system

Abstract

We provide the system. [Solution] Means for collecting animal behavioral information, Means for analyzing the aforementioned behavioral information to detect abnormal behavioral patterns, A means for generating predictive information using detected abnormal behavior patterns, A means of notifying the user of the generated prediction information, A system that includes this.

Description

Technical Field

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

[0006] "Animal behavioral information" refers to data on animal biosignals, movement patterns, and environmental changes, and is used to detect abnormal behavior and generate predictive information.

[0007] An "abnormal behavioral pattern" refers to a pattern of behavior that deviates statistically significantly from the normal behavior of animals, and it forms the basis for generating predictive information.

[0008] "Predictive information" refers to information that indicates the likelihood of future natural disasters or market fluctuations, generated based on collected and analyzed animal behavior data.

[0009] "User notification" refers to the process of communicating predictive information generated via the device to the user, and is a means of quickly taking necessary measures. [Brief explanation of the drawing]

[0010] [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]

[0011] 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.

[0012] First, let's explain the terminology used in the following explanation.

[0013] In the following embodiments, the numbered 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.

[0014] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0015] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0016] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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).

[0017] 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."

[0018] [First Embodiment]

[0019] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0020] 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.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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".

[0031] This invention relates to a system that analyzes abnormal behavioral patterns based on animal behavioral information, generates predictive information, and notifies the user. This system consists of sensors that collect animal behavioral information, a server that processes the data, and a terminal that notifies the user of the information.

[0032] The server collects diverse data, such as animal biosignals, movement patterns, and environmental changes, through sensors. The collected data is transmitted to the server in real time and stored in a database. Next, the server uses machine learning algorithms to analyze this data and detect the presence or absence of patterns that may indicate abnormal behavior.

[0033] When an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. This predictive information includes predictions of the likelihood of natural disasters and market fluctuations. The generated information is visualized by the server and converted into an easily understandable format.

[0034] The terminal receives predictive information transmitted from the server and notifies the user with an alert. This information allows the user to take quick action and make decisions. For example, a user who receives earthquake prediction information is expected to be prompted to evacuate to a safe location. In investment, users can also modify their trading plans based on predictive information regarding anticipated market fluctuations.

[0035] In this way, the present invention effectively utilizes animal behavioral information to achieve accurate and rapid prediction through abnormal behavior. As a result, it can significantly improve the prevention of natural disasters and responsiveness to market fluctuations, providing users with beneficial decision-making support.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The server controls sensors to collect animal behavioral information, monitoring data including animal biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0039] Step 2:

[0040] The server applies machine learning algorithms to the collected behavioral information to detect abnormal behavior that deviates from the animal's normal behavioral patterns. This identifies the presence of abnormal behavioral patterns and assigns them anomaly tags.

[0041] Step 3:

[0042] The server analyzes detected abnormal behavior patterns and generates predictive information based on them. This generated predictive information includes predictions of the likelihood of natural disasters and market fluctuations, and this information is converted into an appropriate format for visualization.

[0043] Step 4:

[0044] The server sends the generated prediction information to the terminal. The terminal receives this information and displays it as an alert or notification so that the user can check it immediately.

[0045] Step 5:

[0046] Users receive predictive information from their devices and respond quickly based on it. They can then make decisions and take action, such as preparing for earthquakes or reviewing investment plans.

[0047] (Example 1)

[0048] 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."

[0049] The problem that this invention aims to solve is to efficiently analyze animal behavioral information and quickly and accurately detect abnormal behavior. Furthermore, it aims to generate predictive information based on this information, enabling users to respond quickly to natural disasters and market fluctuations.

[0050] 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.

[0051] In this invention, the server includes means for a device that collects animal behavior information, means for analyzing the behavior information using a machine learning algorithm to detect abnormal behavior patterns, and means for generating predictive information using the detected abnormal behavior patterns and visualizing that information. This makes it possible to accurately detect abnormal behavior and provide rapid predictive information based on animal behavior data.

[0052] "Animal behavioral information" is a general term for data including animal biosignals, movement patterns, and environmental changes, and represents information that indicates the behavioral state of animals.

[0053] A "machine learning algorithm" is a computational method that analyzes diverse data, automatically learns patterns and regularities, and performs predictions and classifications.

[0054] An "abnormal behavioral pattern" refers to a sequence of animal activities that deviate from normal behavior and suggest some kind of risk or abnormal situation.

[0055] "Predictive information" refers to information generated to predict future events and situations based on collected and analyzed data.

[0056] "Visualization" is a technique that displays analyzed data in visual forms such as graphs and charts to make it easier to understand.

[0057] This invention is a system that analyzes abnormal behavior patterns based on animal behavioral information, generates predictive information, and notifies the user.

[0058] The server first collects diverse data, such as animal biosignals, movement patterns, and environmental changes, using specialized sensors. These sensors consist of GPS, accelerometers, heart rate monitors, etc., and can acquire data in real time. The collected data is organized and stored using a database management system. The server then analyzes this data using machine learning algorithms. Specifically, it builds models that automatically detect abnormal behavior using Python and Scikit-learn.

[0059] Next, if an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. For example, if behavior that could be interpreted as an earthquake precursor is detected, this information is used to assess the risk of natural disasters. The generated predictive information is visualized using libraries such as Matplotlib and Plotly, and converted into an easily understandable format.

[0060] The terminal receives visualization information sent from the server and promptly notifies the user. The user can then check this information on the terminal and take appropriate action as needed, such as beginning preparations to evacuate to a safe place.

[0061] As a concrete example, the prompt statement is written as follows:

[0062] "Please help design a system that analyzes animal behavior data and detects abnormal patterns. Propose specific implementation methods and usable machine learning algorithms."

[0063] A system configured in this way can provide rapid and accurate predictive information from animal behavior data, effectively supporting user decision-making.

[0064] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0065] Step 1:

[0066] The server collects data on animal biosignals, movement patterns, and environmental changes via sensors. These sensors include GPS, accelerometers, and heart rate monitors. Input is raw data obtained from the sensors, and output is organized information recorded in a database. The server saves this data to the database in real time.

[0067] Step 2:

[0068] The server performs preprocessing on the collected data, such as noise reduction and missing value imputation. This improves the quality of the data for analysis. The input is the original data recorded in the database, and the output is the cleaned data. Specifically, it uses the Python Pandas library to imputate missing values ​​and remove outliers.

[0069] Step 3:

[0070] The server feeds preprocessed data into a machine learning algorithm to detect abnormal behavior patterns. This process uses Scikit-learn to build an anomaly detection model and identify patterns of normal and abnormal behavior. The input is the cleaned data, and the output is the result of detecting abnormal behavior patterns.

[0071] Step 4:

[0072] When an abnormal behavior pattern is detected, the server generates predictive information based on that pattern. It utilizes TENSORFLOW® and other AI models to assess future risks and create predictive information. The input is information about abnormal behavior patterns, and the output is predictive information. This predictive information includes risks such as natural disasters and potential market fluctuations.

[0073] Step 5:

[0074] The server visualizes the generated prediction information and sends it to the terminal in an easy-to-understand format. Visualization uses tools such as Matplotlib and Plotly. The input is the generated prediction information, and the output is the visualized information. Specific operations include the process of creating graphs and charts.

[0075] Step 6:

[0076] The terminal notifies the user of visualization information received from the server. The user reviews this notification and takes appropriate action as needed. The input is the visualization information displayed on the terminal, and the output is the user's decision. A concrete example is the user's action of starting evacuation preparations.

[0077] (Application Example 1)

[0078] 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."

[0079] While security is increasingly important in modern society, traditional security systems are costly and complex to operate, making them difficult for many individuals and businesses to implement. Furthermore, early detection of unauthorized intrusions and anomalies is challenging, highlighting the need for effective systems that enable rapid response. Therefore, the challenge lies in developing technologies that utilize animal behavior to provide cost-effective security systems and enable rapid response.

[0080] 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.

[0081] In this invention, the server includes means for collecting animal behavior information, means for analyzing abnormal behavior patterns, and means for generating predictive information using the abnormal behavior patterns. This makes it possible to quickly notify the user of predictive information indicating the possibility of unauthorized intrusion based on animal behavior as a security alarm, prompting appropriate action.

[0082] "Animal behavioral information" refers to information that includes biological signals, movement patterns, environmental changes, or signs of abnormal external stimuli in animals.

[0083] An "abnormal behavior pattern" is a behavioral pattern that serves as the basis for identifying unusual animal behavior and generating related predictive information.

[0084] "Predictive information" is information generated based on abnormal animal behavior that indicates the possibility of natural disasters or unauthorized intrusions.

[0085] "Means of notifying the user" refers to methods for communicating generated predictive information to the user via their device and prompting them to take necessary actions.

[0086] "Means of prompt response" refer to methods that help users take appropriate action quickly when they receive predictive information.

[0087] A "security alert" is information that warns users of the possibility of unauthorized intrusion or abnormal events based on abnormal behavior analyzed from animal behavior.

[0088] This invention is a system that provides security alerts based on animal behavior information. The system consists of a sensor device for collecting animal behavior information, a server for analyzing the data, and a terminal for notifying users of the information.

[0089] The sensor device uses a Raspberry Pi to collect animal behavior information in real time. The collected data is sent to a server via the network. The server functions as a web server using Flask and stores the received data in a database. OpenCV is used to analyze images of the animals, and machine learning algorithms using TensorFlow and Scikit-learn are used to identify abnormal behavior patterns.

[0090] When abnormal behavior is detected, the server uses Firebase Cloud Messaging to send the generated predictive information as a push notification to the user's smartphone. The user can check this notification on their device and take appropriate action if a quick response is required.

[0091] As a concrete example, imagine a dog kept as a pet in a rural area that detects the approach of a stranger. This system monitors the dog's unusual behavior, quickly generates an alarm, and sends a notification to the user's smartphone. The user receives the notification, checks the footage from nearby surveillance cameras, and can take measures against the intruder.

[0092] An example of a prompt to input into a generative AI model is, "Propose a system architecture for detecting anomalies from animal behavior data and providing rapid security alerts." By implementing a system based on this prompt, an efficient security system utilizing animal behavior can be realized.

[0093] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0094] Step 1:

[0095] The server receives animal behavior information from sensor devices. The sensors capture the animal's movements and generate digital data via a Raspberry Pi. This data includes the animal's biosignals, movement patterns, and environmental changes. The input is analog data, and the output is digitized behavioral data.

[0096] Step 2:

[0097] The server stores the received data in a database. Using a database management system, the data is stored in a structured format. The input is digitized behavioral data, and the output is the data stored in the database.

[0098] Step 3:

[0099] The server uses OpenCV to analyze animal behavioral image data. The image data is preprocessed and filtered to identify animal movements. The input is unprocessed animal image data, and the output is filtered animal movement data.

[0100] Step 4:

[0101] The server analyzes abnormal behavior patterns using machine learning algorithms based on TensorFlow and Scikit-learn. The server applies a model to detect anomalies by comparing them with previously known data. The input is filtered animal movement data, and the output is the result of detecting abnormal behavior patterns.

[0102] Step 5:

[0103] The server sends notifications to the user's smartphone via Firebase Cloud Messaging. A security alarm is generated based on the detected abnormal behavior and sent as a push notification in real time. The input is the result of detecting abnormal behavior patterns, and the output is the alarm notification to the user.

[0104] Step 6:

[0105] The user receives a notification on their device and checks its contents. The application on the device displays the received alert as a pop-up notification, and the user takes additional action as needed, such as viewing camera footage. The input is the notification content, and the output is the user's confirmation and response action.

[0106] 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.

[0107] This invention is a system that simultaneously analyzes animal behavior information and user emotions to provide more accurate predictive information and optimize user responses. This system consists of sensors that acquire animal behavior information, a server that performs data analysis, a terminal that displays the information, and an emotion engine that recognizes user emotions.

[0108] The server collects data, including animal biosignals and movement patterns, and executes algorithms to process it effectively. The collected data is analyzed to detect patterns of abnormal behavior, and predictive information is generated based on the results. This predictive information includes forecasts of earthquake probability and market fluctuations.

[0109] The emotion engine estimates the user's emotions from their voice tone, facial expressions, and physical changes. The device sends this emotion data to a server, which adjusts the alert level of predictive information based on the user's emotional state. For example, if the system detects that the user is stressed, the alert content and display method can be made more intuitive to encourage quick and appropriate action. Furthermore, the emotion engine optimizes specific action suggestions based on the user's emotions, helping the user act with greater confidence.

[0110] For example, if a user who has received earthquake prediction information is detected to be in a state of emotional tension, the system will emphasize detailed evacuation procedures and emergency contact information. Similarly, in market fluctuation predictions for investment activities, the system will encourage calm decision-making by presenting reassuring information to alleviate anxiety.

[0111] In this way, the present invention integrates animal behavior information and user emotional information to provide more accurate and user-centric predictive information and countermeasures. This contributes to mitigating damage from natural disasters, improving the efficiency of investment activities, and enhancing user confidence.

[0112] The following describes the processing flow.

[0113] Step 1:

[0114] The server controls sensors to collect animal behavioral information, monitoring biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0115] Step 2:

[0116] The server uses machine learning algorithms to analyze collected behavioral data and detect deviations from normal patterns. Once an abnormal behavioral pattern is identified, it is tagged with an anomaly tag.

[0117] Step 3:

[0118] The server generates predictive information based on detected abnormal behavior patterns. This predicted information concerns the likelihood of earthquakes and market fluctuations, and is prepared in an easy-to-understand format using visualization tools.

[0119] Step 4:

[0120] A device containing an emotion engine collects the user's voice tone and facial expression data to analyze their emotional state. This identifies the user's current emotional state, and the data is sent to a server.

[0121] Step 5:

[0122] The server receives user emotional state data and adjusts the alert priority of predictive information. If user stress or anxiety is detected, the alert is emphasized or adjusted accordingly.

[0123] Step 6:

[0124] The device receives pre-configured forecast information from the server and displays customized alerts and notifications to the user. These notifications are designed to allow the user to respond quickly.

[0125] Step 7:

[0126] Based on the provided forecast information and alerts, users can take specific actions such as earthquake evacuation or reviewing their investment policies. The system also includes emotionally-based suggestions to help users act with confidence.

[0127] (Example 2)

[0128] 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".

[0129] In modern society, predicting natural disasters and market fluctuations brings many benefits, but mere data analysis has the challenge of not being able to provide responses tailored to each individual's situation. Furthermore, conventional systems do not take into account user emotions and provide one-sided notifications, which can cause users to feel stressed or have their anxiety amplified. There is a need to solve these problems and realize the provision of more personalized and adaptive predictive information.

[0130] 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.

[0131] In this invention, the server includes a device for acquiring animal behavior data, an algorithm for analyzing the data and identifying abnormal behavior patterns, and a function for creating predictive information based on the identified patterns. This enables the integrated use of animal behavior and the user's emotional state to provide more accurate and personalized predictive information.

[0132] "Animal behavioral data" refers to a collection of information including animal biological signals, movement patterns, and changes in the surrounding environment, which is acquired through specific technological means.

[0133] A "device" is a piece of equipment consisting of hardware or software that has a specific function and is used to acquire or analyze data.

[0134] An "algorithm" is a sequence of computational procedures or rules used for data analysis, applied to identify abnormal behavioral patterns.

[0135] "Predictive information" refers to information generated based on animal behavior data and other relevant information that suggests future natural disasters, market fluctuations, and other events.

[0136] "User emotional state" refers to the emotional state inferred from the user's tone of voice, facial expressions, physical changes, etc.

[0137] An "engine" is a program or system that analyzes data and performs specific functions; in this context, it is used to recognize the user's emotional state.

[0138] A "terminal" is an electronic device used to display or notify a user of information, and includes personal computers and smartphones.

[0139] A "natural disaster" is an event that causes damage due to natural phenomena such as earthquakes and typhoons.

[0140] "Adaptation" refers to changing or adjusting a system or function in accordance with specific situations or conditions, and specifically means adjusting predictive information according to the user's emotional state.

[0141] This invention specifically provides a technology for generating predictive information using animal behavior data and adaptively notifying users of this information.

[0142] Data acquisition and hardware configuration

[0143] The server receives behavioral data from multiple sensors attached to the animals. This data includes biological signals and movement patterns, and the sensors transmit the information to the server via Bluetooth or Wi-Fi.

[0144] Data analysis and software configuration

[0145] The server analyzes collected behavioral data in real time using a dedicated algorithm. This makes it possible to identify abnormal behavior patterns using machine learning techniques. The analyzed results are automatically generated as predictive information such as earthquakes and market fluctuations.

[0146] Recognition and adaptation of user emotions

[0147] The emotion engine collects the user's voice tone, facial expressions, and physical changes through the camera and microphone, and analyzes this data to determine the user's emotional state. The device sends this emotional data to a server, allowing predictive notifications to be adjusted according to the user's emotions. If the user shows high levels of stress, the notification content is changed to be more intuitive and easier to understand to alleviate anxiety.

[0148] Examples of specific cases and prompt statements

[0149] For example, if the device detects that a user is in a state of tension after receiving earthquake prediction information, it will highlight detailed evacuation procedures and provide voice instructions. Furthermore, when providing information on market trends, it will present reassuring elements to encourage calm decision-making by the user.

[0150] Examples of prompt messages include, "Please tell me how to predict the likelihood of an earthquake based on animal behavior data and adjust the alert content according to the user's emotional state."

[0151] This system aims to help users maximize their profits by predicting natural disasters and market fluctuations, and to provide adaptive information tailored to each individual user.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server receives behavioral data, such as biological signals and movement patterns, from sensors attached to animals. This data is typically transmitted via Bluetooth or Wi-Fi. Inputs include biological signal data and movement patterns, and the output is filtered, clean data. Specifically, the server performs initial filtering of the received data to remove noise.

[0155] Step 2:

[0156] The server runs a dedicated machine learning algorithm using filtered animal behavior data. The input includes filtered, clean data, and the output is the identification of abnormal behavior patterns. Specifically, the server retrains itself by comparing it with historical data to detect anomalies.

[0157] Step 3:

[0158] The server generates predictive information based on identified anomaly patterns. The input is anomaly behavior patterns, and the output is predictive information such as earthquakes and market fluctuations. Specifically, the server utilizes a generation AI model, supplementing its predictions by referencing historical data and external information.

[0159] Step 4:

[0160] The emotion engine acquires the user's voice tone, facial expressions, and physical changes from the camera and microphone, and analyzes their emotional state. Input includes the user's voice and video data, and output is the user's emotional state. Specifically, the device sends the processed emotional data to the server in real time.

[0161] Step 5:

[0162] The server adjusts the content of predictive notifications based on the user's emotional state. The input includes the user's emotional state, and the output is a notification adapted to that emotion. Specifically, the server modifies the notification content based on the emotional state, for example, making warnings more approachable.

[0163] Step 6:

[0164] The device provides the user with adjusted predictive information. The input is the adjusted notification information, and the output is the completion of notification to the user. Specifically, the device displays alerts to the user using voice and visuals, and utilizes vibration and voice guidance in emergencies.

[0165] (Application Example 2)

[0166] 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".

[0167] In physical stores, there is a challenge in responding flexibly to customers' emotional states. In particular, there is a challenge in how to help customers relax and feel satisfied when they are stressed. Traditional systems make it difficult to individually optimize the customer experience, and there is a need for effective means to improve customer satisfaction.

[0168] 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.

[0169] In this invention, the server includes means for collecting animal behavior information, means for analyzing the user's emotional state, and means for adjusting predictive information notifications based on the user's emotional state. This makes it possible to provide a relaxing experience tailored to the customer's emotional state in a physical store.

[0170] "Animal behavioral information" refers to data obtained from animals, such as biosignals, movement patterns, and information about environmental changes.

[0171] An "abnormal behavioral pattern" is a pattern of unusual behavior detected by analyzing animal behavioral information.

[0172] "Predictive information" refers to information about future events generated based on abnormal behavioral patterns.

[0173] A "user" is someone who uses the system or someone who receives notifications.

[0174] "Emotional state" refers to the psychological condition judged based on the user's voice, facial expressions, physical changes, etc.

[0175] "Adjustment" refers to changing the way predictive information is notified and the content of that information based on the user's emotional state.

[0176] A "system" is a device or mechanism that integrates multiple means to achieve a specific function.

[0177] The system that implements this application is designed to optimize the customer experience in physical stores by collecting animal behavioral information and analyzing the emotional state of users.

[0178] The server uses sensor devices to collect animal behavioral information. These sensors acquire the animals' biosignals and movement patterns, and the server performs data analysis. This analysis detects abnormal behavioral patterns and generates predictive information. The generated predictive information is adjusted based on the emotional state of customers in the physical store and provided as visual or auditory feedback within the store.

[0179] On the user's side, an emotion engine operates through devices such as smartphones and tablets. This emotion engine analyzes the user's voice tone and facial expressions to infer their emotional state. If the system determines that the user is feeling tense, it controls the in-store displays and speakers to provide relaxing music and videos.

[0180] As a concrete example, if a customer visiting a pet shop is feeling stressed, the system will play soothing music and guide the user to an area where they can interact with small animals. The hardware used includes sensor devices, smart terminals, display devices, and an acoustic system, while the software utilizes OpenCV and TensorFlow as emotion recognition algorithms.

[0181] For this system to be effective, accurate sentiment analysis and the generation and notification of appropriate predictive information are necessary. An example of a prompt is, "Based on customer sentiment data and the behavior of pets in the store, how should we display data that promotes relaxation?" The generated AI model is required to suggest an appropriate store experience based on this prompt.

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server collects animal behavioral information from sensor devices. Inputs include animal biosignals and movement patterns, which are output as a dataset organized over time. This data is then converted to an appropriate format for subsequent analysis.

[0185] Step 2:

[0186] The server analyzes the collected animal behavior data to detect abnormal behavior patterns. The input is the behavior dataset obtained in step 1, and the analysis algorithm identifies the patterns. The detected abnormal behavior patterns become the output sent to the next processing step.

[0187] Step 3:

[0188] The server generates predictive information based on detected abnormal behavior patterns. The input is abnormal behavior patterns, and a predictive algorithm is used to generate information about possible future events. The generated predictive information is used for interactions in physical stores.

[0189] Step 4:

[0190] The device analyzes the customer's voice tone and facial expressions using an emotion engine to estimate the user's emotional state. The input consists of the customer's voice and video data, which is processed by an emotion recognition algorithm (such as OpenCV or TensorFlow), and the estimated emotional state is output.

[0191] Step 5:

[0192] The server integrates the user's emotional state and generated predictive information to tailor in-store notifications. The input consists of the emotional state and predictive information, which determine the content to display or play. The output is user-optimized feedback.

[0193] Step 6:

[0194] The terminal delivers tailored notifications to customers through in-store displays and speakers. Input is feedback information from the server, which is output as concrete visuals or audio. This provides customers with a relaxing experience.

[0195] This processing flow improves the customer experience in physical stores, particularly enabling appropriate responses to stress.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] This invention relates to a system that analyzes abnormal behavioral patterns based on animal behavioral information, generates predictive information, and notifies the user. This system consists of sensors that collect animal behavioral information, a server that processes the data, and a terminal that notifies the user of the information.

[0213] The server collects diverse data, such as animal biosignals, movement patterns, and environmental changes, through sensors. The collected data is transmitted to the server in real time and stored in a database. Next, the server uses machine learning algorithms to analyze this data and detect the presence or absence of patterns that may indicate abnormal behavior.

[0214] When an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. This predictive information includes predictions of the likelihood of natural disasters and market fluctuations. The generated information is visualized by the server and converted into an easily understandable format.

[0215] The terminal receives predictive information transmitted from the server and notifies the user with an alert. This information allows the user to take quick action and make decisions. For example, a user who receives earthquake prediction information is expected to be prompted to evacuate to a safe location. In investment, users can also modify their trading plans based on predictive information regarding anticipated market fluctuations.

[0216] In this way, the present invention effectively utilizes animal behavioral information to achieve accurate and rapid prediction through abnormal behavior. As a result, it can significantly improve the prevention of natural disasters and responsiveness to market fluctuations, providing users with beneficial decision-making support.

[0217] The following describes the processing flow.

[0218] Step 1:

[0219] The server controls sensors to collect animal behavioral information, monitoring data including animal biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0220] Step 2:

[0221] The server applies machine learning algorithms to the collected behavioral information to detect abnormal behavior that deviates from the animal's normal behavioral patterns. This identifies the presence of abnormal behavioral patterns and assigns them anomaly tags.

[0222] Step 3:

[0223] The server analyzes detected abnormal behavior patterns and generates predictive information based on them. This generated predictive information includes predictions of the likelihood of natural disasters and market fluctuations, and this information is converted into an appropriate format for visualization.

[0224] Step 4:

[0225] The server sends the generated prediction information to the terminal. The terminal receives this information and displays it as an alert or notification so that the user can check it immediately.

[0226] Step 5:

[0227] Users receive predictive information from their devices and respond quickly based on it. They can then make decisions and take action, such as preparing for earthquakes or reviewing investment plans.

[0228] (Example 1)

[0229] 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."

[0230] The problem that this invention aims to solve is to efficiently analyze animal behavioral information and quickly and accurately detect abnormal behavior. Furthermore, it aims to generate predictive information based on this information, enabling users to respond quickly to natural disasters and market fluctuations.

[0231] 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.

[0232] In this invention, the server includes means for a device that collects animal behavior information, means for analyzing the behavior information using a machine learning algorithm to detect abnormal behavior patterns, and means for generating predictive information using the detected abnormal behavior patterns and visualizing that information. This makes it possible to accurately detect abnormal behavior and provide rapid predictive information based on animal behavior data.

[0233] "Animal behavioral information" is a general term for data including animal biosignals, movement patterns, and environmental changes, and represents information that indicates the behavioral state of animals.

[0234] A "machine learning algorithm" is a computational method that analyzes diverse data, automatically learns patterns and regularities, and performs predictions and classifications.

[0235] An "abnormal behavioral pattern" refers to a sequence of animal activities that deviate from normal behavior and suggest some kind of risk or abnormal situation.

[0236] "Predictive information" refers to information generated to predict future events and situations based on collected and analyzed data.

[0237] "Visualization" is a technique that displays analyzed data in visual forms such as graphs and charts to make it easier to understand.

[0238] This invention is a system that analyzes abnormal behavior patterns based on animal behavioral information, generates predictive information, and notifies the user.

[0239] The server first collects diverse data, such as animal biosignals, movement patterns, and environmental changes, using specialized sensors. These sensors consist of GPS, accelerometers, heart rate monitors, etc., and can acquire data in real time. The collected data is organized and stored using a database management system. The server then analyzes this data using machine learning algorithms. Specifically, it builds models that automatically detect abnormal behavior using Python and Scikit-learn.

[0240] Next, if an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. For example, if behavior that could be interpreted as an earthquake precursor is detected, this information is used to assess the risk of natural disasters. The generated predictive information is visualized using libraries such as Matplotlib and Plotly, and converted into an easily understandable format.

[0241] The terminal receives visualization information sent from the server and promptly notifies the user. The user can then check this information on the terminal and take appropriate action as needed, such as beginning preparations to evacuate to a safe place.

[0242] As a concrete example, the prompt statement is written as follows:

[0243] "Please help design a system that analyzes animal behavior data and detects abnormal patterns. Propose specific implementation methods and usable machine learning algorithms."

[0244] A system configured in this way can provide rapid and accurate predictive information from animal behavior data, effectively supporting user decision-making.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The server collects data on animal biosignals, movement patterns, and environmental changes via sensors. These sensors include GPS, accelerometers, and heart rate monitors. Input is raw data obtained from the sensors, and output is organized information recorded in a database. The server saves this data to the database in real time.

[0248] Step 2:

[0249] The server performs preprocessing on the collected data, such as noise reduction and missing value imputation. This improves the quality of the data for analysis. The input is the original data recorded in the database, and the output is the cleaned data. Specifically, it uses the Python Pandas library to imputate missing values ​​and remove outliers.

[0250] Step 3:

[0251] The server feeds preprocessed data into a machine learning algorithm to detect abnormal behavior patterns. This process uses Scikit-learn to build an anomaly detection model and identify patterns of normal and abnormal behavior. The input is the cleaned data, and the output is the result of detecting abnormal behavior patterns.

[0252] Step 4:

[0253] When an abnormal behavior pattern is detected, the server generates predictive information based on that pattern. It utilizes TensorFlow and other AI models to assess future risks and create predictive information. The input is information about abnormal behavior patterns, and the output is predictive information. This predictive information includes risks such as natural disasters and potential market fluctuations.

[0254] Step 5:

[0255] The server visualizes the generated prediction information and sends it to the terminal in an easy-to-understand format. Visualization uses tools such as Matplotlib and Plotly. The input is the generated prediction information, and the output is the visualized information. Specific operations include the process of creating graphs and charts.

[0256] Step 6:

[0257] The terminal notifies the user of visualization information received from the server. The user reviews this notification and takes appropriate action as needed. The input is the visualization information displayed on the terminal, and the output is the user's decision. A concrete example is the user's action of starting evacuation preparations.

[0258] (Application Example 1)

[0259] 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 glasses 214 will be referred to as the "terminal."

[0260] While security is increasingly important in modern society, traditional security systems are costly and complex to operate, making them difficult for many individuals and businesses to implement. Furthermore, early detection of unauthorized intrusions and anomalies is challenging, highlighting the need for effective systems that enable rapid response. Therefore, the challenge lies in developing technologies that utilize animal behavior to provide cost-effective security systems and enable rapid response.

[0261] 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.

[0262] In this invention, the server includes means for collecting animal behavior information, means for analyzing abnormal behavior patterns, and means for generating predictive information using the abnormal behavior patterns. This makes it possible to quickly notify the user of predictive information indicating the possibility of unauthorized intrusion based on animal behavior as a security alarm, prompting appropriate action.

[0263] "Animal behavioral information" refers to information that includes biological signals, movement patterns, environmental changes, or signs of abnormal external stimuli in animals.

[0264] An "abnormal behavior pattern" is a behavioral pattern that serves as the basis for identifying unusual animal behavior and generating related predictive information.

[0265] "Predictive information" is information generated based on abnormal animal behavior that indicates the possibility of natural disasters or unauthorized intrusions.

[0266] "Means of notifying the user" refers to methods for communicating generated predictive information to the user via their device and prompting them to take necessary actions.

[0267] "Means of prompt response" refer to methods that help users take appropriate action quickly when they receive predictive information.

[0268] A "security alert" is information that warns users of the possibility of unauthorized intrusion or abnormal events based on abnormal behavior analyzed from animal behavior.

[0269] This invention is a system that provides security alerts based on animal behavior information. The system consists of a sensor device for collecting animal behavior information, a server for analyzing the data, and a terminal for notifying users of the information.

[0270] The sensor device uses a Raspberry Pi to collect animal behavior information in real time. The collected data is sent to a server via the network. The server functions as a web server using Flask and stores the received data in a database. OpenCV is used to analyze images of the animals, and machine learning algorithms using TensorFlow and Scikit-learn are used to identify abnormal behavior patterns.

[0271] When abnormal behavior is detected, the server uses Firebase Cloud Messaging to send the generated predictive information as a push notification to the user's smartphone. The user can check this notification on their device and take appropriate action if a quick response is required.

[0272] As a concrete example, imagine a dog kept as a pet in a rural area that detects the approach of a stranger. This system monitors the dog's unusual behavior, quickly generates an alarm, and sends a notification to the user's smartphone. The user receives the notification, checks the footage from nearby surveillance cameras, and can take measures against the intruder.

[0273] An example of a prompt to input into a generative AI model is, "Propose a system architecture for detecting anomalies from animal behavior data and providing rapid security alerts." By implementing a system based on this prompt, an efficient security system utilizing animal behavior can be realized.

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The server receives animal behavior information from sensor devices. The sensors capture the animal's movements and generate digital data via a Raspberry Pi. This data includes the animal's biosignals, movement patterns, and environmental changes. The input is analog data, and the output is digitized behavioral data.

[0277] Step 2:

[0278] The server stores the received data in a database. Using a database management system, the data is stored in a structured format. The input is digitized behavioral data, and the output is the data stored in the database.

[0279] Step 3:

[0280] The server analyzes the action image data of animals using OpenCV. The image data is preprocessed, and filtering is performed to identify the movements of animals. The input is the raw animal image data, and the output is the filtered animal movement data.

[0281] Step 4:

[0282] The server analyzes the abnormal behavior patterns using a machine learning algorithm with TensorFlow and Scikit-learn. The server applies a model for detecting abnormalities by comparing with the known data so far. The input is the filtered animal movement data, and the output is the detection result of the abnormal behavior pattern.

[0283] Step 5:

[0284] The server sends a notification to the user's smartphone through Firebase Cloud Messaging. A security alert based on the detected abnormal behavior is generated and sent as a push notification in real time. The input is the detection result of the abnormal behavior pattern, and the output is the alert notification to the user.

[0285] Step 6:

[0286] The user receives the notification on the terminal and checks the content. The application on the terminal displays the received alert as a pop-up notification, and the user takes additional actions such as viewing the camera video if necessary. The input is the notification content, and the output is the confirmation and response actions by the user.

[0287] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0288] This invention is a system that simultaneously analyzes animal behavior information and user emotions to provide more accurate predictive information and optimize user responses. This system consists of sensors that acquire animal behavior information, a server that performs data analysis, a terminal that displays the information, and an emotion engine that recognizes user emotions.

[0289] The server collects data, including animal biosignals and movement patterns, and executes algorithms to process it effectively. The collected data is analyzed to detect patterns of abnormal behavior, and predictive information is generated based on the results. This predictive information includes forecasts of earthquake probability and market fluctuations.

[0290] The emotion engine estimates the user's emotions from their voice tone, facial expressions, and physical changes. The device sends this emotion data to a server, which adjusts the alert level of predictive information based on the user's emotional state. For example, if the system detects that the user is stressed, the alert content and display method can be made more intuitive to encourage quick and appropriate action. Furthermore, the emotion engine optimizes specific action suggestions based on the user's emotions, helping the user act with greater confidence.

[0291] For example, if a user who has received earthquake prediction information is detected to be in a state of emotional tension, the system will emphasize detailed evacuation procedures and emergency contact information. Similarly, in market fluctuation predictions for investment activities, the system will encourage calm decision-making by presenting reassuring information to alleviate anxiety.

[0292] In this way, the present invention integrates animal behavior information and user emotional information to provide more accurate and user-centric predictive information and countermeasures. This contributes to mitigating damage from natural disasters, improving the efficiency of investment activities, and enhancing user confidence.

[0293] The following describes the processing flow.

[0294] Step 1:

[0295] The server controls sensors to collect animal behavioral information, monitoring biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0296] Step 2:

[0297] The server uses machine learning algorithms to analyze collected behavioral data and detect deviations from normal patterns. Once an abnormal behavioral pattern is identified, it is tagged with an anomaly tag.

[0298] Step 3:

[0299] The server generates predictive information based on detected abnormal behavior patterns. This predicted information concerns the likelihood of earthquakes and market fluctuations, and is prepared in an easy-to-understand format using visualization tools.

[0300] Step 4:

[0301] A device containing an emotion engine collects the user's voice tone and facial expression data to analyze their emotional state. This identifies the user's current emotional state, and the data is sent to a server.

[0302] Step 5:

[0303] The server receives user emotional state data and adjusts the alert priority of predictive information. If user stress or anxiety is detected, the alert is emphasized or adjusted accordingly.

[0304] Step 6:

[0305] The device receives pre-configured forecast information from the server and displays customized alerts and notifications to the user. These notifications are designed to allow the user to respond quickly.

[0306] Step 7:

[0307] Based on the provided prediction information and alerts, users take specific actions such as earthquake evacuation and review of investment strategies. It also includes countermeasure proposals based on the emotional situation, which helps support acting with peace of mind.

[0308] (Example 2)

[0309] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0310] In modern society, predicting natural disasters and market fluctuations brings many benefits, but there is a problem that just data analysis alone cannot provide responses suitable for each individual's situation. Also, in conventional systems, the user's emotions are not taken into account, and one-sided notifications are made, which may cause the user to feel stress or amplify anxiety. There is a need to solve such problems and realize the provision of more individualized and adaptable prediction information.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0312] In this invention, the server includes a device for acquiring animal behavior data, an algorithm for analyzing the data to identify abnormal behavior patterns, and a function for creating prediction information based on the identified patterns. Thereby, it becomes possible to integrally utilize the behavior of animals and the emotional state of the user, and provide more accurate and individualized prediction information.

[0313] "Animal behavior data" is a set of information including animal biological signals, movement patterns, ambient changes, etc., and is acquired by specific technical means.

[0314] "Device" is a device having a specific function and composed of hardware or software for acquiring or analyzing data.

[0315] An "algorithm" is a sequence of computational procedures or rules used for data analysis, applied to identify abnormal behavioral patterns.

[0316] "Predictive information" refers to information generated based on animal behavior data and other relevant information that suggests future natural disasters, market fluctuations, and other events.

[0317] "User emotional state" refers to the emotional state inferred from the user's tone of voice, facial expressions, physical changes, etc.

[0318] An "engine" is a program or system that analyzes data and performs specific functions; in this context, it is used to recognize the user's emotional state.

[0319] A "terminal" is an electronic device used to display or notify a user of information, and includes personal computers and smartphones.

[0320] A "natural disaster" is an event that causes damage due to natural phenomena such as earthquakes and typhoons.

[0321] "Adaptation" refers to changing or adjusting a system or function in accordance with specific situations or conditions, and specifically means adjusting predictive information according to the user's emotional state.

[0322] This invention specifically provides a technology for generating predictive information using animal behavior data and adaptively notifying users of this information.

[0323] Data acquisition and hardware configuration

[0324] The server receives behavioral data from multiple sensors attached to the animals. This data includes biological signals and movement patterns, and the sensors transmit the information to the server via Bluetooth or Wi-Fi.

[0325] Data analysis and software configuration

[0326] The server analyzes collected behavioral data in real time using a dedicated algorithm. This makes it possible to identify abnormal behavior patterns using machine learning techniques. The analyzed results are automatically generated as predictive information such as earthquakes and market fluctuations.

[0327] Recognition and adaptation of user emotions

[0328] The emotion engine collects the user's voice tone, facial expressions, and physical changes through the camera and microphone, and analyzes this data to determine the user's emotional state. The device sends this emotional data to a server, allowing predictive notifications to be adjusted according to the user's emotions. If the user shows high levels of stress, the notification content is changed to be more intuitive and easier to understand to alleviate anxiety.

[0329] Examples of specific cases and prompt statements

[0330] For example, if the device detects that a user is in a state of tension after receiving earthquake prediction information, it will highlight detailed evacuation procedures and provide voice instructions. Furthermore, when providing information on market trends, it will present reassuring elements to encourage calm decision-making by the user.

[0331] Examples of prompt messages include, "Please tell me how to predict the likelihood of an earthquake based on animal behavior data and adjust the alert content according to the user's emotional state."

[0332] This system aims to help users maximize their profits by predicting natural disasters and market fluctuations, and to provide adaptive information tailored to each individual user.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The server receives behavioral data, such as biological signals and movement patterns, from sensors attached to animals. This data is typically transmitted via Bluetooth or Wi-Fi. Inputs include biological signal data and movement patterns, and the output is filtered, clean data. Specifically, the server performs initial filtering of the received data to remove noise.

[0336] Step 2:

[0337] The server runs a dedicated machine learning algorithm using filtered animal behavior data. The input includes filtered, clean data, and the output is the identification of abnormal behavior patterns. Specifically, the server retrains itself by comparing it with historical data to detect anomalies.

[0338] Step 3:

[0339] The server generates predictive information based on identified anomaly patterns. The input is anomaly behavior patterns, and the output is predictive information such as earthquakes and market fluctuations. Specifically, the server utilizes a generation AI model, supplementing its predictions by referencing historical data and external information.

[0340] Step 4:

[0341] The emotion engine acquires the user's voice tone, facial expressions, and physical changes from the camera and microphone, and analyzes their emotional state. Input includes the user's voice and video data, and output is the user's emotional state. Specifically, the device sends the processed emotional data to the server in real time.

[0342] Step 5:

[0343] The server adjusts the content of predictive notifications based on the user's emotional state. The input includes the user's emotional state, and the output is a notification adapted to that emotion. Specifically, the server modifies the notification content based on the emotional state, for example, making warnings more approachable.

[0344] Step 6:

[0345] The device provides the user with adjusted predictive information. The input is the adjusted notification information, and the output is the completion of notification to the user. Specifically, the device displays alerts to the user using voice and visuals, and utilizes vibration and voice guidance in emergencies.

[0346] (Application Example 2)

[0347] 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."

[0348] In physical stores, there is a challenge in responding flexibly to customers' emotional states. In particular, there is a challenge in how to help customers relax and feel satisfied when they are stressed. Traditional systems make it difficult to individually optimize the customer experience, and there is a need for effective means to improve customer satisfaction.

[0349] 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.

[0350] In this invention, the server includes means for collecting animal behavior information, means for analyzing the user's emotional state, and means for adjusting predictive information notifications based on the user's emotional state. This makes it possible to provide a relaxing experience tailored to the customer's emotional state in a physical store.

[0351] "Animal behavioral information" refers to data obtained from animals, such as biosignals, movement patterns, and information about environmental changes.

[0352] An "abnormal behavioral pattern" is a pattern of unusual behavior detected by analyzing animal behavioral information.

[0353] "Predictive information" refers to information about future events generated based on abnormal behavioral patterns.

[0354] A "user" is someone who uses the system or someone who receives notifications.

[0355] "Emotional state" refers to the psychological condition judged based on the user's voice, facial expressions, physical changes, etc.

[0356] "Adjustment" refers to changing the way predictive information is notified and the content of that information based on the user's emotional state.

[0357] A "system" is a device or mechanism that integrates multiple means to achieve a specific function.

[0358] The system that implements this application is designed to optimize the customer experience in physical stores by collecting animal behavioral information and analyzing the emotional state of users.

[0359] The server uses sensor devices to collect animal behavioral information. These sensors acquire the animals' biosignals and movement patterns, and the server performs data analysis. This analysis detects abnormal behavioral patterns and generates predictive information. The generated predictive information is adjusted based on the emotional state of customers in the physical store and provided as visual or auditory feedback within the store.

[0360] On the user's side, an emotion engine operates through devices such as smartphones and tablets. This emotion engine analyzes the user's voice tone and facial expressions to infer their emotional state. If the system determines that the user is feeling tense, it controls the in-store displays and speakers to provide relaxing music and videos.

[0361] As a concrete example, if a customer visiting a pet shop is feeling stressed, the system will play soothing music and guide the user to an area where they can interact with small animals. The hardware used includes sensor devices, smart terminals, display devices, and an acoustic system, while the software utilizes OpenCV and TensorFlow as emotion recognition algorithms.

[0362] For this system to be effective, accurate sentiment analysis and the generation and notification of appropriate predictive information are necessary. An example of a prompt is, "Based on customer sentiment data and the behavior of pets in the store, how should we display data that promotes relaxation?" The generated AI model is required to suggest an appropriate store experience based on this prompt.

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The server collects animal behavioral information from sensor devices. Inputs include animal biosignals and movement patterns, which are output as a dataset organized over time. This data is then converted to an appropriate format for subsequent analysis.

[0366] Step 2:

[0367] The server analyzes the collected animal behavior data to detect abnormal behavior patterns. The input is the behavior dataset obtained in step 1, and the analysis algorithm identifies the patterns. The detected abnormal behavior patterns become the output sent to the next processing step.

[0368] Step 3:

[0369] The server generates predictive information based on detected abnormal behavior patterns. The input is abnormal behavior patterns, and a predictive algorithm is used to generate information about possible future events. The generated predictive information is used for interactions in physical stores.

[0370] Step 4:

[0371] The device analyzes the customer's voice tone and facial expressions using an emotion engine to estimate the user's emotional state. The input consists of the customer's voice and video data, which is processed by an emotion recognition algorithm (such as OpenCV or TensorFlow), and the estimated emotional state is output.

[0372] Step 5:

[0373] The server integrates the user's emotional state and generated predictive information to tailor in-store notifications. The input consists of the emotional state and predictive information, which determine the content to display or play. The output is user-optimized feedback.

[0374] Step 6:

[0375] The terminal delivers tailored notifications to customers through in-store displays and speakers. Input is feedback information from the server, which is output as concrete visuals or audio. This provides customers with a relaxing experience.

[0376] This processing flow improves the customer experience in physical stores, particularly enabling appropriate responses to stress.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] 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.

[0383] 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).

[0384] 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.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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".

[0393] This invention relates to a system that analyzes abnormal behavioral patterns based on animal behavioral information, generates predictive information, and notifies the user. This system consists of sensors that collect animal behavioral information, a server that processes the data, and a terminal that notifies the user of the information.

[0394] The server collects diverse data, such as animal biosignals, movement patterns, and environmental changes, through sensors. The collected data is transmitted to the server in real time and stored in a database. Next, the server uses machine learning algorithms to analyze this data and detect the presence or absence of patterns that may indicate abnormal behavior.

[0395] When an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. This predictive information includes predictions of the likelihood of natural disasters and market fluctuations. The generated information is visualized by the server and converted into an easily understandable format.

[0396] The terminal receives predictive information transmitted from the server and notifies the user with an alert. This information allows the user to take quick action and make decisions. For example, a user who receives earthquake prediction information is expected to be prompted to evacuate to a safe location. In investment, users can also modify their trading plans based on predictive information regarding anticipated market fluctuations.

[0397] In this way, the present invention effectively utilizes animal behavioral information to achieve accurate and rapid prediction through abnormal behavior. As a result, it can significantly improve the prevention of natural disasters and responsiveness to market fluctuations, providing users with beneficial decision-making support.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The server controls sensors to collect animal behavioral information, monitoring data including animal biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0401] Step 2:

[0402] The server applies machine learning algorithms to the collected behavioral information to detect abnormal behavior that deviates from the animal's normal behavioral patterns. This identifies the presence of abnormal behavioral patterns and assigns them anomaly tags.

[0403] Step 3:

[0404] The server analyzes detected abnormal behavior patterns and generates predictive information based on them. This generated predictive information includes predictions of the likelihood of natural disasters and market fluctuations, and this information is converted into an appropriate format for visualization.

[0405] Step 4:

[0406] The server sends the generated prediction information to the terminal. The terminal receives this information and displays it as an alert or notification so that the user can check it immediately.

[0407] Step 5:

[0408] Users receive predictive information from their devices and respond quickly based on it. They can then make decisions and take action, such as preparing for earthquakes or reviewing investment plans.

[0409] (Example 1)

[0410] 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."

[0411] The problem that this invention aims to solve is to efficiently analyze animal behavioral information and quickly and accurately detect abnormal behavior. Furthermore, it aims to generate predictive information based on this information, enabling users to respond quickly to natural disasters and market fluctuations.

[0412] 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.

[0413] In this invention, the server includes means for a device that collects animal behavior information, means for analyzing the behavior information using a machine learning algorithm to detect abnormal behavior patterns, and means for generating predictive information using the detected abnormal behavior patterns and visualizing that information. This makes it possible to accurately detect abnormal behavior and provide rapid predictive information based on animal behavior data.

[0414] "Animal behavioral information" is a general term for data including animal biosignals, movement patterns, and environmental changes, and represents information that indicates the behavioral state of animals.

[0415] A "machine learning algorithm" is a computational method that analyzes diverse data, automatically learns patterns and regularities, and performs predictions and classifications.

[0416] An "abnormal behavioral pattern" refers to a sequence of animal activities that deviate from normal behavior and suggest some kind of risk or abnormal situation.

[0417] "Predictive information" refers to information generated to predict future events and situations based on collected and analyzed data.

[0418] "Visualization" is a technique that displays analyzed data in visual forms such as graphs and charts to make it easier to understand.

[0419] This invention is a system that analyzes abnormal behavior patterns based on animal behavioral information, generates predictive information, and notifies the user.

[0420] The server first collects diverse data, such as animal biosignals, movement patterns, and environmental changes, using specialized sensors. These sensors consist of GPS, accelerometers, heart rate monitors, etc., and can acquire data in real time. The collected data is organized and stored using a database management system. The server then analyzes this data using machine learning algorithms. Specifically, it builds models that automatically detect abnormal behavior using Python and Scikit-learn.

[0421] Next, if an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. For example, if behavior that could be interpreted as an earthquake precursor is detected, this information is used to assess the risk of natural disasters. The generated predictive information is visualized using libraries such as Matplotlib and Plotly, and converted into an easily understandable format.

[0422] The terminal receives visualization information sent from the server and promptly notifies the user. The user can then check this information on the terminal and take appropriate action as needed, such as beginning preparations to evacuate to a safe place.

[0423] As a concrete example, the prompt statement is written as follows:

[0424] "Please help design a system that analyzes animal behavior data and detects abnormal patterns. Propose specific implementation methods and usable machine learning algorithms."

[0425] A system configured in this way can provide rapid and accurate predictive information from animal behavior data, effectively supporting user decision-making.

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The server collects data on animal biosignals, movement patterns, and environmental changes via sensors. These sensors include GPS, accelerometers, and heart rate monitors. Input is raw data obtained from the sensors, and output is organized information recorded in a database. The server saves this data to the database in real time.

[0429] Step 2:

[0430] The server performs preprocessing on the collected data, such as noise reduction and missing value imputation. This improves the quality of the data for analysis. The input is the original data recorded in the database, and the output is the cleaned data. Specifically, it uses the Python Pandas library to imputate missing values ​​and remove outliers.

[0431] Step 3:

[0432] The server feeds preprocessed data into a machine learning algorithm to detect abnormal behavior patterns. This process uses Scikit-learn to build an anomaly detection model and identify patterns of normal and abnormal behavior. The input is the cleaned data, and the output is the result of detecting abnormal behavior patterns.

[0433] Step 4:

[0434] When an abnormal behavior pattern is detected, the server generates predictive information based on that pattern. It utilizes TensorFlow and other AI models to assess future risks and create predictive information. The input is information about abnormal behavior patterns, and the output is predictive information. This predictive information includes risks such as natural disasters and potential market fluctuations.

[0435] Step 5:

[0436] The server visualizes the generated prediction information and sends it to the terminal in an easy-to-understand format. Visualization uses tools such as Matplotlib and Plotly. The input is the generated prediction information, and the output is the visualized information. Specific operations include the process of creating graphs and charts.

[0437] Step 6:

[0438] The terminal notifies the user of visualization information received from the server. The user reviews this notification and takes appropriate action as needed. The input is the visualization information displayed on the terminal, and the output is the user's decision. A concrete example is the user's action of starting evacuation preparations.

[0439] (Application Example 1)

[0440] 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."

[0441] While security is increasingly important in modern society, traditional security systems are costly and complex to operate, making them difficult for many individuals and businesses to implement. Furthermore, early detection of unauthorized intrusions and anomalies is challenging, highlighting the need for effective systems that enable rapid response. Therefore, the challenge lies in developing technologies that utilize animal behavior to provide cost-effective security systems and enable rapid response.

[0442] 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.

[0443] In this invention, the server includes means for collecting animal behavior information, means for analyzing abnormal behavior patterns, and means for generating predictive information using the abnormal behavior patterns. This makes it possible to quickly notify the user of predictive information indicating the possibility of unauthorized intrusion based on animal behavior as a security alarm, prompting appropriate action.

[0444] "Animal behavioral information" refers to information that includes biological signals, movement patterns, environmental changes, or signs of abnormal external stimuli in animals.

[0445] An "abnormal behavior pattern" is a behavioral pattern that serves as the basis for identifying unusual animal behavior and generating related predictive information.

[0446] "Predictive information" is information generated based on abnormal animal behavior that indicates the possibility of natural disasters or unauthorized intrusions.

[0447] "Means of notifying the user" refers to methods for communicating generated predictive information to the user via their device and prompting them to take necessary actions.

[0448] "Means of prompt response" refer to methods that help users take appropriate action quickly when they receive predictive information.

[0449] A "security alert" is information that warns users of the possibility of unauthorized intrusion or abnormal events based on abnormal behavior analyzed from animal behavior.

[0450] This invention is a system that provides security alerts based on animal behavior information. The system consists of a sensor device for collecting animal behavior information, a server for analyzing the data, and a terminal for notifying users of the information.

[0451] The sensor device uses a Raspberry Pi to collect animal behavior information in real time. The collected data is sent to a server via the network. The server functions as a web server using Flask and stores the received data in a database. OpenCV is used to analyze images of the animals, and machine learning algorithms using TensorFlow and Scikit-learn are used to identify abnormal behavior patterns.

[0452] When abnormal behavior is detected, the server uses Firebase Cloud Messaging to send the generated predictive information as a push notification to the user's smartphone. The user can check this notification on their device and take appropriate action if a quick response is required.

[0453] As a concrete example, imagine a dog kept as a pet in a rural area that detects the approach of a stranger. This system monitors the dog's unusual behavior, quickly generates an alarm, and sends a notification to the user's smartphone. The user receives the notification, checks the footage from nearby surveillance cameras, and can take measures against the intruder.

[0454] An example of a prompt to input into a generative AI model is, "Propose a system architecture for detecting anomalies from animal behavior data and providing rapid security alerts." By implementing a system based on this prompt, an efficient security system utilizing animal behavior can be realized.

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The server receives animal behavior information from sensor devices. The sensors capture the animal's movements and generate digital data via a Raspberry Pi. This data includes the animal's biosignals, movement patterns, and environmental changes. The input is analog data, and the output is digitized behavioral data.

[0458] Step 2:

[0459] The server stores the received data in a database. Using a database management system, the data is stored in a structured format. The input is digitized behavioral data, and the output is the data stored in the database.

[0460] Step 3:

[0461] The server uses OpenCV to analyze animal behavioral image data. The image data is preprocessed and filtered to identify animal movements. The input is unprocessed animal image data, and the output is filtered animal movement data.

[0462] Step 4:

[0463] The server analyzes abnormal behavior patterns using machine learning algorithms based on TensorFlow and Scikit-learn. The server applies a model to detect anomalies by comparing them with previously known data. The input is filtered animal movement data, and the output is the result of detecting abnormal behavior patterns.

[0464] Step 5:

[0465] The server sends notifications to the user's smartphone via Firebase Cloud Messaging. A security alarm is generated based on the detected abnormal behavior and sent as a push notification in real time. The input is the result of detecting abnormal behavior patterns, and the output is the alarm notification to the user.

[0466] Step 6:

[0467] The user receives a notification on their device and checks its contents. The application on the device displays the received alert as a pop-up notification, and the user takes additional action as needed, such as viewing camera footage. The input is the notification content, and the output is the user's confirmation and response action.

[0468] 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.

[0469] This invention is a system that simultaneously analyzes animal behavior information and user emotions to provide more accurate predictive information and optimize user responses. This system consists of sensors that acquire animal behavior information, a server that performs data analysis, a terminal that displays the information, and an emotion engine that recognizes user emotions.

[0470] The server collects data, including animal biosignals and movement patterns, and executes algorithms to process it effectively. The collected data is analyzed to detect patterns of abnormal behavior, and predictive information is generated based on the results. This predictive information includes forecasts of earthquake probability and market fluctuations.

[0471] The emotion engine estimates the user's emotions from their voice tone, facial expressions, and physical changes. The device sends this emotion data to a server, which adjusts the alert level of predictive information based on the user's emotional state. For example, if the system detects that the user is stressed, the alert content and display method can be made more intuitive to encourage quick and appropriate action. Furthermore, the emotion engine optimizes specific action suggestions based on the user's emotions, helping the user act with greater confidence.

[0472] For example, if a user who has received earthquake prediction information is detected to be in a state of emotional tension, the system will emphasize detailed evacuation procedures and emergency contact information. Similarly, in market fluctuation predictions for investment activities, the system will encourage calm decision-making by presenting reassuring information to alleviate anxiety.

[0473] In this way, the present invention integrates animal behavior information and user emotional information to provide more accurate and user-centric predictive information and countermeasures. This contributes to mitigating damage from natural disasters, improving the efficiency of investment activities, and enhancing user confidence.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The server controls sensors to collect animal behavioral information, monitoring biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0477] Step 2:

[0478] The server uses machine learning algorithms to analyze collected behavioral data and detect deviations from normal patterns. Once an abnormal behavioral pattern is identified, it is tagged with an anomaly tag.

[0479] Step 3:

[0480] The server generates predictive information based on detected abnormal behavior patterns. This predicted information concerns the likelihood of earthquakes and market fluctuations, and is prepared in an easy-to-understand format using visualization tools.

[0481] Step 4:

[0482] A device containing an emotion engine collects the user's voice tone and facial expression data to analyze their emotional state. This identifies the user's current emotional state, and the data is sent to a server.

[0483] Step 5:

[0484] The server receives user emotional state data and adjusts the alert priority of predictive information. If user stress or anxiety is detected, the alert is emphasized or adjusted accordingly.

[0485] Step 6:

[0486] The device receives pre-configured forecast information from the server and displays customized alerts and notifications to the user. These notifications are designed to allow the user to respond quickly.

[0487] Step 7:

[0488] Based on the provided forecast information and alerts, users can take specific actions such as earthquake evacuation or reviewing their investment policies. The system also includes emotionally-based suggestions to help users act with confidence.

[0489] (Example 2)

[0490] 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."

[0491] In modern society, predicting natural disasters and market fluctuations brings many benefits, but mere data analysis has the challenge of not being able to provide responses tailored to each individual's situation. Furthermore, conventional systems do not take into account user emotions and provide one-sided notifications, which can cause users to feel stressed or have their anxiety amplified. There is a need to solve these problems and realize the provision of more personalized and adaptive predictive information.

[0492] 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.

[0493] In this invention, the server includes a device for acquiring animal behavior data, an algorithm for analyzing the data and identifying abnormal behavior patterns, and a function for creating predictive information based on the identified patterns. This enables the integrated use of animal behavior and the user's emotional state to provide more accurate and personalized predictive information.

[0494] "Animal behavioral data" refers to a collection of information including animal biological signals, movement patterns, and changes in the surrounding environment, which is acquired through specific technological means.

[0495] A "device" is a piece of equipment consisting of hardware or software that has a specific function and is used to acquire or analyze data.

[0496] An "algorithm" is a sequence of computational procedures or rules used for data analysis, applied to identify abnormal behavioral patterns.

[0497] "Predictive information" refers to information generated based on animal behavior data and other relevant information that suggests future natural disasters, market fluctuations, and other events.

[0498] "User emotional state" refers to the emotional state inferred from the user's tone of voice, facial expressions, physical changes, etc.

[0499] An "engine" is a program or system that analyzes data and performs specific functions; in this context, it is used to recognize the user's emotional state.

[0500] A "terminal" is an electronic device used to display or notify a user of information, and includes personal computers and smartphones.

[0501] A "natural disaster" is an event that causes damage due to natural phenomena such as earthquakes and typhoons.

[0502] "Adaptation" refers to changing or adjusting a system or function in accordance with specific situations or conditions, and specifically means adjusting predictive information according to the user's emotional state.

[0503] This invention specifically provides a technology for generating predictive information using animal behavior data and adaptively notifying users of this information.

[0504] Data acquisition and hardware configuration

[0505] The server receives behavioral data from multiple sensors attached to the animals. This data includes biological signals and movement patterns, and the sensors transmit the information to the server via Bluetooth or Wi-Fi.

[0506] Data analysis and software configuration

[0507] The server analyzes collected behavioral data in real time using a dedicated algorithm. This makes it possible to identify abnormal behavior patterns using machine learning techniques. The analyzed results are automatically generated as predictive information such as earthquakes and market fluctuations.

[0508] Recognition and adaptation of user emotions

[0509] The emotion engine collects the user's voice tone, facial expressions, and physical changes through the camera and microphone, and analyzes this data to determine the user's emotional state. The device sends this emotional data to a server, allowing predictive notifications to be adjusted according to the user's emotions. If the user shows high levels of stress, the notification content is changed to be more intuitive and easier to understand to alleviate anxiety.

[0510] Examples of specific cases and prompt statements

[0511] For example, if the device detects that a user is in a state of tension after receiving earthquake prediction information, it will highlight detailed evacuation procedures and provide voice instructions. Furthermore, when providing information on market trends, it will present reassuring elements to encourage calm decision-making by the user.

[0512] Examples of prompt messages include, "Please tell me how to predict the likelihood of an earthquake based on animal behavior data and adjust the alert content according to the user's emotional state."

[0513] This system aims to help users maximize their profits by predicting natural disasters and market fluctuations, and to provide adaptive information tailored to each individual user.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The server receives behavioral data, such as biological signals and movement patterns, from sensors attached to animals. This data is typically transmitted via Bluetooth or Wi-Fi. Inputs include biological signal data and movement patterns, and the output is filtered, clean data. Specifically, the server performs initial filtering of the received data to remove noise.

[0517] Step 2:

[0518] The server runs a dedicated machine learning algorithm using filtered animal behavior data. The input includes filtered, clean data, and the output is the identification of abnormal behavior patterns. Specifically, the server retrains itself by comparing it with historical data to detect anomalies.

[0519] Step 3:

[0520] The server generates predictive information based on identified anomaly patterns. The input is anomaly behavior patterns, and the output is predictive information such as earthquakes and market fluctuations. Specifically, the server utilizes a generation AI model, supplementing its predictions by referencing historical data and external information.

[0521] Step 4:

[0522] The emotion engine acquires the user's voice tone, facial expressions, and physical changes from the camera and microphone, and analyzes their emotional state. Input includes the user's voice and video data, and output is the user's emotional state. Specifically, the device sends the processed emotional data to the server in real time.

[0523] Step 5:

[0524] The server adjusts the content of predictive notifications based on the user's emotional state. The input includes the user's emotional state, and the output is a notification adapted to that emotion. Specifically, the server modifies the notification content based on the emotional state, for example, making warnings more approachable.

[0525] Step 6:

[0526] The device provides the user with adjusted predictive information. The input is the adjusted notification information, and the output is the completion of notification to the user. Specifically, the device displays alerts to the user using voice and visuals, and utilizes vibration and voice guidance in emergencies.

[0527] (Application Example 2)

[0528] 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."

[0529] In physical stores, there is a challenge in responding flexibly to customers' emotional states. In particular, there is a challenge in how to help customers relax and feel satisfied when they are stressed. Traditional systems make it difficult to individually optimize the customer experience, and there is a need for effective means to improve customer satisfaction.

[0530] 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.

[0531] In this invention, the server includes means for collecting animal behavior information, means for analyzing the user's emotional state, and means for adjusting predictive information notifications based on the user's emotional state. This makes it possible to provide a relaxing experience tailored to the customer's emotional state in a physical store.

[0532] "Animal behavioral information" refers to data obtained from animals, such as biosignals, movement patterns, and information about environmental changes.

[0533] An "abnormal behavioral pattern" is a pattern of unusual behavior detected by analyzing animal behavioral information.

[0534] "Predictive information" refers to information about future events generated based on abnormal behavioral patterns.

[0535] A "user" is someone who uses the system or someone who receives notifications.

[0536] "Emotional state" refers to the psychological condition judged based on the user's voice, facial expressions, physical changes, etc.

[0537] "Adjustment" refers to changing the way predictive information is notified and the content of that information based on the user's emotional state.

[0538] A "system" is a device or mechanism that integrates multiple means to achieve a specific function.

[0539] The system that implements this application is designed to optimize the customer experience in physical stores by collecting animal behavioral information and analyzing the emotional state of users.

[0540] The server uses sensor devices to collect animal behavioral information. These sensors acquire the animals' biosignals and movement patterns, and the server performs data analysis. This analysis detects abnormal behavioral patterns and generates predictive information. The generated predictive information is adjusted based on the emotional state of customers in the physical store and provided as visual or auditory feedback within the store.

[0541] On the user's side, an emotion engine operates through devices such as smartphones and tablets. This emotion engine analyzes the user's voice tone and facial expressions to infer their emotional state. If the system determines that the user is feeling tense, it controls the in-store displays and speakers to provide relaxing music and videos.

[0542] As a concrete example, if a customer visiting a pet shop is feeling stressed, the system will play soothing music and guide the user to an area where they can interact with small animals. The hardware used includes sensor devices, smart terminals, display devices, and an acoustic system, while the software utilizes OpenCV and TensorFlow as emotion recognition algorithms.

[0543] For this system to be effective, accurate sentiment analysis and the generation and notification of appropriate predictive information are necessary. An example of a prompt is, "Based on customer sentiment data and the behavior of pets in the store, how should we display data that promotes relaxation?" The generated AI model is required to suggest an appropriate store experience based on this prompt.

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The server collects animal behavioral information from sensor devices. Inputs include animal biosignals and movement patterns, which are output as a dataset organized over time. This data is then converted to an appropriate format for subsequent analysis.

[0547] Step 2:

[0548] The server analyzes the collected animal behavior data to detect abnormal behavior patterns. The input is the behavior dataset obtained in step 1, and the analysis algorithm identifies the patterns. The detected abnormal behavior patterns become the output sent to the next processing step.

[0549] Step 3:

[0550] The server generates predictive information based on detected abnormal behavior patterns. The input is abnormal behavior patterns, and a predictive algorithm is used to generate information about possible future events. The generated predictive information is used for interactions in physical stores.

[0551] Step 4:

[0552] The device analyzes the customer's voice tone and facial expressions using an emotion engine to estimate the user's emotional state. The input consists of the customer's voice and video data, which is processed by an emotion recognition algorithm (such as OpenCV or TensorFlow), and the estimated emotional state is output.

[0553] Step 5:

[0554] The server integrates the user's emotional state and generated predictive information to tailor in-store notifications. The input consists of the emotional state and predictive information, which determine the content to display or play. The output is user-optimized feedback.

[0555] Step 6:

[0556] The terminal delivers tailored notifications to customers through in-store displays and speakers. Input is feedback information from the server, which is output as concrete visuals or audio. This provides customers with a relaxing experience.

[0557] This processing flow improves the customer experience in physical stores, particularly enabling appropriate responses to stress.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] [Fourth Embodiment]

[0562] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0563] 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.

[0564] 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).

[0565] 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.

[0566] 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.

[0567] 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).

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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".

[0575] This invention relates to a system that analyzes abnormal behavioral patterns based on animal behavioral information, generates predictive information, and notifies the user. This system consists of sensors that collect animal behavioral information, a server that processes the data, and a terminal that notifies the user of the information.

[0576] The server collects diverse data, such as animal biosignals, movement patterns, and environmental changes, through sensors. The collected data is transmitted to the server in real time and stored in a database. Next, the server uses machine learning algorithms to analyze this data and detect the presence or absence of patterns that may indicate abnormal behavior.

[0577] When an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. This predictive information includes predictions of the likelihood of natural disasters and market fluctuations. The generated information is visualized by the server and converted into an easily understandable format.

[0578] The terminal receives predictive information transmitted from the server and notifies the user with an alert. This information allows the user to take quick action and make decisions. For example, a user who receives earthquake prediction information is expected to be prompted to evacuate to a safe location. In investment, users can also modify their trading plans based on predictive information regarding anticipated market fluctuations.

[0579] In this way, the present invention effectively utilizes animal behavioral information to achieve accurate and rapid prediction through abnormal behavior. As a result, it can significantly improve the prevention of natural disasters and responsiveness to market fluctuations, providing users with beneficial decision-making support.

[0580] The following describes the processing flow.

[0581] Step 1:

[0582] The server controls sensors to collect animal behavioral information, monitoring data including animal biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0583] Step 2:

[0584] The server applies machine learning algorithms to the collected behavioral information to detect abnormal behavior that deviates from the animal's normal behavioral patterns. This identifies the presence of abnormal behavioral patterns and assigns them anomaly tags.

[0585] Step 3:

[0586] The server analyzes detected abnormal behavior patterns and generates predictive information based on them. This generated predictive information includes predictions of the likelihood of natural disasters and market fluctuations, and this information is converted into an appropriate format for visualization.

[0587] Step 4:

[0588] The server sends the generated prediction information to the terminal. The terminal receives this information and displays it as an alert or notification so that the user can check it immediately.

[0589] Step 5:

[0590] Users receive predictive information from their devices and respond quickly based on it. They can then make decisions and take action, such as preparing for earthquakes or reviewing investment plans.

[0591] (Example 1)

[0592] 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".

[0593] The problem that this invention aims to solve is to efficiently analyze animal behavioral information and quickly and accurately detect abnormal behavior. Furthermore, it aims to generate predictive information based on this information, enabling users to respond quickly to natural disasters and market fluctuations.

[0594] 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.

[0595] In this invention, the server includes means for a device that collects animal behavior information, means for analyzing the behavior information using a machine learning algorithm to detect abnormal behavior patterns, and means for generating predictive information using the detected abnormal behavior patterns and visualizing that information. This makes it possible to accurately detect abnormal behavior and provide rapid predictive information based on animal behavior data.

[0596] "Animal behavioral information" is a general term for data including animal biosignals, movement patterns, and environmental changes, and represents information that indicates the behavioral state of animals.

[0597] A "machine learning algorithm" is a computational method that analyzes diverse data, automatically learns patterns and regularities, and performs predictions and classifications.

[0598] An "abnormal behavioral pattern" refers to a sequence of animal activities that deviate from normal behavior and suggest some kind of risk or abnormal situation.

[0599] "Predictive information" refers to information generated to predict future events and situations based on collected and analyzed data.

[0600] "Visualization" is a technique that displays analyzed data in visual forms such as graphs and charts to make it easier to understand.

[0601] This invention is a system that analyzes abnormal behavior patterns based on animal behavioral information, generates predictive information, and notifies the user.

[0602] The server first collects diverse data, such as animal biosignals, movement patterns, and environmental changes, using specialized sensors. These sensors consist of GPS, accelerometers, heart rate monitors, etc., and can acquire data in real time. The collected data is organized and stored using a database management system. The server then analyzes this data using machine learning algorithms. Specifically, it builds models that automatically detect abnormal behavior using Python and Scikit-learn.

[0603] Next, if an abnormal behavioral pattern is detected, the server generates predictive information based on that pattern. For example, if behavior that could be interpreted as an earthquake precursor is detected, this information is used to assess the risk of natural disasters. The generated predictive information is visualized using libraries such as Matplotlib and Plotly, and converted into an easily understandable format.

[0604] The terminal receives visualization information sent from the server and promptly notifies the user. The user can then check this information on the terminal and take appropriate action as needed, such as beginning preparations to evacuate to a safe place.

[0605] As a concrete example, the prompt statement is written as follows:

[0606] "Please help design a system that analyzes animal behavior data and detects abnormal patterns. Propose specific implementation methods and usable machine learning algorithms."

[0607] A system configured in this way can provide rapid and accurate predictive information from animal behavior data, effectively supporting user decision-making.

[0608] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0609] Step 1:

[0610] The server collects data on animal biosignals, movement patterns, and environmental changes via sensors. These sensors include GPS, accelerometers, and heart rate monitors. Input is raw data obtained from the sensors, and output is organized information recorded in a database. The server saves this data to the database in real time.

[0611] Step 2:

[0612] The server performs preprocessing on the collected data, such as noise reduction and missing value imputation. This improves the quality of the data for analysis. The input is the original data recorded in the database, and the output is the cleaned data. Specifically, it uses the Python Pandas library to imputate missing values ​​and remove outliers.

[0613] Step 3:

[0614] The server feeds preprocessed data into a machine learning algorithm to detect abnormal behavior patterns. This process uses Scikit-learn to build an anomaly detection model and identify patterns of normal and abnormal behavior. The input is the cleaned data, and the output is the result of detecting abnormal behavior patterns.

[0615] Step 4:

[0616] When an abnormal behavior pattern is detected, the server generates predictive information based on that pattern. It utilizes TensorFlow and other AI models to assess future risks and create predictive information. The input is information about abnormal behavior patterns, and the output is predictive information. This predictive information includes risks such as natural disasters and potential market fluctuations.

[0617] Step 5:

[0618] The server visualizes the generated prediction information and sends it to the terminal in an easy-to-understand format. Visualization uses tools such as Matplotlib and Plotly. The input is the generated prediction information, and the output is the visualized information. Specific operations include the process of creating graphs and charts.

[0619] Step 6:

[0620] The terminal notifies the user of visualization information received from the server. The user reviews this notification and takes appropriate action as needed. The input is the visualization information displayed on the terminal, and the output is the user's decision. A concrete example is the user's action of starting evacuation preparations.

[0621] (Application Example 1)

[0622] 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".

[0623] While security is increasingly important in modern society, traditional security systems are costly and complex to operate, making them difficult for many individuals and businesses to implement. Furthermore, early detection of unauthorized intrusions and anomalies is challenging, highlighting the need for effective systems that enable rapid response. Therefore, the challenge lies in developing technologies that utilize animal behavior to provide cost-effective security systems and enable rapid response.

[0624] 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.

[0625] In this invention, the server includes means for collecting animal behavior information, means for analyzing abnormal behavior patterns, and means for generating predictive information using the abnormal behavior patterns. This makes it possible to quickly notify the user of predictive information indicating the possibility of unauthorized intrusion based on animal behavior as a security alarm, prompting appropriate action.

[0626] "Animal behavioral information" refers to information that includes biological signals, movement patterns, environmental changes, or signs of abnormal external stimuli in animals.

[0627] An "abnormal behavior pattern" is a behavioral pattern that serves as the basis for identifying unusual animal behavior and generating related predictive information.

[0628] "Predictive information" is information generated based on abnormal animal behavior that indicates the possibility of natural disasters or unauthorized intrusions.

[0629] "Means of notifying the user" refers to methods for communicating generated predictive information to the user via their device and prompting them to take necessary actions.

[0630] "Means of prompt response" refer to methods that help users take appropriate action quickly when they receive predictive information.

[0631] A "security alert" is information that warns users of the possibility of unauthorized intrusion or abnormal events based on abnormal behavior analyzed from animal behavior.

[0632] This invention is a system that provides security alerts based on animal behavior information. The system consists of a sensor device for collecting animal behavior information, a server for analyzing the data, and a terminal for notifying users of the information.

[0633] The sensor device uses a Raspberry Pi to collect animal behavior information in real time. The collected data is sent to a server via the network. The server functions as a web server using Flask and stores the received data in a database. OpenCV is used to analyze images of the animals, and machine learning algorithms using TensorFlow and Scikit-learn are used to identify abnormal behavior patterns.

[0634] When abnormal behavior is detected, the server uses Firebase Cloud Messaging to send the generated predictive information as a push notification to the user's smartphone. The user can check this notification on their device and take appropriate action if a quick response is required.

[0635] As a concrete example, imagine a dog kept as a pet in a rural area that detects the approach of a stranger. This system monitors the dog's unusual behavior, quickly generates an alarm, and sends a notification to the user's smartphone. The user receives the notification, checks the footage from nearby surveillance cameras, and can take measures against the intruder.

[0636] An example of a prompt to input into a generative AI model is, "Propose a system architecture for detecting anomalies from animal behavior data and providing rapid security alerts." By implementing a system based on this prompt, an efficient security system utilizing animal behavior can be realized.

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The server receives animal behavior information from sensor devices. The sensors capture the animal's movements and generate digital data via a Raspberry Pi. This data includes the animal's biosignals, movement patterns, and environmental changes. The input is analog data, and the output is digitized behavioral data.

[0640] Step 2:

[0641] The server stores the received data in a database. Using a database management system, the data is stored in a structured format. The input is digitized behavioral data, and the output is the data stored in the database.

[0642] Step 3:

[0643] The server uses OpenCV to analyze animal behavioral image data. The image data is preprocessed and filtered to identify animal movements. The input is unprocessed animal image data, and the output is filtered animal movement data.

[0644] Step 4:

[0645] The server analyzes abnormal behavior patterns using machine learning algorithms based on TensorFlow and Scikit-learn. The server applies a model to detect anomalies by comparing them with previously known data. The input is filtered animal movement data, and the output is the result of detecting abnormal behavior patterns.

[0646] Step 5:

[0647] The server sends notifications to the user's smartphone via Firebase Cloud Messaging. A security alarm is generated based on the detected abnormal behavior and sent as a push notification in real time. The input is the result of detecting abnormal behavior patterns, and the output is the alarm notification to the user.

[0648] Step 6:

[0649] The user receives a notification on their device and checks its contents. The application on the device displays the received alert as a pop-up notification, and the user takes additional action as needed, such as viewing camera footage. The input is the notification content, and the output is the user's confirmation and response action.

[0650] 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.

[0651] This invention is a system that simultaneously analyzes animal behavior information and user emotions to provide more accurate predictive information and optimize user responses. This system consists of sensors that acquire animal behavior information, a server that performs data analysis, a terminal that displays the information, and an emotion engine that recognizes user emotions.

[0652] The server collects data, including animal biosignals and movement patterns, and executes algorithms to process it effectively. The collected data is analyzed to detect patterns of abnormal behavior, and predictive information is generated based on the results. This predictive information includes forecasts of earthquake probability and market fluctuations.

[0653] The emotion engine estimates the user's emotions from their voice tone, facial expressions, and physical changes. The device sends this emotion data to a server, which adjusts the alert level of predictive information based on the user's emotional state. For example, if the system detects that the user is stressed, the alert content and display method can be made more intuitive to encourage quick and appropriate action. Furthermore, the emotion engine optimizes specific action suggestions based on the user's emotions, helping the user act with greater confidence.

[0654] For example, if a user who has received earthquake prediction information is detected to be in a state of emotional tension, the system will emphasize detailed evacuation procedures and emergency contact information. Similarly, in market fluctuation predictions for investment activities, the system will encourage calm decision-making by presenting reassuring information to alleviate anxiety.

[0655] In this way, the present invention integrates animal behavior information and user emotional information to provide more accurate and user-centric predictive information and countermeasures. This contributes to mitigating damage from natural disasters, improving the efficiency of investment activities, and enhancing user confidence.

[0656] The following describes the processing flow.

[0657] Step 1:

[0658] The server controls sensors to collect animal behavioral information, monitoring biosignals, movement patterns, and environmental changes. The collected data is transmitted to the server in real time and recorded in a database.

[0659] Step 2:

[0660] The server uses machine learning algorithms to analyze collected behavioral data and detect deviations from normal patterns. Once an abnormal behavioral pattern is identified, it is tagged with an anomaly tag.

[0661] Step 3:

[0662] The server generates predictive information based on detected abnormal behavior patterns. This predicted information concerns the likelihood of earthquakes and market fluctuations, and is prepared in an easy-to-understand format using visualization tools.

[0663] Step 4:

[0664] A device containing an emotion engine collects the user's voice tone and facial expression data to analyze their emotional state. This identifies the user's current emotional state, and the data is sent to a server.

[0665] Step 5:

[0666] The server receives user emotional state data and adjusts the alert priority of predictive information. If user stress or anxiety is detected, the alert is emphasized or adjusted accordingly.

[0667] Step 6:

[0668] The device receives pre-configured forecast information from the server and displays customized alerts and notifications to the user. These notifications are designed to allow the user to respond quickly.

[0669] Step 7:

[0670] Based on the provided forecast information and alerts, users can take specific actions such as earthquake evacuation or reviewing their investment policies. The system also includes emotionally-based suggestions to help users act with confidence.

[0671] (Example 2)

[0672] 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".

[0673] In modern society, predicting natural disasters and market fluctuations brings many benefits, but mere data analysis has the challenge of not being able to provide responses tailored to each individual's situation. Furthermore, conventional systems do not take into account user emotions and provide one-sided notifications, which can cause users to feel stressed or have their anxiety amplified. There is a need to solve these problems and realize the provision of more personalized and adaptive predictive information.

[0674] 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.

[0675] In this invention, the server includes a device for acquiring animal behavior data, an algorithm for analyzing the data and identifying abnormal behavior patterns, and a function for creating predictive information based on the identified patterns. This enables the integrated use of animal behavior and the user's emotional state to provide more accurate and personalized predictive information.

[0676] "Animal behavioral data" refers to a collection of information including animal biological signals, movement patterns, and changes in the surrounding environment, which is acquired through specific technological means.

[0677] A "device" is a piece of equipment consisting of hardware or software that has a specific function and is used to acquire or analyze data.

[0678] An "algorithm" is a sequence of computational procedures or rules used for data analysis, applied to identify abnormal behavioral patterns.

[0679] "Predictive information" refers to information generated based on animal behavior data and other relevant information that suggests future natural disasters, market fluctuations, and other events.

[0680] "User emotional state" refers to the emotional state inferred from the user's tone of voice, facial expressions, physical changes, etc.

[0681] An "engine" is a program or system that analyzes data and performs specific functions; in this context, it is used to recognize the user's emotional state.

[0682] A "terminal" is an electronic device used to display or notify a user of information, and includes personal computers and smartphones.

[0683] A "natural disaster" is an event that causes damage due to natural phenomena such as earthquakes and typhoons.

[0684] "Adaptation" refers to changing or adjusting a system or function in accordance with specific situations or conditions, and specifically means adjusting predictive information according to the user's emotional state.

[0685] This invention specifically provides a technology for generating predictive information using animal behavior data and adaptively notifying users of this information.

[0686] Data acquisition and hardware configuration

[0687] The server receives behavioral data from multiple sensors attached to the animals. This data includes biological signals and movement patterns, and the sensors transmit the information to the server via Bluetooth or Wi-Fi.

[0688] Data analysis and software configuration

[0689] The server analyzes collected behavioral data in real time using a dedicated algorithm. This makes it possible to identify abnormal behavior patterns using machine learning techniques. The analyzed results are automatically generated as predictive information such as earthquakes and market fluctuations.

[0690] Recognition and adaptation of user emotions

[0691] The emotion engine collects the user's voice tone, facial expressions, and physical changes through the camera and microphone, and analyzes this data to determine the user's emotional state. The device sends this emotional data to a server, allowing predictive notifications to be adjusted according to the user's emotions. If the user shows high levels of stress, the notification content is changed to be more intuitive and easier to understand to alleviate anxiety.

[0692] Examples of specific cases and prompt statements

[0693] For example, if the device detects that a user is in a state of tension after receiving earthquake prediction information, it will highlight detailed evacuation procedures and provide voice instructions. Furthermore, when providing information on market trends, it will present reassuring elements to encourage calm decision-making by the user.

[0694] Examples of prompt messages include, "Please tell me how to predict the likelihood of an earthquake based on animal behavior data and adjust the alert content according to the user's emotional state."

[0695] This system aims to help users maximize their profits by predicting natural disasters and market fluctuations, and to provide adaptive information tailored to each individual user.

[0696] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0697] Step 1:

[0698] The server receives behavioral data, such as biological signals and movement patterns, from sensors attached to animals. This data is typically transmitted via Bluetooth or Wi-Fi. Inputs include biological signal data and movement patterns, and the output is filtered, clean data. Specifically, the server performs initial filtering of the received data to remove noise.

[0699] Step 2:

[0700] The server runs a dedicated machine learning algorithm using filtered animal behavior data. The input includes filtered, clean data, and the output is the identification of abnormal behavior patterns. Specifically, the server retrains itself by comparing it with historical data to detect anomalies.

[0701] Step 3:

[0702] The server generates predictive information based on identified anomaly patterns. The input is anomaly behavior patterns, and the output is predictive information such as earthquakes and market fluctuations. Specifically, the server utilizes a generation AI model, supplementing its predictions by referencing historical data and external information.

[0703] Step 4:

[0704] The emotion engine acquires the user's voice tone, facial expressions, and physical changes from the camera and microphone, and analyzes their emotional state. Input includes the user's voice and video data, and output is the user's emotional state. Specifically, the device sends the processed emotional data to the server in real time.

[0705] Step 5:

[0706] The server adjusts the content of predictive notifications based on the user's emotional state. The input includes the user's emotional state, and the output is a notification adapted to that emotion. Specifically, the server modifies the notification content based on the emotional state, for example, making warnings more approachable.

[0707] Step 6:

[0708] The device provides the user with adjusted predictive information. The input is the adjusted notification information, and the output is the completion of notification to the user. Specifically, the device displays alerts to the user using voice and visuals, and utilizes vibration and voice guidance in emergencies.

[0709] (Application Example 2)

[0710] 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".

[0711] In physical stores, there is a challenge in responding flexibly to customers' emotional states. In particular, there is a challenge in how to help customers relax and feel satisfied when they are stressed. Traditional systems make it difficult to individually optimize the customer experience, and there is a need for effective means to improve customer satisfaction.

[0712] 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.

[0713] In this invention, the server includes means for collecting animal behavior information, means for analyzing the user's emotional state, and means for adjusting predictive information notifications based on the user's emotional state. This makes it possible to provide a relaxing experience tailored to the customer's emotional state in a physical store.

[0714] "Animal behavioral information" refers to data obtained from animals, such as biosignals, movement patterns, and information about environmental changes.

[0715] An "abnormal behavioral pattern" is a pattern of unusual behavior detected by analyzing animal behavioral information.

[0716] "Predictive information" refers to information about future events generated based on abnormal behavioral patterns.

[0717] A "user" is someone who uses the system or someone who receives notifications.

[0718] "Emotional state" refers to the psychological condition judged based on the user's voice, facial expressions, physical changes, etc.

[0719] "Adjustment" refers to changing the way predictive information is notified and the content of that information based on the user's emotional state.

[0720] A "system" is a device or mechanism that integrates multiple means to achieve a specific function.

[0721] The system that implements this application is designed to optimize the customer experience in physical stores by collecting animal behavioral information and analyzing the emotional state of users.

[0722] The server uses sensor devices to collect animal behavioral information. These sensors acquire the animals' biosignals and movement patterns, and the server performs data analysis. This analysis detects abnormal behavioral patterns and generates predictive information. The generated predictive information is adjusted based on the emotional state of customers in the physical store and provided as visual or auditory feedback within the store.

[0723] On the user's side, an emotion engine operates through devices such as smartphones and tablets. This emotion engine analyzes the user's voice tone and facial expressions to infer their emotional state. If the system determines that the user is feeling tense, it controls the in-store displays and speakers to provide relaxing music and videos.

[0724] As a concrete example, if a customer visiting a pet shop is feeling stressed, the system will play soothing music and guide the user to an area where they can interact with small animals. The hardware used includes sensor devices, smart terminals, display devices, and an acoustic system, while the software utilizes OpenCV and TensorFlow as emotion recognition algorithms.

[0725] For this system to be effective, accurate sentiment analysis and the generation and notification of appropriate predictive information are necessary. An example of a prompt is, "Based on customer sentiment data and the behavior of pets in the store, how should we display data that promotes relaxation?" The generated AI model is required to suggest an appropriate store experience based on this prompt.

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The server collects animal behavioral information from sensor devices. Inputs include animal biosignals and movement patterns, which are output as a dataset organized over time. This data is then converted to an appropriate format for subsequent analysis.

[0729] Step 2:

[0730] The server analyzes the collected animal behavior data to detect abnormal behavior patterns. The input is the behavior dataset obtained in step 1, and the analysis algorithm identifies the patterns. The detected abnormal behavior patterns become the output sent to the next processing step.

[0731] Step 3:

[0732] The server generates predictive information based on detected abnormal behavior patterns. The input is abnormal behavior patterns, and a predictive algorithm is used to generate information about possible future events. The generated predictive information is used for interactions in physical stores.

[0733] Step 4:

[0734] The device analyzes the customer's voice tone and facial expressions using an emotion engine to estimate the user's emotional state. The input consists of the customer's voice and video data, which is processed by an emotion recognition algorithm (such as OpenCV or TensorFlow), and the estimated emotional state is output.

[0735] Step 5:

[0736] The server integrates the user's emotional state and generated predictive information to tailor in-store notifications. The input consists of the emotional state and predictive information, which determine the content to display or play. The output is user-optimized feedback.

[0737] Step 6:

[0738] The terminal delivers tailored notifications to customers through in-store displays and speakers. Input is feedback information from the server, which is output as concrete visuals or audio. This provides customers with a relaxing experience.

[0739] This processing flow improves the customer experience in physical stores, particularly enabling appropriate responses to stress.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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."

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] The following is further disclosed regarding the embodiments described above.

[0762] (Claim 1)

[0763] Means for collecting animal behavioral information,

[0764] Means for analyzing the aforementioned behavioral information to detect abnormal behavioral patterns,

[0765] A means for generating predictive information using detected abnormal behavior patterns,

[0766] A means of notifying the user of the generated prediction information,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, wherein the behavioral information of the animal includes biological signals, movement patterns, or environmental changes.

[0770] (Claim 3)

[0771] The system according to claim 1, wherein the aforementioned prediction information indicates the possibility of an earthquake.

[0772] "Example 1"

[0773] (Claim 1)

[0774] Means including a device for collecting animal behavior information,

[0775] A means for detecting abnormal behavioral patterns by analyzing the aforementioned behavioral information using a machine learning algorithm,

[0776] A means for generating predictive information using detected abnormal behavior patterns and visualizing that information,

[0777] A means of notifying the user of the generated prediction information via a terminal,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, wherein the behavioral information of the animal includes information such as biological signals, movement patterns, or environmental changes.

[0781] (Claim 3)

[0782] The system according to claim 1, which includes information that indicates the likelihood of natural disasters or market fluctuations, and that supports rapid decision-making based thereon.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] Means for collecting animal behavioral information,

[0786] Means for analyzing the aforementioned behavioral information to detect abnormal behavioral patterns,

[0787] A means for generating predictive information using detected abnormal behavior patterns,

[0788] A means of notifying the user of the generated prediction information,

[0789] A means to prompt a user who has received the aforementioned prediction information to take a prompt action,

[0790] A means for transmitting abnormalities derived from the behavior of the aforementioned animals as a security alarm,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the behavioral information of the animal includes biological signals, movement patterns, environmental changes, or signs of abnormal external stimuli.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the predictive information indicates the possibility of unauthorized intrusion.

[0796] "Example 2 of combining an emotion engine"

[0797] (Claim 1)

[0798] A device for acquiring animal behavioral data,

[0799] An algorithm that analyzes the aforementioned data to identify abnormal operation patterns,

[0800] A function to create predictive information based on the identified pattern,

[0801] An engine that recognizes the user's emotional state,

[0802] A function to adjust the notification level of predictive information according to the aforementioned emotional state,

[0803] A terminal that provides the adjusted information to the user,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, wherein the animal behavior data includes biological signals, movement patterns, or changes in the surrounding environment.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the predictive information indicates the possibility of a natural disaster and is adapted based on the user's emotional state.

[0809] "Application example 2 when combining with an emotional engine"

[0810] (Claim 1)

[0811] Means for collecting animal behavioral information,

[0812] Means for analyzing the aforementioned behavioral information to detect abnormal behavioral patterns,

[0813] A means for generating predictive information using detected abnormal behavior patterns,

[0814] A means of notifying the user of the generated prediction information,

[0815] A means of analyzing the emotional state of users,

[0816] Means for adjusting the predictive information notification based on the user's emotional state,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the behavioral information of the animal includes biological signals, movement patterns, or environmental changes.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the aforementioned prediction information indicates the possibility of crustal deformation. [Explanation of Symbols]

[0822] 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. Means for collecting animal behavioral information, Means for analyzing the aforementioned behavioral information to detect abnormal behavioral patterns, A means for generating predictive information using detected abnormal behavior patterns, A means of notifying the user of the generated prediction information, A system that includes this.

2. The system according to claim 1, wherein the behavioral information of the animal includes biological signals, movement patterns, or environmental changes.

3. The system according to claim 1, wherein the aforementioned prediction information indicates the possibility of an earthquake.

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