natural Language API is used. As a result of the analysis, keywords related to important symptoms are extracted, and basic data is generated to predict the pet's health status. In this process, pre-registered characteristic information about the pet is also taken into consideration. Generative AI models can be used in this process, with TensorFlow models being one example.

[0596] Furthermore, the server uses an emotion analysis engine to analyze the user's emotional state. This engine includes, for example, IBM Watson Tone Analyzer. Based on the analysis results, it determines how anxious or relaxed the user is and applies this information to subsequent processes.

[0597] Based on a prediction of the pet's health condition, appropriate first-aid instructions are generated, and recommended pet food and supplements are selected according to the pet's health status. This information is presented in detail and in an appropriate tone on the user's device. If the user is feeling very anxious, more detailed explanations and advice are added to provide reassurance.

[0598] Furthermore, when a user initiates a purchase, the server optimizes the process to ensure a smooth flow. For example, if a pet has a decreased appetite, the server automatically recommends foods that stimulate appetite and supplements that aid digestion, providing a process that allows for easy purchase.

[0599] An example of a prompt message for a generative AI model would be: "This user is concerned about their pet's health. Based on the entered symptom information and acquired emotion data, please recommend pet food. The current emotion state is 'calm'."

[0600] This method allows users to manage their pets' health more easily and with greater peace of mind, while also providing them with information on the most suitable food for their pets.

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The user operates a smartphone application and inputs natural language data about their pet's health. This data includes the pet's symptoms and any changes in its appearance. This data is then transmitted to a server by the device.

[0604] Step 2:

[0605] The server analyzes the received natural language data using tools such as the Google Cloud Natural Language API. The input is text data describing the pet's symptoms, and the output is the extraction of keywords indicating important symptoms. This analysis processes and interprets the data, generating basic information for evaluating the pet's health status.

[0606] Step 3:

[0607] The server uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is the user's emotional expressions associated with natural language data, and the output is an evaluation of the user's emotional state. This process determines whether the user is anxious or calm and applies that information to subsequent processes.

[0608] Step 4:

[0609] The server uses a generative AI model to predict the pet's health status, taking into account the pet's pre-registered characteristic information. The input is the set of symptom keywords and characteristic information extracted in the previous step, and the output is predicted pet health status data. In this process, the AI ​​model estimates the health status through calculations of the data.

[0610] Step 5:

[0611] The server generates first-aid instructions and pet food recommendations based on the predicted health status. The inputs are the predicted health status and the user's emotional state, while the output is a specific first-aid document and a list of recommended pet foods to present to the user. The recommendations are tailored to take the user's emotions into consideration and to provide reassurance.

[0612] Step 6:

[0613] The terminal displays first-aid instructions and pet food recommendations sent from the server to the user. Input consists of instruction documents and recommendation lists from the server, while output is information presented in visual or auditory formats. The information is provided to the user in a user-friendly and easy-to-understand format.

[0614] Step 7:

[0615] When a user indicates their intention to purchase pet food, the server assists in facilitating the purchase process. The input is the user's purchase request information, and the output is an optimized purchase process. This allows users to complete their purchase easily and with peace of mind.

[0616] 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.

[0617] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0618] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0619] [Fourth Embodiment]

[0620] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0621] 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.

[0622] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0623] 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.

[0624] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0625] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0626] 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.

[0627] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0628] 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.

[0629] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0630] The 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.

[0631] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0632] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] This invention is a system that helps pet owners respond quickly and accurately when they notice their pet is unwell. This system is implemented via terminals, servers, and a network.

[0634] First, users use devices such as smartphones or computers to input natural language data about their pet's health. For example, a user might input something like, "My cat is lethargic and not eating."

[0635] The device sends this input to the server via the communication network. The server analyzes the received text data using natural language processing (NLP) techniques to extract important keywords and phrases. Based on the extracted information, the server uses a machine learning model to estimate the pet's health status. In this estimation, characteristic information such as the pet's age, sex, and medical history is also taken into consideration.

[0636] Based on the health status estimated by the server, recommended first aid measures are determined. For example, if dehydration is likely, "give more water" will be suggested as first aid. These first aid instructions are sent from the server to the terminal and displayed to the user. The user can then use this information to administer first aid to their pet.

[0637] Furthermore, this system has the functionality to identify nearby veterinary hospitals and present their location and contact information to the user. If the user wishes to make an appointment, the system has the functionality to assist the hospital with the booking process. This allows users to visit a medical institution quickly and easily.

[0638] In this way, the present invention provides a practical solution for achieving both the health of pets and the psychological well-being of users.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] Users use devices such as smartphones or computers to input detailed natural language data about their pet's health. This data describes the symptoms the pet is exhibiting. For example, "The dog has lost its appetite and has diarrhea."

[0642] Step 2:

[0643] The terminal formats the data entered by the user into a digital format and creates an API request to send it to the server over the network.

[0644] Step 3:

[0645] The server receives the transmitted input data. Next, it uses a natural language processing (NLP) engine to extract important keywords and related phrases from the input data. This extracts the information necessary for analysis.

[0646] Step 4:

[0647] The server uses extracted keywords and pet characteristic information (age, sex, medical history, etc.) provided by the user in advance to run an AI model and estimate the pet's health status. This estimation also includes checking whether similar symptoms exist in the past database.

[0648] Step 5:

[0649] The server uses AI to select the most appropriate first aid based on the estimated health status. The content of the first aid is determined taking into account factors such as the pet's species and its usual health condition.

[0650] Step 6:

[0651] The server compiles information, including diagnostic results and details of first aid, and sends it to the terminal. This information includes immediate action to take for the pet.

[0652] Step 7:

[0653] The terminal displays information received from the server on the user interface, assisting the user in taking appropriate action. This includes reassuring explanations and specific procedures.

[0654] Step 8:

[0655] If the user wishes to seek medical attention as a further measure, the device will provide information on the nearest animal hospital. If a reservation support function is available, the device will send a reservation request to the selected hospital via the server.

[0656] This series of steps enables users to quickly and appropriately manage their pet's health and receive support for diagnosis and treatment at a medical facility when necessary.

[0657] (Example 1)

[0658] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] There is a need for a system that can help pet owners quickly and accurately identify when their pets are unwell and support them in providing appropriate first aid or visiting medical facilities. However, currently, accurately diagnosing a pet's illness requires specialized knowledge, and there is a problem in that rapid response is difficult, especially in emergencies. Therefore, there is a need to develop a user-friendly and effective support system that allows pet owners to easily assess their pet's health and take appropriate measures.

[0660] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0661] In this invention, the server includes means for acquiring natural language data relating to the health status of a pet, means for information processing means to analyze the natural language data using natural language processing technology and extract important keywords, and means for estimating the health status of the pet using a machine learning model based on the extracted important keywords. This allows pet owners to quickly recognize when their pet is unwell, receive instructions for appropriate first aid, and easily make reservations at medical facilities if necessary.

[0662] "Health status" refers to the physical and physiological condition of a pet, and is determined from its normal activity, eating habits, and other behaviors.

[0663] "Natural language data" refers to information expressed in natural language entered by the user, and is text data that describes the symptoms and behavior of pets.

[0664] "Information processing means" refers to systems and processes for analyzing received data, and methods for interpreting data using natural language processing techniques and machine learning models.

[0665] "Key terms" refer to words and phrases extracted from natural language data that are particularly meaningful in estimating a pet's health status.

[0666] A "machine learning model" is an algorithm or method that makes predictions and classifications based on past data. It learns patterns from the data and is used to estimate the health status of pets.

[0667] "First aid" refers to initial measures that can be taken quickly when a pet becomes ill, and are implemented before professional diagnosis or treatment.

[0668] A "medical facility" is a specialized institution that provides health checkups and treatment for pets, and includes facilities such as animal hospitals and clinics.

[0669] "Appointment booking" refers to the process of setting a date and time to visit a medical facility, and is carried out through a system as needed.

[0670] This invention provides a support system that enables pet owners to quickly recognize when their pets are unwell and take appropriate action. This system operates via terminals, servers, and a network, and utilizes natural language processing technology and machine learning.

[0671] First, users use devices such as smartphones or computers to input information about their pet's health in natural language. This input is expected to include specific symptoms and behaviors. For example, a user might input something like, "My cat has lost its appetite and is sleeping frequently."

[0672] The terminal sends the collected data to the server via the network. The server analyzes the received natural language data using natural language processing libraries (e.g., spaCy or TensorFlow). This analysis extracts important keywords and phrases.

[0673] The server uses this extracted information to estimate the pet's health status using a machine learning model (e.g., a model using Scikit-learn or PyTorch). This process also takes into account the pet's characteristics, such as age, sex, and medical history.

[0674] Based on the estimation results, the server generates appropriate first-aid instructions and sends that information to the terminal. The terminal displays the received instructions on the screen and prompts the user to take specific action.

[0675] Furthermore, the server uses location information to identify nearby medical facilities and, if necessary, assists with the booking process at medical institutions. This allows users to effectively manage their pets' health and receive appropriate medical services quickly.

[0676] When using this system, the system displays prompts such as, "Please enter information about your pet's health. For example, 'My dog ​​has been lethargic since this morning,'" to support the input of necessary data. This allows pet owners to easily record their pet's condition, enabling detailed analysis on the server side.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] Users input natural language data about their pet's health using a device. Specifically, they input text such as, "My dog ​​has been lethargic and has no appetite since this morning," through the device's input interface. This allows the user's observational information to be collected as digital data on the device.

[0680] Step 2:

[0681] The terminal receives natural language data entered by the user and prepares to send it to the server. The entered string data is converted into packets according to the network protocol and securely transferred to the server via the communication network.

[0682] Step 3:

[0683] The server analyzes the received text data using natural language processing techniques. First, it tokenizes the received data and extracts important keywords and phrases using a natural language processing library (e.g., spaCy) to analyze the language structure. The input is natural language text data, and the output is the important words and phrases extracted as features.

[0684] Step 4:

[0685] The server uses a machine learning model to estimate the pet's health status based on extracted key keywords. This prediction takes into account pre-stored characteristic information such as the pet's age, sex, and medical history. The input consists of key keywords and characteristic information, and the output is the estimated health status of the pet. Specifically, a classification model is executed using libraries such as Scikit-learn.

[0686] Step 5:

[0687] The server determines appropriate first aid based on the estimated health status. For example, it generates specific instructions such as "give more water." The input is the estimated health status, and the output is the first aid instruction.

[0688] Step 6:

[0689] The server sends first aid instructions to the terminal. The terminal displays these instructions on the user's screen. The user can then use this information to administer first aid to their pet. This allows the user to quickly and effectively take appropriate action.

[0690] (Application Example 1)

[0691] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] There is a need for a system that not only provides information to enable quick and appropriate first aid when a pet becomes ill, but also immediately suggests necessary related products and allows for quick purchase procedures. Such a system is expected to reduce the burden of maintaining pet health and increase convenience for pet owners.

[0693] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0694] In this invention, the server includes a device for receiving natural language data related to a pet's poor health, an information processing device for analyzing the natural language data and predicting the pet's health condition, a device for creating first aid instructions based on the predicted health condition, a device for suggesting relevant products according to the results of the analysis of the pet's health condition, and a device for enabling the purchase of suggested products through electronic payment. This enables the rapid provision of first aid instructions and the suggestion and purchase of relevant products.

[0695] "Natural language data related to pet health problems" refers to data in which pet owners express their observations and concerns about their pets' health in natural language.

[0696] An "information processing device" is a device that analyzes input data, understands its meaning and intent based on that data, and determines the next processing step.

[0697] "Predicting health status" means estimating a pet's health status using a specialized algorithm based on its current physical condition and past information.

[0698] "First aid instructions" are directives that specifically outline the immediate actions and responses necessary to maintain or restore a pet's health.

[0699] A "device that suggests related products" is a device that allows users to easily select and present appropriate products and services according to their pet's health condition and problems.

[0700] A "device that enables purchase via electronic payment" is a device that allows the proposed product to be purchased quickly through a specific online payment system.

[0701] The system for implementing this invention consists of a user's terminal, a server, and an associated network. The user uses a terminal such as a smartphone or computer to input natural language data regarding their pet's health condition. This data is transmitted from the terminal to the server via a communication network.

[0702] The server first analyzes the received natural language data using natural language processing (NLP) libraries. Specifically, libraries such as spaCy and Hugging Face Transformers are used to extract important keywords and phrases from the data. Based on the extracted information, the server uses machine learning algorithms to predict the pet's health status. This prediction uses machine learning frameworks such as TensorFlow and PyTorch, and also takes into account characteristic information such as the pet's age, sex, and medical history.

[0703] Once a health status prediction is made, the server generates first-aid instructions based on the results and sends them to the user's terminal. Following these instructions, the user can perform first aid on their pet. Furthermore, the server suggests and presents relevant products based on the pet's health condition. This relevant product information is obtained using shopping APIs such as Amazon and Rakuten. Users can purchase these products through electronic payment systems, such as Stripe and PayPal.

[0704] As a concrete example of its use, suppose a user inputs "My cat is drinking water frequently." In this case, the server will use the input data to assess the potential risk of diabetes and suggest purchasing diabetes-friendly food or testing kits.

[0705] Examples of prompts for a generative AI model include the following:

[0706] "Please suggest products I should buy to improve my pet's health. The situation is as follows: 'My cat is drinking water frequently.'"

[0707] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0708] Step 1:

[0709] Users input natural language data about their pet's health issues using a smartphone or computer. This data reflects the user's observations and concerns (e.g., "My cat is drinking water frequently"). The input data is stored on the device and transmitted to a server via the network.

[0710] Step 2:

[0711] The server analyzes the received natural language data using natural language processing (NLP) libraries (e.g., spaCy, Hugging Face Transformers). This analysis extracts important keywords and phrases from the text. Based on the input data, it identifies elements related to specific health conditions. The analysis results provide information that suggests potential health problems in pets.

[0712] Step 3:

[0713] The server applies machine learning algorithms (e.g., TensorFlow, PyTorch) to predict the health status using the extracted information. Pet characteristics (age, sex, medical history, etc.) are also considered. Based on the input information, the health status is output as a numerical value or diagnostic result. This output is then used to construct instructions for first aid.

[0714] Step 4:

[0715] The server generates first-aid instructions based on the predicted health condition. These instructions (e.g., "Give more fluids") are sent to the user's device, allowing the user to immediately take specific action.

[0716] Step 5:

[0717] The server retrieves the most suitable related products based on the pet's health condition via shopping APIs (e.g., Amazon, Rakuten Market). Product information, such as test kits or specific pet food, is suggested. The user can view the suggested products on their device.

[0718] Step 6:

[0719] When a user wishes to make a purchase, the process of purchasing the goods through an electronic payment system (e.g., Stripe, PayPal) is initiated. The server processes the payment information and guides the user through the steps to complete the transaction. In this way, the purchase process is completed smoothly.

[0720] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0721] This invention is a system that combines natural language data related to a pet's health problems with an emotion engine that recognizes the user's emotions. When a user inputs their pet's symptoms, the system can detect the user's emotional state from the words and context, and dynamically adapt the style of emergency treatment and information presentation accordingly.

[0722] To use the system, users first input natural language data about their pet's health using a terminal. This data is in the form of a description of how the pet is feeling unwell. The input includes the pet's name, symptoms, and any visible abnormalities.

[0723] The terminal sends the input data to the server, which uses natural language processing technology to extract important symptom keywords from the data. In addition, an emotion engine picks up emotional hints from the user's input and analyzes the user's emotions, such as whether they are tense or calm.

[0724] Based on the analysis results, the server uses an AI model to estimate the pet's health status. This process also takes into account the pet's pre-registered characteristics (age, sex, medical history, etc.).

[0725] Based on estimations, optimal first-aid instructions are generated, utilizing the results of the emotion engine's analysis. For example, if the user is feeling very anxious, the server will provide instructions with more detailed and reassuring explanations. These first-aid instructions and information on recommended medical facilities are presented to the user on their device.

[0726] If the user wishes to seek further medical attention, the device will identify the nearest veterinary hospital and provide assistance with making an appointment. The emotion engine also adjusts the guidance based on the user's current emotional state, providing a smooth and reassuring experience.

[0727] This system enables flexible adaptation of the user interface, which was difficult with conventional systems, resulting in more personalized support. Users will be able to use the system with peace of mind and confidence, in addition to managing their pets' health.

[0728] The following describes the processing flow.

[0729] Step 1:

[0730] Users input information about their pet's symptoms and condition into the device using natural language. For example, they might provide specific details such as, "My cat has lost its appetite and is lethargic."

[0731] Step 2:

[0732] The terminal prepares to send the input natural language data to the server via the communication network. During this process, the data is converted to an appropriate format and sent to the server as an API request.

[0733] Step 3:

[0734] The server receives natural language data from the terminal. The server then uses a natural language processing engine to analyze the data and detect important keywords and phrases related to the pet's symptoms.

[0735] Step 4:

[0736] Simultaneously, the server uses an emotion engine to detect the user's emotions from natural language data. This allows it to determine the user's emotional state, such as whether they are anxious, calm, or restless.

[0737] Step 5:

[0738] The server uses an AI model to estimate the pet's health status based on the analyzed data and the user's emotional state. Pet characteristics (e.g., age, sex, medical history) are taken into consideration during this process.

[0739] Step 6:

[0740] Based on the estimated health status, the server generates first-aid instructions. Using the results of the emotion engine, the instructions are adjusted to the user's emotional state. For example, if the user is anxious, more detailed explanations are added to provide greater reassurance.

[0741] Step 7:

[0742] The server transmits first-aid instructions and medical condition information to the terminal. The terminal displays this information on its user interface, clearly presenting it to the user.

[0743] Step 8:

[0744] When a user wishes to visit a medical institution, the device communicates this to the server. The server identifies the nearest animal hospital, provides the user with that information, and also offers a reservation assistance function.

[0745] Through this series of processes, the system aims to support pet health management and the user's emotional well-being.

[0746] (Example 2)

[0747] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] In today's world, where prompt and appropriate responses to pet illnesses are essential, it's crucial to ensure that pet owners can cope with the situation with peace of mind, even when they are emotionally unstable. However, conventional systems fail to provide information that takes the owner's emotional state into account, and they do not fully utilize individual pet information, making it difficult to provide appropriate first aid.

[0749] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0750] In this invention, the server includes a device that receives natural language data related to the pet's poor health, an information processing device that extracts symptom keywords and analyzes the user's emotional state, and a computing device that estimates the pet's health condition based on the extracted keywords and emotional data. This enables personalized first aid instructions that are tailored to the user's emotional state.

[0751] "Natural language data" refers to textual data from users describing the current health status of their pets, including information indicating symptoms or abnormalities in the pets.

[0752] "Symptom keywords" are important words and phrases related to a pet's health condition, extracted from natural language data.

[0753] "User emotional state" refers to the mental state or emotional tendencies expressed when a user inputs natural language data.

[0754] An "information processing device" is a device or software used to analyze natural language data, extract symptom keywords, and analyze the emotional state of a user.

[0755] A "computational device" refers to a device or infrastructure with computing capabilities for estimating a pet's health status based on extracted symptom keywords and emotional data.

[0756] "First aid instructions" refer to information about the actions that should be taken immediately in response to a pet's illness, based on its estimated health condition.

[0757] A "medical facility" refers to a veterinary facility or hospital that provides medical care for pets.

[0758] This invention is a system aimed at quickly assessing a pet's health condition and providing owners with appropriate first aid and peace of mind.

[0759] The terminal first has a means of receiving natural language data entered by the user. This includes information processing devices such as smartphones and personal computers, and the user uses the terminal to input the pet's symptoms and related information. An example of the input content might be in the format of "My cat Milk is lethargic and has no appetite."

[0760] The terminal sends this natural language data to the server. The server, acting as an information processing device, first uses spaCy, a natural language processing library, to extract keywords related to the symptoms. It also uses NLTK for emotion analysis to analyze the user's emotional state. This allows the server to understand whether the user is anxious or calm.

[0761] Based on this information, the server uses the GPT-3 generative AI model to estimate the pet's health status, taking into account the extracted keywords and emotional state. The calculated results also incorporate the pet's pre-registration information, resulting in a more accurate estimate of the pet's health status.

[0762] The server generates first-aid instructions based on the estimated results and presents them to the user via the terminal. For example, if the user is feeling very anxious, it can add detailed and reassuring explanations such as, "Don't panic, warm up some milk, and let the baby rest for a while."

[0763] In addition, if a user wishes to visit a medical institution, the device will use their current location to identify the nearest medical facility and assist with making an appointment. The suggested medical facilities will also be presented in a way that takes the user's feelings into consideration.

[0764] This system overcomes the limitations of conventional information provision systems, enabling personalized, safe, and reliable medical support for users.

[0765] A concrete example of a prompt might be the input, "My dog, Coco, has been coughing since this morning. What should I do?" Based on this prompt, the generative AI model provides appropriate first aid and explanations.

[0766] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0767] Step 1:

[0768] The user provides natural language input about their pet's health condition to the device. The device receives input such as, "My cat, Milk, is lethargic and has no appetite." This input is processed as the starting data for the entire system.

[0769] Step 2:

[0770] The terminal sends the received natural language input data to the server. This transmission takes place over the internet using secure communication methods. The next process begins as soon as the server receives the data.

[0771] Step 3:

[0772] The server analyzes the received natural language data using an information processing device. Using a natural language processing library (e.g., spaCy), symptom keywords such as "feeling unwell" and "loss of appetite" are extracted from the input text. The input for this process is the user's natural language data, and the output is a list of extracted keywords.

[0773] Step 4:

[0774] The server uses an emotion analysis engine to analyze the user's emotional state from their input. For example, a natural language processing library (such as NLTK) is used to extract emotions like "anxiety" and "worry." At this stage, the input is the user's natural language text, and the output is an index of their emotional state.

[0775] Step 5:

[0776] The server uses the extracted symptom keywords and emotional states as a dataset to estimate the pet's health status using a generative AI model (e.g., GPT-3). The input here is symptom keywords and emotional indicators, and the output is information about the estimated health status. This process allows for the planning of first aid measures appropriate to the pet's current condition.

[0777] Step 6:

[0778] The server generates first-aid instructions based on the estimation results and sends them to the terminal. The generated instructions include smooth and reassuring explanations that take the user's emotions into consideration. The input is information about the estimated health status, and the output is first-aid instructions presented to the user.

[0779] Step 7:

[0780] When a user wishes to visit a medical institution, the terminal identifies the nearest medical facility based on their current location. The server supports this by providing information to facilitate the booking process. The input here is the user's current location, and the output is information about the recommended nearest medical facility.

[0781] This process allows users to receive emotionally reassuring support and be guided to appropriate first aid and medical facilities.

[0782] (Application Example 2)

[0783] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0784] Providing appropriate solutions for pet health management while considering the owner's feelings is challenging. Furthermore, the lack of optimized food recommendations and purchasing procedures tailored to the pet's health condition highlights the need to improve the user experience. When a pet is unwell, owners often experience anxiety, and information and support are needed to alleviate this anxiety. Additionally, a system is desired that appropriately recommends the most suitable food for each pet's health condition and facilitates its purchase.

[0785] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0786] In this invention, the server includes means for receiving natural language data related to the pet's poor health, means for analyzing the user's emotional state and adjusting the information presentation style according to the emotional state, and means for recommending the most suitable food according to the pet's health condition. This makes it possible to provide appropriate first aid measures regarding pet health management, and to optimize the recommendation and purchase process of pet food, while taking into account the owner's emotions.

[0787] "Natural language data related to pet health problems" refers to information that pet owners have written themselves using language about unusual symptoms or signs that may affect their pet's health.

[0788] "Information processing means" refers to a computational function or software and hardware environment for analyzing received data and estimating the current health status of a pet.

[0789] "Means for generating first aid instructions" refers to a process or device for automatically creating and presenting appropriate initial responses to an estimated health condition.

[0790] "Means of presenting to the user" refers to a mechanism including a display device or audio output device for conveying generated information or instructions to the user visually or aurally.

[0791] "A means of identifying the nearest medical facility and assisting with reservations" refers to a process that uses information about the pet's health to determine the geographically closest animal hospital or other medical facility and provides an interface for making appointments.

[0792] "A means of analyzing a user's emotional state and adjusting the information presentation style according to that emotional state" refers to a system that evaluates the user's psychological state from the input data and dynamically changes the format and tone of information provision based on the results.

[0793] "A method for recommending the optimal food according to a pet's health condition" refers to a process that automatically selects and suggests foods and nutrients that are suitable for a pet's current health condition.

[0794] "Means of optimizing the purchase process" refers to a process that simplifies and makes purchase-related operations easier to use, based on the user's emotional state and other factors.

[0795] The system for implementing this invention mainly consists of a server and a user terminal. The user inputs natural language data about the pet's health using a smartphone application. This includes the pet's symptoms, changes in appearance, and changes in appetite. The data received by the terminal is transmitted to the server.

[0796] The server analyzes the input data using natural language processing software. For example, the Google Cloud Natural Language API is used. As a result of the analysis, keywords related to important symptoms are extracted, and basic data is generated to predict the pet's health status. In this process, pre-registered characteristic information about the pet is also taken into consideration. Generative AI models can be used in this process, with TensorFlow models being one example.

[0797] Furthermore, the server uses an emotion analysis engine to analyze the user's emotional state. This engine includes, for example, IBM Watson Tone Analyzer. Based on the analysis results, it determines how anxious or relaxed the user is and applies this information to subsequent processes.

[0798] Based on a prediction of the pet's health condition, appropriate first-aid instructions are generated, and recommended pet food and supplements are selected according to the pet's health status. This information is presented in detail and in an appropriate tone on the user's device. If the user is feeling very anxious, more detailed explanations and advice are added to provide reassurance.

[0799] Furthermore, when a user initiates a purchase, the server optimizes the process to ensure a smooth flow. For example, if a pet has a decreased appetite, the server automatically recommends foods that stimulate appetite and supplements that aid digestion, providing a process that allows for easy purchase.

[0800] An example of a prompt message for a generative AI model would be: "This user is concerned about their pet's health. Based on the entered symptom information and acquired emotion data, please recommend pet food. The current emotion state is 'calm'."

[0801] This method allows users to manage their pets' health more easily and with greater peace of mind, while also providing them with information on the most suitable food for their pets.

[0802] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0803] Step 1:

[0804] The user operates a smartphone application and inputs natural language data about their pet's health. This data includes the pet's symptoms and any changes in its appearance. This data is then transmitted to a server by the device.

[0805] Step 2:

[0806] The server analyzes the received natural language data using tools such as the Google Cloud Natural Language API. The input is text data describing the pet's symptoms, and the output is the extraction of keywords indicating important symptoms. This analysis processes and interprets the data, generating basic information for evaluating the pet's health status.

[0807] Step 3:

[0808] The server uses IBM Watson Tone Analyzer to analyze the user's emotional state. The input is the user's emotional expressions associated with natural language data, and the output is an evaluation of the user's emotional state. This process determines whether the user is anxious or calm and applies that information to subsequent processes.

[0809] Step 4:

[0810] The server uses a generative AI model to predict the pet's health status, taking into account the pet's pre-registered characteristic information. The input is the set of symptom keywords and characteristic information extracted in the previous step, and the output is predicted pet health status data. In this process, the AI ​​model estimates the health status through calculations of the data.

[0811] Step 5:

[0812] The server generates first-aid instructions and pet food recommendations based on the predicted health status. The inputs are the predicted health status and the user's emotional state, while the output is a specific first-aid document and a list of recommended pet foods to present to the user. The recommendations are tailored to take the user's emotions into consideration and to provide reassurance.

[0813] Step 6:

[0814] The terminal displays first-aid instructions and pet food recommendations sent from the server to the user. Input consists of instruction documents and recommendation lists from the server, while output is information presented in visual or auditory formats. The information is provided to the user in a user-friendly and easy-to-understand format.

[0815] Step 7:

[0816] When a user indicates their intention to purchase pet food, the server assists in facilitating the purchase process. The input is the user's purchase request information, and the output is an optimized purchase process. This allows users to complete their purchase easily and with peace of mind.

[0817] 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.

[0818] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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 with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0819] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0820] 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.

[0821] Figure 9 shows an 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.

[0822] 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.

[0823] 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.

[0824] 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, motorcycles, etc., 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, for example, based 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.

[0825] 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."

[0826] 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.

[0827] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0828] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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 the like 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.

[0837] 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.

[0838] The following is further disclosed regarding the embodiments described above.

[0839] (Claim 1)

[0840] A means of receiving natural language data related to a pet's health problems,

[0841] Information processing means for analyzing the aforementioned natural language data to estimate the health status of a pet,

[0842] A means for generating first aid instructions based on estimated health status,

[0843] A means for presenting the user with instructions for the aforementioned first aid,

[0844] A means to identify the nearest medical institution and assist with making an appointment,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, which stores characteristic information of a pet and uses the information processing means to estimate the pet's health status by taking said characteristic information into consideration.

[0848] (Claim 3)

[0849] The system according to claim 1, wherein the processing for analyzing the aforementioned natural language data is performed using a machine learning model.

[0850] "Example 1"

[0851] (Claim 1)

[0852] A means of acquiring natural language data regarding the health status of pets,

[0853] Means for transmitting the aforementioned natural language data to a processing device via a communication network,

[0854] Information processing means includes means for analyzing the natural language data using natural language processing technology and extracting important words,

[0855] A means for estimating the health status of a pet using a machine learning model based on the extracted important keywords,

[0856] A means for determining emergency treatment based on the estimated health condition,

[0857] A means for presenting the aforementioned first aid measures to the dialogue device,

[0858] A means to identify nearby medical facilities and assist with the appointment process,

[0859] A system that includes this.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein a pet's characteristic information is recorded and an information processing means estimates its health status taking said characteristic information into consideration.

[0862] (Claim 3)

[0863] The system according to claim 1, wherein the analysis of the natural language data is performed using a natural language processing library, and the estimation of health status is performed using a machine learning model.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A device that receives natural language data related to a pet's health problems,

[0867] An information processing device that analyzes the aforementioned natural language data to predict the health status of a pet,

[0868] A device that generates first aid instructions based on predicted health conditions,

[0869] A device that displays the instructions for the aforementioned first aid to the user,

[0870] A device that identifies the nearest medical institution and assists with making reservations,

[0871] A device that suggests related products based on the results of analyzing the health status of a pet,

[0872] A device that allows the purchase of products proposed through electronic payment,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, wherein the system stores characteristic information of a pet, and the information processing device predicts the pet's health status by considering the characteristic information.

[0876] (Claim 3)

[0877] The system according to claim 1, wherein the processing for analyzing the aforementioned natural language data is performed using a machine learning algorithm.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A device that receives natural language data related to a pet's health problems,

[0881] An information processing device that extracts symptom keywords from the aforementioned natural language data and analyzes the user's emotional state,

[0882] A computing device that estimates the health status of a pet based on extracted keywords and analyzed emotional data,

[0883] A means of generating first aid instructions based on the estimated health condition and adjusting the information to match the user's emotions,

[0884] An interface that visually presents the aforementioned first aid instructions to the user,

[0885] A means to identify the nearest medical facility and assist with making an appointment,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, which stores characteristic information of a pet and has the computing device estimate the health status of the pet by taking said characteristic information into consideration.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the information processing device analyzes natural language data and the user's emotional state using a learning model.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A means of receiving natural language data related to a pet's health problems,

[0894] Information processing means for analyzing the aforementioned natural language data to estimate the health status of a pet,

[0895] A means for generating first aid instructions based on estimated health status,

[0896] A means for presenting the user with instructions for the aforementioned first aid,

[0897] A means to identify the nearest medical institution and assist with making an appointment,

[0898] A means for analyzing the user's emotional state and adjusting the information presentation style according to that emotional state,

[0899] A means of recommending the best food for your pet according to its health condition,

[0900] A means of optimizing the purchase process based on the user's emotional state,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, which stores characteristic information of a pet and uses the information processing means to estimate the pet's health status by taking said characteristic information into consideration.

[0904] (Claim 3)

[0905] The system according to claim 1, wherein the processing for analyzing the aforementioned natural language data is performed using a generative AI model. [Explanation of Symbols]

[0906] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving natural language data related to a pet's health problems, Information processing means for analyzing the aforementioned natural language data to estimate the health status of a pet, A means for generating first aid instructions based on estimated health status, A means for presenting the user with instructions for the aforementioned first aid, A means to identify the nearest medical institution and assist with making an appointment, A system that includes this.

2. The system according to claim 1, which stores characteristic information of a pet and uses the information processing means to estimate the pet's health status by taking the characteristic information into consideration.

3. The system according to claim 1, wherein the processing for analyzing the natural language data is performed using a machine learning model.

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